Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
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 .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
[] |
2015
|
Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer
|
Approximately 14 million persons living in areas endemic for lymphatic filariasis have lymphedema of the leg . Clinical studies indicate that repeated episodes of bacterial acute dermatolymphangioadenitis ( ADLA ) lead to progression of lymphedema and that basic lymphedema management , which emphasizes hygiene , skin care , exercise , and leg elevation , can reduce ADLA frequency . However , few studies have prospectively evaluated the effectiveness of basic lymphedema management or assessed the role of compressive bandaging for lymphedema in resource-poor settings . Between 1995 and 1998 , we prospectively monitored ADLA incidence and leg volume in 175 persons with lymphedema of the leg who enrolled in a lymphedema clinic in Leogane , Haiti , an area endemic for Wuchereria bancrofti . During the first phase of the study , when a major focus of the program was to reduce leg volume using compression bandages , ADLA incidence was 1 . 56 episodes per person-year . After March 1997 , when hygiene and skin care were systematically emphasized and bandaging discouraged , ADLA incidence decreased to 0 . 48 episodes per person-year ( P<0 . 0001 ) . ADLA incidence was significantly associated with leg volume , stage of lymphedema , illiteracy , and use of compression bandages . Leg volume decreased in 78% of patients; over the entire study period , this reduction was statistically significant only for legs with stage 2 lymphedema ( P = 0 . 01 ) . Basic lymphedema management , which emphasized hygiene and self-care , was associated with a 69% reduction in ADLA incidence . Use of compression bandages in this setting was associated with an increased risk of ADLA . Basic lymphedema management is feasible and effective in resource-limited areas that are endemic for lymphatic filariasis .
Lymphedema of the leg and its advanced form , known as elephantiasis , are major causes of disability and morbidity in filariasis-endemic areas , with an estimated 14 million cases worldwide [1] . When the World Health Organization's Global Program to Eliminate Lymphatic Filariasis ( GPELF ) was launched in 1998 , its stated goals included not only interrupting transmission of the parasite , but also providing care to persons who suffer from clinical disease [2] , [3] . During the 1990s , several studies in filariasis-endemic areas highlighted the importance of repeated episodes of acute bacterial dermatolymphangioadenitis ( ADLA ) in the progression of lymphedema severity [4]–[8] . These inflammatory episodes , characterized by intense pain , swelling , fever , and chills , accelerate damage to the peripheral lymphatic channels in the skin , which leads to worsened lymphatic dysfunction , fibrosis , and increased risk of further ADLA episodes . Clinical studies suggest that basic lymphedema management – including hygiene , skin care , elevation of the limb , and range-of-motion exercises – can halt , or perhaps even partially reverse , this progression [9]–[14] . The current prospective study was done to test , under field conditions , the feasibility and effectiveness of basic lymphedema management as a public health intervention in a resource-poor setting . Leogane , Haiti , located approximately 30 km west of Port au Prince , has long been endemic for lymphatic filariasis; the prevalence of Wuchereria bancrofti microfilaremia was 16% in 2000 [15] . The outpatient clinic at Ste . Croix Hospital , the major health facility for Leogane Commune , was the site of this study . Lymphedema of the leg , which disproportionately affects women [16] , is a major public health problem in Leogane [17] . As in many other areas where bancroftian filariasis is endemic , few persons with lymphedema of the leg remain infected with the parasite [18] .
In June 1995 , the physical therapist at Ste . Croix ( JLC ) recruited a group of 30 patients with lymphedema of the leg to participate in a pilot project of basic lymphedema management . The goals of this pilot were to assess the feasibility and acceptance of basic lymphedema management in a small group of patients and to test the feasibility of volumetric measurement in this setting . During this pilot phase , leg volume sometimes was measured twice a day to assess diurnal variation ( data not shown ) . The pilot was intended to last two months , but it continued with these 30 patients until the spring of 1996 , when increased funding allowed the project to scale up . For the purposes of analysis , the pilot study is considered as part of Phase I . With the project expansion in March 1996 , eight additional staff , including a nurse , a community health worker , and others with high school-level education but no health background were trained as lymphedema technicians . A hospital physician ( FL ) provided consultation and medical care as needed . During Phase I , an attempt was made to compare the effectiveness of basic lymphedema management with a more intensive method that included compressive bandaging , a component of “complex decongestive physiotherapy” [20] . Two to four weeks of compressive bandaging ( using Comprilan® ) resulted in rapid volume reduction , which was maintained with a locally designed and produced compressive garment made of Velcro® . However , because it resulted in rapid volume reduction , the popularity of compressive bandaging made it impossible to randomize patients to the non-bandage intervention group . In March 1997 , Dr . Gerusa Dreyer , who had developed a basic lymphedema management program in Recife , Brazil , visited Leogane to review the project and to conduct training for the staff . As a result of that visit , the program shifted its primary focus from reducing leg volume to preventing ADLA through hygiene and skin care . Use of compressive bandaging and garments was no longer encouraged and their use declined dramatically . In November 1997 , a colorful booklet with the messages of basic lymphedema management was given to each patient , and a “soap opera” was broadcast over local radio that depicted the life of a young woman with lymphedema and how she benefited from lymphedema self-care . The analysis was limited to patients who returned to the clinic for at least 5 routinely scheduled visits over a period of least 6 months . We considered shorter periods of observation inadequate to evaluate the effect of the intervention . ADLA incidence was calculated as episodes per person-year of observation . Changes in leg volume were calculated by subtracting last-measured volume from initial volume during a specified time period . The pooled t-test was used to compare differences in age by study phase; the paired t-test to compare changes in leg volumes; the chi-square or Fisher's Exact test to compare proportions associated with gender and literacy; and Poisson regression to calculate and compare ADLA incidence rates and to identify factors associated with ADLA episodes . The effect of factors associated with leg volume was investigated using multivariate regression . All regressions were run using SAS proc genmod , using the generalized estimating equations ( GEE ) procedure to adjust for correlation of multiple observations ( e . g . , ADLA episodes in different legs ) from the same individual over time [21] . Analyses were performed using SAS version 9 . 2 , SAS Institute Inc , Cary , NC , USA . Statistical significance was set at alpha = 0 . 05 .
During the 12 months before entering the program , the 175 patients reported a mean of 2 . 1 episodes of ADLA ( range 0–13 ) , with a mean duration of 2 . 6 days each ( range <1 day–44 days ) . Reported ADLA incidence by 12-month recall was not associated with patient age or sex but was significantly associated with illiteracy ( RR 1 . 6 , P = 0 . 01 ) , bilateral lymphedema ( RR 1 . 6 , P = 0 . 02 ) and greater lymphedema stage ( 0 . 67 , 1 . 72 , 2 . 32 , and 3 . 08 episodes per year for persons with stages 1 through 4 , respectively , P = 0 . 01 ) . During the study , a total of 242 ADLA episodes were reported , for an overall incidence of 0 . 75 episodes per person-year , with a range of 0 to 10 per patient . Of these episodes , 141 ( 58% ) were witnessed by clinic staff . Mean reported duration of each ADLA episode was 3 . 9 days ( range 1–22 ) . Eighty-seven patients ( 49 . 7% ) had no ADLA during the study , 67 ( 38 . 3% ) had an annual incidence of <2 . 0 per person-year , and 21 ( 12 . 0% ) experienced two or more episodes per year . In univariate analysis , ADLA incidence during the study was positively associated with lymphedema stage and leg volume , illiteracy , use of compression bandages or garments , and reported frequency of ADLA before entering the study ( Table 2 ) . No consistent seasonal pattern was observed for ADLA incidence , although it tended to increase between the third and fourth quarters of the year; this increase was statistically significant ( P = 0 . 006 ) only for 1997 . Because the emphasis of the program shifted from leg volume reduction during Phase I to ADLA prevention through self-care during Phase II , we compared ADLA incidence for these two periods ( Figure 1 ) . In Phase I , the incidence of ADLA was 1 . 56 episodes per person-year , compared to 0 . 48 overall in Phase II ( P<0 . 0001 ) . A significant reduction was observed even when the analysis was restricted to those who entered the study during Phase I . Among these 127 persons , ADLA incidence decreased to 0 . 54 per person-year during Phase II ( P<0 . 0001 ) . ADLA incidence during Phase II was even lower for the 48 persons who entered the study during Phase II , 0 . 22 episodes per person-year ( P<0 . 0001 ) ( Table 3 ) . A multivariate Poisson regression analysis considered the effect of treatment compliance , illiteracy , gender , and use of compression bandages on ADLA incidence among the 127 persons who entered the study during Phase I , when compressive bandaging use was most common . Only illiteracy and use of compression bandages or garments were significantly associated with ADLA incidence . A significant ( P = 0 . 02 ) interactive effect of bandaging and illiteracy was observed . Among illiterate persons , mean ADLA incidence did not change with or without use of compression bandages ( 1 . 1 episodes per year vs . 1 . 0 episode per year , respectively ) . However , among literate persons , those who ever used compression bandages had an overall ADLA incidence of 0 . 76 per year , compared to 0 . 20 per year for those who never used compression bandages or garments ( P = 0 . 005 ) . Of 175 patients , 137 ( 78 . 3% ) experienced some reduction in leg volume during the study; volume decreased in 66 . 4% of lymphedematous legs ( median reduction , 90 ml ) . In general , volume decreased dramatically following application of compressive bandages and increased rapidly during ADLA . Compared to leg volume on entering the study , mean leg volume at the end of the study was 14 mL greater in normal legs and 5 mL greater in legs with stage 1 lymphedema . Mean decreases of 59 mL , 93 mL , and 571 mL were observed for lymphedema stages 2 , 3 , and 4 , respectively , statistically significant over the entire study period only for stage 2 ( P = 0 . 01 ) ( Table 4 ) . Controlling for study phase , volume reduction in all 175 patients was significantly associated with increasing lymphedema stage ( P = 0 . 005 ) , lower frequency of ADLA during the study period ( P = 0 . 016 ) , and use of compressive bandages ( a mean decrease of 86 . 6 mL , compared to 8 . 2 mL in lymphedematous legs in which no compression was used , P = 0 . 039 ) , but not with age or sex . For the purposes of data analysis , compliance with the four recommended practices of leg washing , exercises , leg elevation , and sleeping with the foot of the bed raised was defined as having reported , on at least 75% of all clinic visits , that the behaviors had been practiced every day since the previous clinic visit . Using this definition , compliance rates were 88 . 0% , 38 . 3% , 69 . 7% , and 49 . 7% for these four practices , respectively . Significant increases in daily compliance between Phase I and Phase II were observed for leg washing ( from 61 . 7% to 94 . 3% , P<0 . 001 ) , leg elevation ( 59 . 2% to 68 . 6% , P = 0 . 01 ) , and sleeping with the foot of the bed elevated ( 40 . 8% to 51 . 4% , P<0 . 001 ) . Overall compliance was calculated by summing the percentage figures for all four recommended practices; a score of ≥300 ( of a possible 400 ) was considered compliant . Overall compliance increased from 45 . 8% to 66 . 3% between Phase I and Phase II ( P = 0 . 003 ) . In a multivariate Poisson regression analysis , compliance was significantly associated with female gender ( OR 1 . 6 , 95% Confidence Interval [CI] 1 . 0–2 . 5 , P = 0 . 05 ) and age >40 years ( OR 1 . 5 , 95% CI 1 . 1–2 . 0 , P = 0 . 02 ) , but not with literacy or lymphedema stage .
This prospective study provides evidence that basic lymphedema management is feasible and acceptable to patients in resource-poor areas where lymphatic filariasis is endemic . In general , patients were able to incorporate basic self-care measures , especially leg washing , into their daily routines . When proper hygiene and skin care were emphasized , the incidence of ADLA decreased rapidly to 31% of earlier levels and these reductions were sustained over time . A follow-up study by Dahl and colleagues in 2000–2001 showed that ADLA incidence in these patients remained low and even decreased further ( Dahl , BA , unpublished thesis , Emory University , Atlanta , Georgia ) . Recent studies indicate that , in addition to being a critical risk factor for progression of lymphedema [4]–[8] , [13] , [14] , ADLA episodes are strongly associated with poor quality of life [10] , [22]–[28] . Thus , reducing ADLA frequency is arguably the most important objective of lymphedema management in resource-poor countries . Accordingly , ADLA has emerged as a key indicator for monitoring lymphedema management programs in filariasis-endemic areas [29] . In contrast , the usefulness of leg volume as an indicator of clinical improvement in filariasis-endemic areas seems limited . Leg volume , albeit an “objective” measure , varied widely with use of compression garments and , to an extent , with time of day ( data not shown ) . Overall , leg volume decreased in 78% of our patients , but these reductions were generally modest , and statistically significant only for stage 2 lymphedema . Our initial focus on reducing leg volume , although popular with patients , distracted attention from hygiene and skin care; as a result , no significant reduction in ADLA incidence was observed during Phase I of the study . Compression bandages reduced leg volume quickly , but their use was associated with an increase in ADLA incidence . It was difficult to keep the compression bandages clean , and they were prohibitively expensive – equivalent to the annual income of many patients . Frequent visits to the clinic for reapplication of compression bandages in the initial stages of treatment also fostered dependence on clinic staff and undermined key messages of self-reliance and self-care . Thus , although reduction in leg volume is a desirable outcome , our experience suggests that it should not be the primary goal of lymphedema treatment in resource-poor settings . After receiving training from Dr . Dreyer midway through the study , the clinic staff was better equipped to deliver simple , clear , assertive messages to patients regarding hygiene and skin care . These messages were later reinforced by a simple booklet that was given to each patient , opportunities to participate in support groups , and a radio drama about a young woman with lymphedema that was aired repeatedly on local radio . With these interventions , ADLA incidence dropped dramatically and remained low . The relative contribution of each component of the overall intervention is unclear; we believe that they acted synergistically . Coreil and colleagues showed that patients in Leogane who regularly participated in support groups had a lower incidence of ADLA than those who did not [30] . In addition , self-efficacy ( belief in one's ability to perform lymphedema self-care ) increased significantly following distribution of booklets and broadcasts of the radio dramas ( Wendt , JM , unpublished thesis , Emory University , Atlanta , GA ) . The number and duration of patient contacts required for patients to understand , become proficient in , and fully committed to life-long lymphedema self-care is not clear . In practice , the intensity of patient training and follow-up varies among filariasis programs [29] . Our results indicate that patients who were illiterate and those with advanced disease continued to be at risk of ADLA episodes . These results suggest that more intensive effort may be needed for such patients , such as home visits and support groups . The fact that illiterate persons remained at a 2-fold risk of ADLA throughout the study , even when controlling for other factors , also suggests that illiteracy may be a marker for other factors related to ADLA incidence , such as lower self-efficacy or increased risk of skin lesions that predispose to ADLA [31] , [32] . In addition to reduced ADLA incidence , we observed other improvements in quality of life among patients in this study . These were often dramatic . Patients commonly reported decreased stigma and shame and a return to active life , including school or work [30 , Wendt JM , unpublished] . Those with advanced lymphedema and multiple skin folds and lesions reported , often for the first time in years , the elimination of offensive odor . Serial biopsies in a subset of patients confirmed a decrease in chronic inflammation in lymphedematous legs [33] . Despite these remarkable changes at the tissue level , most patients did not experience reduction in lymphedema stage . The 4-stage classification that we used is not particularly sensitive to localized changes , and considerable heterogeneity can be found within each stage . Most of the lymphedema classification and staging systems that have been proposed suffer from these limitations [34] . Studies have reported that the incidence of adenolymphangitis ( presumably ADLA or ADLA-like ) may decrease following mass treatment with antifilarial drugs [35]–[38] . The current study was completed almost two years before mass treatment was initiated in Leogane [15]; diethylcarbamazine was not readily available . Therefore , it seems unlikely that the clinical improvement and decreased ADLA frequency that we observed can be attributed to reduced transmission of W . bancrofti . Seasonal fluctuations in ADLA , associated with rainfall , have been reported in other studies [39] . We observed no consistent seasonal trends in ADLA incidence . The tendency for ADLA incidence to increase between the third and fourth quarters was statistically significant only for 1997 . The reason for this is not clear . Rainfall patterns varied from year to year in Haiti during the study period , and the fall of 1997 was relatively dry . Prospective ADLA monitoring , as was done in this study , is accurate , but expensive . The accuracy of patient recall over longer periods is unknown , but the pain and suffering associated with ADLA make it a memorable event . Among the 127 patients who entered the study during Phase I , the observed incidence of ADLA during Phase I was 1 . 6 episodes per person-year , compared to 2 . 4 self-reported for the 12 months before entering the study . It is risky to compare retrospective and prospective data , and it also is possible that messages regarding hygiene and skin care during Phase I , albeit sub-optimal , might have reduced ADLA incidence from baseline levels . However , the data suggest that 12-month recall may be adequate for rapid epidemiologic assessments or program monitoring . Additional research is warranted to assess reliability of 12-month ADLA recall in different filariasis-endemic areas . The preponderance of women in our study sample reflects the gender distribution among persons with lymphedema in the Leogane population [16] . In many areas where bancroftian filariasis is endemic , lymphedema of the leg is more common in women than in men , although this finding is not universal [38] . This study has several limitations . First , both the incidence of ADLA and compliance with lymphedema self-care were assessed by self-report – a method that , typically , is less than completely reliable . Recall of ADLA episodes during the year before entering the study was facilitated by careful questioning and using memorable public events to help “frame” the previous 12 months . During the study , patients were encouraged to report ADLA and compliance accurately and honestly , and they were assured that clinical care would continue , free of charge , regardless of what they reported . Some 58% of ADLA episodes reported by patients were observed and confirmed by research staff at the clinic . In many of the remaining cases , patients were seen within 1–2 weeks after the episode , when clinical signs ( such as peeling of the skin , warmth , or edema [19] ) were still visible . Skin condition and interdigital lesions were carefully assessed during routine clinic visits , and they provided a check on self-reported compliance . Ad-hoc home visits also encouraged accurate self-reporting . For example , the absence of soap , a clean towel , or an appropriate washing basin would call into question high levels of self-reported compliance . Second , because of ethical concerns about withholding treatment , we did not collect comparative prospective data on a control group of patients . Further , although we desired to randomize patients with regard to compressive bandaging , we were unable to do so . However , we were able to compare ADLA incidence as a function of the focus of our programmatic intervention ( i . e . , leg volume during Phase I and ADLA incidence during Phase II ) . Third , because of the conditions under which this study was conducted , it cannot be regarded as a study of the efficacy of lymphedema management . We had limited on-site access to specialists in lymphedema management , our staff was comprised mostly of motivated young people rather than health professionals , and the intervention evolved as we gained experience . It is likely that even greater reductions in ADLA frequency would be observed under more optimal conditions . Despite these limitations , our results suggest that basic lymphedema management is both feasible and effective in filariasis-endemic areas where resources are limited . When provided with the skills and motivation to practice lymphedema self-care , most patients will do so . However , simple messages that unequivocally emphasize the importance of hygiene and skin care are important . Our experience also suggests that compressive bandages , although useful for individual patients , should not be the mainstay of treatment in filariasis-endemic areas , and that volume reduction may not be the optimal measure of success .
|
Lymphatic filariasis is a parasitic disease that is spread by mosquitoes . In tropical countries where lymphatic filariasis occurs , approximately 14 million people suffer from chronic swelling of the leg , known as lymphedema . Repeated episodes of bacterial skin infection ( acute attacks ) cause lymphedema to progress to its disfiguring form , elephantiasis . To help achieve the goal of eliminating lymphatic filariasis globally , the World Health Organization recommends basic lymphedema management , which emphasizes hygiene , skin care , exercise , and leg elevation . Its effectiveness in reducing acute attack frequency , as well as the role of compressive bandaging , have not been adequately evaluated in filariasis-endemic areas . Between 1995 and 1998 , we studied 175 people with lymphedema of the leg in Leogane , Haiti . During Phase I of the study , when compression bandaging was used to reduce leg volume , the average acute attack rate was 1 . 56 episodes per year; it was greater in people who were illiterate and those who used compression bandages . After March 1997 , when hygiene and skin care were emphasized and bandaging discouraged , acute attack frequency significantly decreased to 0 . 48 episodes per year . This study highlights the effectiveness of hygiene and skin care , as well as limitations of compressive bandaging , in managing lymphedema in filariasis-endemic areas .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology/health",
"policy",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/preventive",
"medicine",
"infectious",
"diseases/skin",
"infections",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases"
] |
2010
|
Feasibility and Effectiveness of Basic Lymphedema Management in Leogane, Haiti, an Area Endemic for Bancroftian Filariasis
|
Dengue virus ( DENV ) is the leading cause of arboviral diseases in humans worldwide . The envelope ( E ) protein of DENV is the major target of neutralizing antibodies ( Abs ) . Previous studies have shown that a significant proportion of anti-E Abs in human serum after DENV infection recognize the highly conserved fusion loop ( FL ) of E protein . The role of anti-FL Abs in protection against subsequent DENV infection versus pathogenesis remains unclear . A human anti-E monoclonal Ab was used as a standard in a virion-capture ELISA to measure the concentration of anti-E Abs , [anti-E Abs] , in dengue-immune sera from Nicaraguan patients collected 3 , 6 , 12 and 18 months post-infection . The proportion of anti-FL Abs was determined by capture ELISA using virus-like particles containing mutations in FL , and the concentration of anti-FL Abs , [anti-FL Abs] , was calculated . Neutralization titers ( NT50 ) were determined using a previously described flow cytometry-based assay . Analysis of sequential samples from 10 dengue patients revealed [anti-E Abs] and [anti-FL Abs] were higher in secondary than in primary DENV infections . While [anti-FL Abs] did not correlate with NT50 against the current infecting serotype , it correlated with NT50 against the serotypes to which patients had likely not yet been exposed ( “non-exposed” serotypes ) in 14 secondary DENV3 and 15 secondary DENV2 cases . These findings demonstrate the kinetics of anti-FL Abs and provide evidence that anti-FL Abs play a protective role against “non-exposed” serotypes after secondary DENV infection .
The four serotypes of dengue virus ( DENV1–4 ) are the leading cause of arboviral diseases in humans in tropical and subtropical regions [1] , [2] . It has been estimated that more than 3 billion people in over 100 countries are at risk of infection and 50–100 million DENV infections occur annually worldwide [1] , [2] . The clinical presentation after DENV infection ranges from asymptomatic infection to a self-limited illness , dengue fever ( DF ) , to severe and potentially life-threatening disease , dengue hemorrhagic fever/dengue shock syndrome ( DHF/DSS ) [1] , [2] . While considerable efforts have been made to develop interventions , currently there is no licensed vaccine or antiviral therapeutic against dengue available [3] . DENV belongs to the genus Flavivirus in the family Flaviviridae . It contains a positive-sense RNA genome of approximately 11 kilobases in length . Flanked by the 5′ and 3′ untranslated regions , the genome has a single open reading frame encoding a polyprotein precursor , which is cleaved by cellular and viral proteases into three structural proteins , the capsid , precursor membrane ( prM ) and envelope ( E ) , and seven non-structural proteins [4] . The E protein forms 90 “head-to-tail” homodimers on the surface of mature virions [4]–[6] . The E protein participates in virus entry and is the major target of neutralizing antibodies ( Abs ) [3] , [4] . In the presence of non-neutralizing or suboptimal concentrations of neutralizing anti-E Abs , DENV replicates to higher titers in human Fcγ receptor-bearing cells in vitro , a phenomenon known as antibody-dependent enhancement ( ADE ) [7]–[9] . The ectodomain of E protein contains three domains [10] . Domain II contains an internal fusion loop ( FL ) that is involved in membrane fusion . Domain III is believed to participate in receptor binding [4] , [10] , [11] . In the genus Flavivirus , there are several serocomplexes , including DENV , Japanese-encephalitis virus , and tick-borne encephalitis virus serocomplexes . Abs that recognize members from different serocomplexes , members within a serocomplex , or a single member are called flavivirus group-reactive , complex-reactive , or type-specific , respectively [12] . Previous studies of polyclonal human sera revealed that a significant proportion of anti-E Abs after DENV infection was group-reactive and recognized the FL of domain II , whereas only a minor proportion was type-specific and recognized E domain III [13]–[16] . The change in the amount of anti-FL Abs over time and the role of anti-FL Abs in dengue protection versus pathogenesis remain unclear . Following primary DENV infection , individuals develop monotypic neutralizing Abs against the infecting serotype [9] . A recent study on depletion of human sera by recombinant E protein has shown that cross-reactive Abs ( including anti-FL Abs ) do not contribute substantially to monotypic neutralization against the infecting serotype after primary DENV infection [17] . After secondary DENV infection , individuals develop not only neutralizing Abs against serotypes to which they have been previously exposed but also cross-reactive neutralizing Abs against serotypes to which they have not yet been exposed ( “non-exposed” serotypes ) [9] . The nature of such heterotypic neutralizing Abs remains unknown . We hypothesize that the cross-reactive anti-FL Abs may play a role in protection against the non-exposed serotypes after secondary infection . In this study , we developed a DENV virion-capture ELISA to measure the concentrations of anti-E Abs , [anti-E Abs] , against DENV in sera , determined the proportion of anti-FL Abs ( % anti-FL Abs ) by a previously-described capture ELISA using virus-like particles ( VLPs ) [14] , [16] , and calculated the concentrations of anti-FL Abs , [anti-FL Abs] . We examined the changes of [anti-E Abs] and [anti-FL Abs] over time in sequential serum samples from 10 cases of primary or secondary DENV infection and then measured the [anti-FL Abs] in 26 additional secondary DENV infections . While [anti-FL Abs] did not correlate with NT50 against the current infecting serotype , it correlated with neutralization titers against likely “non-exposed” serotypes in 29 secondary infections . These findings support our hypothesis that anti-FL Abs may play a protective role against the “non-exposed” serotypes after secondary DENV infection .
Thirty-six laboratory-confirmed dengue patients , who were admitted to the Hospital Infantil Manuel de Jesús Rivera in Managua , Nicaragua between October 2006 and October 2008 and followed up for 18 months , were selected arbitrarily for the analysis . The study was approved by the Institutional Review Boards of the University of California , Berkeley , and the Nicaraguan Ministry of Health . Parents or legal guardians of all subjects provided written informed consent , and subjects 6 years of age and older provided assent . DENV infection was confirmed by detection of viral RNA by RT-PCR directed to the capsid region [18] , [19]; virus isolation in C6/36 cells [20]; IgM seroconversion between acute and convalescent-phase samples; and/or a ≥4-fold increase in total anti-DENV Abs between acute and convalescent-phase samples as measured by Inhibition ELISA [19] , [21] . Primary DENV infection was defined by an Ab titer by Inhibition ELISA [21] of <10 in acute samples or <2 , 560 in convalescent ( day 14 ) samples , while secondary infection was defined by an Ab titer by Inhibition ELISA of ≥10 in acute samples or ≥2 , 560 in convalescent samples [22] . Disease severity was classified according to the 1997 World Health Organization Guidelines [23] . The plasmids expressing prM/E proteins of DENV1 ( Hawaii strain , pCB-D1 ) and DENV2 ( strain 16681 ) were used to generate WT and mutant VLPs containing mutations at critical FL residues ( W101A plus F108A ) as described previously [16] , [24] . VLPs derived from ultracentrifugation of culture supernatants of transfectants of 293T cells were used in a capture ELISA as described previously [14] , [16] . Briefly , 96-well plates were coated with rabbit anti-serum against DENV1 at 4°C overnight , followed by blocking with 1% BSA in 1× PBS for 1 hour . VLPs and mutant VLPs ( at ∼0 . 01 µg/ml ) were added , followed by two-fold serial dilutions ( using dilution buffer ) of each human serum sample , anti-human IgG conjugated to horseradish-peroxidase ( HRP ) , TMB substrate and stop solution [16] . The absorbance at a wavelength of 450 nm ( OD 450 ) with reference wavelength of 650 nm was read . The endpoint titers were calculated as the reciprocal of the highest titers that yielded a signal greater than 3 standard deviations of the mean signal from multiple ( n = 5 ) normal human sera . The proportion of anti-FL Abs was determined by the formula: % anti-FL Abs = [1 - endpoint titer to mutant VLPs/endpoint titer to WT VLPs]×100% [14] . Mixtures of mAbs containing different proportions of a mouse anti-FL mAb ( FL0231 ) and a mouse anti-E domain III mAb ( DA6-7 ) in the VLP-capture ELISA revealed a linear relationship between the proportion of FL0231 ( anti-FL mAb ) added and the measured proportion of FL0231 ( P = 0 . 003 , two-tailed Spearman correlation test ) ( Figure S1 ) . The limit of detection was 4% based on the experiment of mixing mAbs ( Figure S1 ) . The virion-capture ELISA was performed similarly to the VLP-capture ELISA except that DENV1 ( Hawaii strain ) virions derived from ultracentrifugation of culture supernatants of infected Vero cells were used as antigen . Briefly , 96-well plates were coated with rabbit anti-serum against DENV1 at 4°C overnight , followed by blocking with 1% BSA in 1× PBS for 1 hour and adding DENV1 virions ( at ∼0 . 01 µg/ml ) . To determine the [anti-E Abs] , the virion capture-ELISA was initially performed using two-fold serial dilutions of each serum to identify the dilution that gave rise to OD values within the linear range of the standard curve ( from 6 . 6 to 105 . 63 ng/ml ) , based on known concentrations of a human mAb 82 . 11 that targets the FL epitope ( Figure 1A ) [25] . Then the virion capture-ELISA was performed using such serum dilution in parallel to mAb 82 . 11 ( from 1 . 65 to 6760 ng/ml ) in duplicates . The OD values were interpolated to determine the [anti-E Abs] in each human serum sample ( Figure 1B ) ( GraphPad Prism 5 . 0 , GraphPad software Inc . , CA ) . The 50% neutralization titer ( NT50 ) was determined using a flow cytometry-based neutralization assay with reporter viral particles ( RVPs ) of four different DENV serotypes as described previously [26] . Briefly , eight 3-fold dilutions of each serum sample were mixed with DENV RVPs ( a GFP DENV reporter replicon packaged by DENV structural proteins C-prM/M-E expressed in trans ) [26] , [27] and incubated with human Raji-DC-SIGNR cells for 48 hours . GFP-positive infected cells were quantified by flow cytometry , and raw data extracted using FlowJo software , version 7 . 2 . 5 ( TreeStar Software ) was graphed in the GraphPad Prism 5 . 0 program as percent infection versus the log of the reciprocal serum dilution . A sigmoidal dose response curve with a variable slope was then generated to determine NT50 , the Ab dilution at which a 50% reduction in infection was observed compared to the no-Ab control [26] , [28] . For patients with secondary DENV infection , the current infecting serotype was identified by RT-PCR and/or virus isolation , and the previous infecting serotype ( s ) were determined based on the neutralization pattern , the epidemiology of dominant DENV serotype circulation in Nicaragua , and the age of the patients [29] . For each patient , the remaining serotypes were considered as likely “non-exposed serotypes” . The two-tailed Mann-Whitney test was used to determine the difference in [anti-FL Abs] and [anti-E Abs] among primary and secondary DENV infections . The one-tailed Spearman correlation test was used to determine the relationship between [anti-FL Abs] and NT50 using the program GraphPad Prism 5 . 0 . The two-tailed Spearman correlation test was used to determine the relationship between the proportion of anti-FL mAb FL0231 added and that measured in the VLP-ELISA .
We first developed a virion-capture ELISA using known concentrations of a human anti-E mAb , 82 . 11 , to generate a standard curve ( Figure 1A ) [25]; the OD values derived from human sera in the same capture ELISA were interpolated to determine [anti-E Abs] ( Figure 1B ) . MAb 82 . 11 bound to the E protein of the four DENV serotypes equivalently , as shown by Western blot analysis and virion-capture ELISA ( Figure 1C ) , which is in agreement with previous reports of its neutralization potency against the four DENV serotypes at comparable concentration [25] . Table 1 summarizes the [anti-E Abs] in sera collected longitudinally 3 , 6 , 12 and 18 months post-illness from 6 confirmed dengue cases with primary DENV infection and 4 dengue cases with secondary infection . The [anti-E Abs] ranged from 5 . 8 to 158 . 8 µg/ml and 58 . 8 to 1894 . 9 µg/ml in primary and secondary DENV infections , respectively . We next employed a previously-described VLP-capture ELISA using DENV1 WT and mutant VLPs containing representative FL mutations ( W101A plus F108A ) to determine the % anti-FL Abs in each serum sample [14] , [16] . As shown in Figure 2A , the % anti-FL Abs in the 3-month post-illness serum of a secondary DENV2 case ( ID 237 ) was 57% . We also examined the same serum by using DENV2 WT and mutant VLPs ( W101A plus F108A ) in the VLP-capture ELISA , and the % anti-FL Abs was found to be 56% . The % anti-FL Abs determined by DENV1 and DENV2 VLP-capture ELISA was similar in the 6-month and 18-month sera of ID237 and in the sera of other cases as well ( Figure S2 ) . This finding is consistent with the notion that the FL residues recognized by anti-FL Abs are highly conserved by different flaviviruses , and therefore the % anti-FL Abs determined by VLPs of different DENV serotypes was similar . We thus used DENV1 VLP-capture ELISA to determine the % anti-FL Abs for all sera in this study . Table 1 summarizes the % anti-FL Abs in sequential serum samples from 10 cases , which ranged from 4 to 63% . After determining the % anti-FL Abs we calculated the [anti-FL Abs] , which ranged from 0 to 0 . 94 µg/ml ( Table 1 ) . As shown in Figure 3A , the [anti-E Abs] in most cases slightly decreased from 3 months to 6 months after infection except one DHF case , ID 274 , that displayed peak anti-E Abs 6 months after infection . There was no difference in [anti-E Abs] between 6 and 12 months and between 12 and 18 months among primary DENV infections ( P = 0 . 66 and 1 , respectively ) and secondary infections ( P = 0 . 4 and 0 . 88 , respectively , two-tailed Mann-Whitney test ) . The [anti-E Abs] was higher in secondary infections than in primary infections ( P = 0 . 02 , 0 . 07 , 0 . 01 and 0 . 06 , at 3 , 6 , 12 and 18 months , respectively , two-tailed Mann-Whitney test ) . A similar trend in the [anti-FL Abs] was noted , again except ID 274 , where [anti-FL Abs] peaked 6 months after infection ( Figure 3B ) . The [anti-FL Abs] was also significantly higher in secondary as compared to primary DENV infections ( P = 0 . 02 , 0 . 04 , 0 . 04 and 0 . 03 , at 3 , 6 , 12 and 18 months , respectively , two-tailed Mann-Whitney test ) . Notably , two cases , ID312 ( primary DENV1 infection ) and ID265 ( primary DENV3 infection ) , had undetectable anti-FL Abs 12 months after infection . We then examined the [anti-E Abs] , % anti-FL Abs and [anti-FL Abs] in sera collected 12 months post-illness from another 26 patients with secondary DENV infection ( Table S1 ) . Of the total of 30 secondary DENV infections , 13 were classified as DF and 17 as DHF/DSS . No significant difference was observed in the [anti-E Abs] , % anti-FL Abs or [anti-FL Abs] at 12 months post-infection between DF and DHF/DSS patients ( P = 0 . 48 , 0 . 79 , and 0 . 43 , respectively , two-tailed Mann-Whitney test ) , although the number of cases examined was small . We next measured neutralizing Abs in each serum sample against the four DENV serotypes using a previously described flow cytometry-based assay with DENV RVPs [26] . The NT50 in 12-month sera from the 30 secondary DENV infections ( 15 DENV2 , 14 DENV3 and 1 DENV1 ) are summarized in Table 2 . For each patient , the previous infecting serotypes were determined based on the neutralization pattern , the epidemiological history of dominant DENV serotype circulation in Nicaragua , and the age of the patient [29] . The serotypes excluding the current and previous infecting serotype ( s ) were considered as likely “non-exposed serotypes” . We then examined the relationship between [anti-FL Abs] and NT50 12 months post-infection in patients with secondary DENV infection . [anti-FL Abs] did not correlate with the NT50 against the current infecting serotype ( Figures 4A and 4E ) . However , [anti-FL Abs] did correlate with the NT50 against the likely “non-exposed” serotypes ( DENV4 and DENV2 and DENV1 ) for patients with secondary DENV3 infection ( P = 0 . 01 , P = 0 . 03 and P = 0 . 04 , respectively , one-tailed Spearman correlation test ) ( Figures 4B to 4D ) . For patients with secondary DENV2 infection , [anti-FL Abs] correlated with the NT50 against the “non-exposed” serotypes ( DENV4 and DENV3 ) but not DENV1 , likely due to the small sample size of this subgroup ( P = 0 . 03 , P = 0 . 04 and P = 0 . 13 , respectively , one-tailed Spearman correlation test ) ( Figures 4F to 4H ) .
We and others have previously reported that a significant proportion of anti-E Abs in human dengue-immune sera recognize the highly conserved FL residues in domain II of E protein , whereas a small proportion of anti-E Abs recognize E domain III residues [13]–[16] . The role of anti-FL Abs in protection against subsequent infections and/or dengue pathogenesis remains unclear . In this study , we first developed capture ELISAs to measure the [anti-E Abs] and [anti-FL Abs] in sera from dengue patients to investigate the kinetics of anti-E Abs and anti-FL Abs over time and found that [anti-E Abs] and [anti-FL Abs] were quite stable across time and were higher in secondary DENV infections than in primary infections . We then examined the relationship between [anti-FL Abs] and NT50 in patients with secondary DENV infection . While [anti-FL Abs] did not correlate with NT50 against the current infecting serotype , it correlated with NT50 against likely “non-exposed” serotypes ( DENV4 , DENV2 and DENV1 ) in 14 secondary DENV3 cases and “non-exposed” serotypes ( DENV4 and DENV3 ) in 15 secondary DENV2 cases . These findings suggest that anti-FL Abs may play a protective role against “non-exposed” serotypes after secondary DENV infection . It is known that after primary DENV infection , individuals develop life-long protection against the infecting serotype , which correlates with the appearance of monotypic neutralizing Abs against the infecting serotype [30]–[34] . The type-specific anti-E neutralizing Abs rather than the group-reactive anti-FL Abs generated after primary infection are believed to contribute to such monotypic neutralizing activity . This concept was supported by the observation that the monotypic neutralizing activity in human sera after primary infection was greatly reduced by depleting type-specific binding activity with virions of the infecting serotype but was not substantially reduced by depleting cross-reactive binding activity ( including anti-FL Abs ) with virions of other serotypes [17] . Consistent with this , we found that [anti-FL Abs] did not correlate with NT50 against the infecting serotype in patients with primary DENV infection ( data not shown ) . The nature of the type-specific anti-E neutralizing Abs , which were initially thought to be those targeting E domain III based on the studies of anti-domain III mAbs [35]–[38] , were recently reported to be those targeting quaternary epitopes spanning adjacent E dimers on the virion or possibly other as yet to be identified epitopes [17] , [39] . After secondary DENV infection , individuals develop not only neutralizing Abs against serotypes to which they have been previously exposed but also heterotypic neutralizing Abs against serotypes to which they have not yet been exposed [9] . The heterotypic neutralizing Abs are believed to account for heterotypic protection against subsequent infection by non-experienced serotypes and contribute to the very low numbers of hospital admission observed after a third or fourth DENV infection in humans [40] as well as the low rate of viremia after a third DENV infection in monkeys [41]–[44] . The nature of such heterotypic neutralizing Abs remains unknown . One possibility is that some complex-reactive anti-E Abs contribute to the neutralizing activities against “non-exposed” serotypes after secondary infection . Alternatively , the group-reactive anti-FL Abs generated after secondary DENV infection may contribute to such neutralizing activity . Our findings that [anti-FL Abs] correlated with NT50 against likely “non-exposed serotypes” in secondary cases suggest that anti-FL Abs contribute significantly to heterotypic neutralizing activity against “non-exposed” serotypes after secondary DENV infection . Nonetheless , it is likely that such heterotypic neutralizing Abs against “non-exposed” serotypes include other non-FL cross-reactive Abs as well . The epitopes recognized by anti-FL Abs include several key residues such as 101W , 106G , 107L and 108F in the FL of E domain II [16] , [45] , which are highly conserved by different flaviviruses and absolutely conserved by the four DENV serotypes . It is conceivable that during secondary DENV infection , memory B cells recognizing these highly conserved FL residues expand and generate anti-FL Abs with higher avidity through affinity maturation [46] . Consistent with this , studies of human sera after DENV infection , which likely contained a significant proportion of anti-FL Abs , showed that the binding avidity of anti-DENV Abs from secondary infections was higher than that from primary infections [28] , [47] . Moreover , it was reported recently that cross-reactive memory B cells or plasma cells as well as serum avidity increase greatly during acute secondary DENV infection , with greater reactivity to the previous infecting serotype than the current infecting serotype [28] , [48] . Future studies involving experiments that remove cross-reactive anti-FL Abs from the sera of secondary DENV infections and examine the neutralizing activity against “non-exposed” serotypes will help to further elucidate the contribution of anti-FL Abs to heterotypic neutralizing activity . It is worth noting that while anti-FL Abs are cross-reactive to all four DENV serotypes , [anti-FL Abs] did not correlate with NT50 against the current infecting serotype ( Figures 4A and 4E ) . This suggests that type-specific or other non-FL Abs probably dominate the neutralizing activity against the current infecting serotype . In this study , we used known concentrations of a human anti-E mAb ( 82 . 11 ) as a standard in our quantitative virion-capture ELISA to measure [anti-E Abs] in human sera . MAb 82 . 11 , which recognizes FL residues and is a group-reactive neutralizing Ab against four DENV serotypes [25 , data not shown] , was used as a reference for anti-E Abs in human sera because group-reactive anti-FL Abs constitute a significant proportion of anti-E Abs in human serum . The possibility that some anti-E Abs had different binding properties to the DENV virion compared with 82 . 11 and were thus over- or under-estimated in the quantification cannot be completely ruled out . To further validate the use of DENV1 virion- and VLP-capture in determining the [anti-E Abs] , % anti-FL Abs and [anti-FL Abs] in this study , we used DENV3′ virion- and VLP-capture ELISA systems to determine the [anti-E Abs] , % anti-FL Abs and [anti-FL Abs] in 14 DENV3 infection samples , and found a nice correlation with these three values determined by the two systems ( DENV1 vs . DENV3: r = 0 . 91 , P<0 . 0001; r = 0 . 78 , P = 0 . 0009; and r = 0 . 74 , P = 0 . 0027 , respectively , two-tailed Spearman correlation test ) ( Figure S3 ) . In addition , we determined the [anti-E Abs] , % anti-FL Abs and [anti-FL Abs] of 7 DENV2 infection samples using DENV2 virion- and VLP-capture ELISA systems , yielding a good correlation ( DENV1 vs . DENV2: r = 0 . 9 , P = 0 . 0046; r = 0 . 96 , P = 0 . 0028; and r = 1 , P = 0 . 0004 , respectively , two-tailed Spearman correlation test ) ( Figure S3 ) . It is also worth noting that the [anti-E Abs] and [anti-FL Abs] determined were for IgG . To address the possibility that IgM might confound these values , we used a previously described IgM ELISA [20] , [21] to measure anti-DENV IgM and found IgM was negative for all 36 serum samples at 12 months ( data not shown ) , suggesting that IgM is unlikely to confound the 12-month [anti-FL Abs] determined and thus the correlation with NT50 analyzed in Figure 4 . Another concern is that anti-prM Abs might affect the anti-E Abs determined . Previous studies have shown that the level of anti-prM Abs in dengue-immune sera ( either primary or secondary DENV infection ) was much lower than that of anti-E Abs based on the intensity of prM and E bands in Western blot analysis , where the antigens were derived from virus-infected cell lysates and presumably contained equal molar ratios of prM and E proteins [13] , [49]–[51] . We used serial dilutions of known concentrations of anti-prM and anti-E human mAbs together with human serum on the same blot and estimated the level of anti-prM Abs in human serum to be 30 fold less than anti-E Abs ( data not shown ) . Therefore , the amount of anti-prM Abs detected in our virion-capture ELISA is trivial compared with that of anti-E Abs , though the possibility of confounding cannot be completely ruled out . Nonetheless , the [anti-E Abs] thus measured , demonstrating higher concentrations in secondary DENV infections as compared to primary infections , is in agreement with previous reports using other methods such as the plaque reduction neutralization test and endpoint dilution determined by ELISA [9] , [15] , [52] . Moreover , the [anti-FL Abs] calculated based on [anti-E Abs] and % anti-FL Abs showed a correlation with NT50 against likely “non-exposed” serotypes in secondary DENV infections , which is consistent with the historical observations of heterotypic neutralization against “non-exposed” serotypes after secondary DENV infection [9] . In summary , our assay for [anti-FL Abs] provides a simple and quantitative method to study the role of anti-FL Abs in protection against or enhancement of dengue disease .
|
The four serotypes of dengue virus ( DENV ) are the leading cause of mosquito-borne viral diseases in humans . Whereas infection with one DENV serotype is thought to confer protection against re-infection with that serotype , it can be either protective or enhance disease severity upon subsequent ( “secondary” ) infection with a different serotype . The envelope ( E ) protein of DENV is the major target of neutralizing antibodies . Previously , we and others reported that a significant proportion of anti-E antibodies in human dengue-immune sera recognize the fusion loop ( FL ) of E protein . The role of anti-FL antibodies in protection against subsequent DENV infections versus pathogenesis remains unclear . In this study , we developed capture ELISAs to measure the concentrations of anti-E and anti-FL antibodies in sera of Nicaraguan dengue patients collected 3 , 6 , 12 and 18 months post-illness , and investigated the kinetics of these antibodies and their relationship to neutralization activity . While the concentrations of anti-FL antibodies did not correlate with 50% neutralization titers ( NT50 ) against the current infecting serotype , it correlated with NT50 against serotypes to which patients had likely not yet been exposed ( “non-exposed” serotypes ) in secondary DENV infections . These findings provide evidence that anti-FL antibodies play a protective role against “non-exposed” serotypes after secondary DENV infection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Analysis of Cross-Reactive Antibodies Recognizing the Fusion Loop of Envelope Protein and Correlation with Neutralizing Antibody Titers in Nicaraguan Dengue Cases
|
Plasmodium vivax is a widely distributed , neglected parasite that can cause malaria and death in tropical areas . It is associated with an estimated 80–300 million cases of malaria worldwide . Brazilian tropical rain forests encompass host- and vector-rich communities , in which two hypothetical mechanisms could play a role in the dynamics of malaria transmission . The first mechanism is the dilution effect caused by presence of wild warm-blooded animals , which can act as dead-end hosts to Plasmodium parasites . The second is diffuse mosquito vector competition , in which vector and non-vector mosquito species compete for blood feeding upon a defensive host . Considering that the World Health Organization Malaria Eradication Research Agenda calls for novel strategies to eliminate malaria transmission locally , we used mathematical modeling to assess those two mechanisms in a pristine tropical rain forest , where the primary vector is present but malaria is absent . The Ross–Macdonald model and a biodiversity-oriented model were parameterized using newly collected data and data from the literature . The basic reproduction number ( ) estimated employing Ross–Macdonald model indicated that malaria cases occur in the study location . However , no malaria cases have been reported since 1980 . In contrast , the biodiversity-oriented model corroborated the absence of malaria transmission . In addition , the diffuse competition mechanism was negatively correlated with the risk of malaria transmission , which suggests a protective effect provided by the forest ecosystem . There is a non-linear , unimodal correlation between the mechanism of dead-end transmission of parasites and the risk of malaria transmission , suggesting a protective effect only under certain circumstances ( e . g . , a high abundance of wild warm-blooded animals ) . To achieve biological conservation and to eliminate Plasmodium parasites in human populations , the World Health Organization Malaria Eradication Research Agenda should take biodiversity issues into consideration .
The dynamics of malaria transmission involve a tritrophic interaction among vector mosquitoes ( Anopheles species ) , protozoan parasites ( Plasmodium species ) , and vertebrate hosts . Malaria is endemic in tropical and subtropical regions [1]–[3] . The Global Malaria Eradication Program adopted in the 1950s has failed to meet expectations for malaria control in tropical and subtropical countries . One of the causes of that failure was the lack of an in-depth knowledge of the ecology of malaria-parasite transmission [4] . In addition , Plasmodium vivax malaria has been neglected as a chronic disease [5] . The prevalence of malaria remains high , especially in Africa , the Americas , Asia , and the western Pacific . In those regions collectively , the prevalence was 2% in 2011 , most cases occurring in children [6] . Recently , Murray et al . suggested that , although malaria mortality rates have remained stable worldwide , the World Health Organization underestimated malaria mortality for the last two decades , purporting that the number of deaths from malaria among adults in Africa , as well as among adults and children outside of Africa , was substantially higher than that reported [7] . Because of the suffering caused for malaria to humans mainly in developing countries , elimination of this disease is a challenge for the Malaria Eradication Research Agenda [8] . According to the World Health Organization agenda for vector control , there is an urgent need to identify key knowledge gaps in vector ecology and biology [9] . Such knowledge will be important to define strategies for mosquito control , as well as to reduce the number of infective bites and the basic reproduction number [8] . The basic reproduction number ( ) is the expected number of secondary cases arising from a single case in a given susceptible population and is used as a measure of malaria-parasite transmission , as well as of the impact of control programs . In the forested areas of the biogeographical subregion known as the Serra do Mar ( mountain range ) , within the Atlantic Forest of southeastern Brazil [10] , where the levels of insect and vertebrate richness are high [11] , [12] , malaria is hypoendemic [13]–[16] , and the primary malaria parasite being Plasmodium vivax [17] . In the Atlantic forest , species of the Anopheles subgenus Kerteszia are the primary malaria vectors . The majority of Kerteszia species use bromeliad-phytotelmata as larval habitats [18] , and it has been suggested that Kerteszia spp . participate in the dynamics of malaria-parasite transmission in Trinidad [19] and along the Atlantic coast of Brazil [20] , [21] . Between 1944 and 1951 , there were malaria epidemics in the southern Atlantic Forest within the states of Santa Catarina and Paraná , the overall incidence for the period being 5% [21] . Such epidemics mainly ceased because of deliberate deforestation that eliminated 3 , 800 of native forest [22] , removing bromeliads and reducing the number of resting sites for adult mosquitoes within the forest [21] . Although malaria epidemics are currently uncommon , temporal and spatial clustering of cases can occur in the Atlantic Forest . One low-incidence outbreak occurred among outdoor workers in the forested highlands of the state of Espírito Santo between 2001 and 2004 [23] . In 2006 , another epidemic occurred in the southern periphery of the city of São Paulo , where residents of the Marsilac district invaded the Serra do Mar Natural Forest Reserve to construct houses [24] . Given the presence of Anopheles vector species , as well as that of infected and susceptible hosts , together with the circulation of Plasmodium , it is hypothesized that ecological interactions among Anopheles ( Kerteszia ) cruzii , Plasmodium species , and the local biodiversity are modulating malaria transmission in the Serra do Mar . Forested areas offer a diverse range of habitats for mosquito species [25] . Consequently , high levels of mosquito species richness and abundance are expected [11] . This scenario can decrease the number of infective bites , because multiple vector and non-vector mosquito species would try to feed on a defensive host [26] , [27] , decreasing the chances of successful bites by the vector population . In addition , malaria parasite transmission could be affected by an abundance of non-competent hosts that would prevent mosquitoes from transmitting Plasmodium parasites to humans [28] . This would represent a dilution effect of wild warm-blooded animals , which act as dead-end hosts [29] . Therefore , diffuse mosquito vector competition and dead-end transmission of parasites are mechanisms in the dynamics of malaria transmission in tropical forests that , consequently , can alter the chances of malaria emergence . The insight that ecological mechanisms can influence the dynamics of malaria parasite transmission supports arguments against human occupation of protected natural areas . Current theory says that biodiversity can have an impact on the emergence and transmission of infectious diseases , which is a new focus of conservation studies [30] . Some authors have shown that when biodiversity declines , there is an increased risk of humans contracting schistosomiasis [31] , West Nile fever [32] , hantavirus infection [33] , or Lyme disease [34] . Similarly , diseases that affect coral reefs become more widespread when biodiversity is reduced by human activities [35] . The relationship between a decline of biodiversity and an elevated risk of vector-borne disease might be attributable to changes in the abundance of hosts and vectors or to modified host , vector , or parasite behavior [30] . In the Brazilian Amazon , gradual and continuous changes in the natural ecosystems can create ecological conditions that favor a rapid increase in abundance of Anopheles darlingi , which have been associated with an increased risk of malaria . Sawyer and Sawyer coined the term “frontier malaria” to define the dynamics of malaria transmission in recently deforested areas of the Amazon Forest [36] . In those areas , malaria transmission decreases when the natural ecosystem is highly modified , to the point that the maintenance of vector species and Plasmodium circulation are not ecologically supported [36] , [37] . Therefore , malaria in Amazon and in the Atlantic Forest are both associated with biodiversity , because the larval habitats of An . darlingi , the primary vector in Amazon Region , and An . cruzii , the primary vector in Atlantic Forest , depend on the presence of forested areas . Historically , tropical regions have been considered economically underdeveloped hotspots of biodiversity [38] . However , Brazil is now becoming an emerging global economy , which suggests that its forest cover , along with its biodiversity , will decline rapidly [39] . If this occurs , frontier malaria will be eliminated , which would increase the risk of rural and urban malaria alike , because Anopheles marajoara could become a vector of Plasmodium parasites [40] , [41] . Given that malaria cannot be completely eliminated and that there is an urgent need for conservation/restoration of tropical biodiversity , it is important to understand interactions between the dynamics of malaria transmission and the diversity of vertebrates and mosquitoes . Here , we employed a mathematical model to develop a theoretical framework that might explain how biodiversity can modulate malaria epidemics in a tropical rain forest . Our case study site is a protected area within the Atlantic Forest , inhabited by indigenous peoples and fishermen , where An . cruzii is present but no malaria cases have been reported in the last 30 years . Our objectives were to propose a novel mathematical model for malaria transmission with explicit mechanisms of diffuse mosquito vector competition and dead-end transmission of parasites , applying this model to this case study site , as well as assessing how diffuse competition among mosquito vectors and dead-end transmission affect malaria epidemics .
The Iguape-Cananéia-Paranaguá estuarine lagoon region is a coastal plain area of approximately 22 , 600- , situated on the southeastern coast of Brazil ( Figure 1A ) , between the Ribeira de Iguape River and the Atlantic Ocean ( Figure 1B ) . Three great islands stretch along the coast a hundred kilometers from northeast to southwest , namely Comprida , Cananéia , and Cardoso [42] . Cardoso island , hereafter referred to as Parque Estadual da Ilha do Cardoso ( PEIC ) , is a São Paulo State Park within the Atlantic Forest [43] . The PEIC is separated from the mainland by the Ararapira Channel , a body of water that is as narrow as 30 m wide at places . Therefore , even large animals , such as muriqui ( Brachyteles ) can cross [44] . Common wild warm-blooded animals include medium to large birds , such as quail ( Odontophorus capueira ) , toucans ( Ramphastos species ) , guans ( Penelope species and Pipile jacutinga ) , tinamous ( Tinamus solicarius ) , and mammals , such as howler monkeys ( Alouatta species ) , agoutis ( Dasyprocta leporina ) , and squirrels ( Sciurus ingrami ) , as described by Bernardo [45] . Vegetation types form a successional gradient from sand dunes at the shore to higher and ancient terrains inland . Along the coastal plain , there is sand dune vegetation , scrubland , and low forests with sandy soil ( arboreal restinga , or shoal vegetation ) . As can be seen in Figure 1C , tropical pluvial forest vegetation types are found on hillsides and hilltops [46] . Descendents of European colonists previously occupied what is now the PEIC , and the major local activities were fishing and family farming [47] . The population density is currently approximately 3 . 3 people/ , which has no relevant impact on local biodiversity . However , tourism has become one of the main sources of income , and thousands of tourists arrive every summer in the fishing village of Marujá , to the south ( Figure 1C ) . In addition , the indigenous Guarani Mbya tribe has been settled in the northwestern part of the PEIC since 1992 , having the right to engage in subsistence hunting and logging in the forest [48] , [49] . Anopheles cruzii , i . e . , a primary malaria vector in the Atlantic Forest , is present in PEIC . Although no malaria cases have been reported in the last 30 years in PEIC , P . vivax is circulating in the immediate surrounding region [13] , [15] , [17] ( Figure S1 ) . Therefore , it is plausible to assume that introduction of Plasmodium species can occur in the region because of the thousands of tourists that visit the PEIC during the summer , including those traveling from endemic areas . The mathematical model proposed herein represents parasite transmission among four compartments that play specific roles in the dynamics of malaria transmission in the Atlantic Forest . The first compartment is susceptible human populations in the Guarani and Marujá settlements , which were set at constant sizes . Considering that few humans are allowed to live in the PEIC , the size of the human population size remains approximately constant . However , malaria could emerge in the area because of its location in the Serra do Mar , where low-level endemic parasite transmission occurs [13] , [15] . In addition , it is also plausible to assume that introduction of Plasmodium species can occur in the region because of the 15 , 000 tourists that visit the PEIC during the summer [48] , including those traveling from endemic areas . Consequently , the human population from PEIC can be exposed to malaria parasites . The human risk of Plasmodium infection in PEIC depends on the An . cruzii biting rate [50] and the probability of an infective bite [51] , [52] . Therefore , a proportion of susceptible humans are included in the second compartment , infected humans . For this simple vector-human malaria parasite transmission , however , ecological interactions can be added in order to create a more realistic scenario of malaria transmission . Other mosquito species and a diversified vertebrate community [12] , [45] ( Table S1 and Table S2 ) are intermixed , competing for food and spatial resources with mosquito vectors and humans . Therefore , human malaria transmission may become difficult because of the increased abundance of non-vector mosquitoes and of vertebrate animals . Infectious humans can recover , becoming again susceptible to infection with malaria parasites . This assumption is based on the lack of human cross-immunity against Plasmodium species that circulate in the Atlantic Forest . Until infectious humans are cured of the infection , they can infect An . cruzii [50] with a given probability of infection [51] , [52] . In the third compartment , An . cruzii population was assumed to be in equilibrium , meaning that its mean mortality rate [50] was assumed to be lower than its hatch rate because of its high abundance in forested areas of the Atlantic Forest [14] , [21] . The An . cruzii hatch rate is dependent on successful bites on animals and humans . Biting success was expressed by the mean period of free biting until the occurrence of host defensive behavior ( Figure S2 ) . Susceptible An . cruzii in the third compartment may be infected by means of biting infectious humans . Non-vector mosquito species decrease the risk of human-vector contact , whereas increased numbers of animals and humans can increase vector abundance . In the forth compartment , An . cruzii is now infected by Plasmodium and can transmit it back to humans . To calculate the risk of malaria parasite transmission , we used our model and the Ross–Macdonald model for the two most populated human settlements , the Guarani village and Marujá ( Figure 1C ) . With the Ross–Macdonald model , it was assumed that An . cruzii abundance was constant; that is , there were no ecological interactions [53] . Therefore , the model was mathematically expressed as follows [54]: ( 1 ) ( 2 ) ( 3 ) ( 4 ) where = susceptible humans; = infected humans; = susceptible mosquitoes; = infected mosquitoes; ; = biting rate; = transmission probability from a biting infected mosquito to a human; = transmission probability from a infected human to a biting mosquito; = recovery rate by humans; and = mortality rate of vector mosquitoes . To add ecological interactions , such as diffuse mosquito vector competition and dead-end transmission of parasites , we based our model on the Ross–Macdonald model [54] . First , we transformed the transmission factor in the Ross–Macdonald model ( i . e . , ) in another in which malaria-parasite transmission can be blocked by defensive hosts and non-vectors ( i . e . , diffuse mosquito vector competition ) and non-hosts ( i . e . , dead-end transmission of parasites ) : ( 5 ) where = abundance of An . cruzii females , = biting tolerance exhibited by vertebrate animal and human hosts; = abundance of wild warm-blooded animals; and = abundance of non-vector mosquito females . Second , we considered that population dynamics of An . cruzii females was present: ( 6 ) where the rationale is that a successful bite is needed in order to start a new mosquito generation in the larval habitat in which density-dependent mechanisms can occur . It includes the following factors: female adult recruitment in the larval habitat ( ) and female adult activity plus interactions with hosts and non-vectors . The parameter is a measure of recruitment of adult female emergence and it can be fitted with abundance data collected in the field . Predation and competition which are strong components of mosquito populations in the larval habitat are implicitly considered in this parameter . Abundance of An . cruzii adult females was assumed to be associated with life conditions and interactions in the larval habitat to estimate . This association means that high abundance of adult females is expected when optimum physical , chemical and biological conditions are present in the larval habitat , or vice-versa . Abundance data was obtained with automatic CDC-CO2 traps which collect a sample of species ( i . e . , host-seeking females ) in mosquito community . This automatic trap do not mimic host defensive behavior and diffuse competition which were thus made explicit in the population dynamics of An . cruzii females . Third , we estimated considering that the abundance of An . cruzii is in equilibrium ( ) and utilizing the following equation: ( 7 ) where the underlying assumption is that competition affects both An . cruzii and non-vector mosquito species , leading to a situation in which vector and non-vector mosquito species can coexist and An . cruzii is therefore not excluded by competition . Therefore , our final model for abundance of individuals in each compartment is as follows: ( 8 ) ( 9 ) ( 10 ) ( 11 ) where . The Ross–Macdonald model is a special case of our model if we consider that wild warm-blooded animals are either absent or do not interact with An . cruzii ( ) ; non-vector mosquito species are absent or do not interact with An . cruzii ( ) ; humans do not react to mosquito bites ( ) ; and An . cruzii abundance is constant ( ) . Analyses performed using our model had parameter values as inputs in order to calculate the basic as an output ( Text S1 , Text S2 ) . The goal of the analyses was to examine the relationship between malaria transmission dynamics ( synthesized by estimate ) and hypotheses regarding ecological interactions ( formalized into a model structure ) . Finally , an intermediate model , in which both An . cruzii and non-vector mosquito species have constant populations , was elaborated ( Text S3 ) .
On the basis of collected data and data from the literature , the models described in the Materials and Methods section were completely parameterized for the case study site ( Text S1 ) . Empirical values were unavailable only for the host tolerance ( ) parameter , which was set to a range of permissible values and submitted to a sensitivity analysis ( Text S1 ) . The indigenous settlement in the study area ( a village occupied by members of the Guarani Mbya tribe ) is inhabited by 150 natives in a 2 . 8- area , whereas 165 people ( fishermen and their families ) live in Marujá in a 0 . 8- area ( parameter; Figure S3 ) . As can be seen in Table 1 and in the Figure S4 , Figure S5 and Table S1 , the estimated abundance of wild birds and mammals ( parameter ) was 172 in The Guarani Mbya village and 47 in Marujá . The estimated abundance of mosquitoes in The Guarani Mbya village and Marujá , respectively , was 1 , 514 and 300 for the malaria vector An . cruzii ( parameter ) , compared with 14 , 101 and 3 , 640 for non-vectors ( parameter ) , which were supported by Figure S6 , Figure S7 and Table S3 . Data in the literature , from laboratory and field experiments , show that the estimated maximum An . cruzii biting rate ( parameter ) is 0 . 5 bites/day ( Table 1 ) . In the laboratory , we found the estimated gonotrophic cycle to be four days , and our field experiments indicated gonotrophic discordance in natural populations ( Table 1 ) . In our laboratory experiments , An . cruzii mortality ( parameter ) was estimated to be 0 . 80/day ( Table 1 ) . Employing the previously mentioned data from the literature , we estimated the parameter , which indicates how many new adults will be generated from a single successful bite of An . cruzii , to be 5 . 5 in The Guarani Mbya village and 3 . 1 in Marujá ( Table 1 ) . The estimated probabilities of Plasmodium species transmission and the human recovery rate correspond to the dynamics of transmission in a low-endemicity area ( Table 1 ) . Mosquitoes transmit malaria parasites to humans with a probability of 0 . 022 ( parameter ) , humans infect mosquitoes with a probability of 0 . 24 ( parameter ) , and the human recovery rate ( parameter ) is 0 . 0035/day , which means that average duration of the infectious period is 286 days ( Table 1 ) . The parameter was derived from two other values: the number of bites/day before a host starts a defensive action ( assumed herein to be 10 bites/day ) , divided by the maximum biting rate ( 0 . 5 bites/day for An . cruzii ) . Another way to interpret the parameter is that host defensive behavior will be stronger when the average biting rate is greater than 10 bites/day ( Table 1 ) . On the basis of our field work experience in tropical forests ( mainly the Atlantic Forest ) , we can state that neither humans nor animals can stand mosquito biting rates greater than approximately 10 bites/day before they begin to exhibit defensive behavior ( e . g . , shaking body parts or , in the case of humans , waving hands and swatting ) . Therefore , was set at 20 . Mosquito vector diffuse competition and dead-end parasite transmission patterns were assessed for values within 20 and 30 ( e . g . , 21 , 25 , and 29 ) in the sensitivity analysis ( Figure S8 , Figure S9 and Figure S10 ) . As a result , no qualitative changes could be made in the initial interpretations ( see the following paragraphs ) . The basic ( Text S2 ) , as calculated by the Ross–Macdonald model is as follows: ( 12 ) whereas the basic predicted by our model is the following: ( 13 ) where no malaria cases are to be reported when is 1 and if it exceeds 1 ( 1 ) then the disease can invade and there should be an epidemic episode . If we employ the estimate relative to the Ross–Macdonald model ( eq . 12 ) , The Guarani Mbya village would have an of 2 . 18 and Marujá would have an of 0 . 93 . Using the model employed in the present study ( eq . 13 ) , we found the to be 0 . 30 for The Guarani Mbya village and 0 . 39 for Marujá . If the abundances of non-vector mosquito and non-host vertebrate species were reduced by approximately 80% and 70% , respectively , the critical threshold level ( = 1 ) would be exceeded in The Guarani Mbya village ( Figure 2 ) . Similarly , a 50% reduction in the abundance of non-vector mosquito species could cause malaria invasion ( 1 ) in Marujá ( Figure 3 ) . However , epidemics would not occur in Marujá even if there were local extinction of all non-host vertebrate species but would occur if the human population increased by approximately 50% ( Figure 4 ) . Additionally , new simulations utilizing the intermediate model ( Text S3 ) supported the previous statements ( Figure S11 ) and showed that the original results are robust .
Although scarce malaria cases are reported annually in the surrounding area [13] , [15] , [17] , no malaria cases have been reported in the last 30 years , being assumed that Plasmodium transmission does not occur in Parque Estadual da Ilha do Cardoso . The estimated provided by Ross–Macdonald model ( ) suggests that malaria parasite transmission may be occurring in that region . Contrasting , based on the model proposed herein , estimated for The Guarani Mbya village was 0 . 30 , which is in accordance with assumption of no malarial transmission . This model simulations ( i . e . , predicting hypothetical scenarios ) were performed in order to show what would happen with estimate value if: 1 ) Non-vector populations increase ( i . e . , effect of diffuse mosquito vector competition ) , 2 ) Non-host populations increase ( i . e . , effect of dead-end transmission of parasites ) , and 3 ) Human population increases ( effect of over-encroachment of human populations ) . Simulation results provide support for biodiversity preventing the circulation of P . vivax in human settlements embedded in natural ecosystems . The absence of malaria cases can be explained by the diffuse mosquito vector competition and dead-end transmission of parasites provided by high abundances of mosquitoes and vertebrates . Greater abundances of mosquitoes and vertebrates can be correlated with higher levels of biodiversity , which increase ecosystem's functional redundancy , thus decreasing the chances of malaria occurrence , which is in keeping with the insurance hypothesis [55] . According to this hypothesis , an insurance effect is the ability of an ecosystem to buffer perturbations ( e . g . , P . vivax circulation ) , as well as the ability of the species in the community to respond differentially to perturbations ( e . g . , diffuse mosquito vector competition and dead-end transmission of parasites ) . Therefore , these mechanisms that hinder malaria parasite transmission are services provided by the forest ecosystems . In view of the results of simulations conducted using the models applied in the present study ( Figure 2 , 3 ) , we suggest that increasing non-vector mosquito abundance can reduce the number of An . cruzii bites , decreasing malaria parasite transmission in the Atlantic Forest . A new law of mosquito-host relationship is proposed here and it is supported by the following evidences: 1 ) there must be an intense selection pressure on hosts to exhibit defensive behavior against biting insects [56] , [57] , and 2 ) contacts between mosquito species and specific hosts in a community may be influenced more by the presence/absence of hosts than by innate mosquito choices [58] . This law can be defined as a community of defensive hosts in which the access to their blood is a limiting resource , providing competition among opportunistic blood-feeder mosquito species . The total abundance of non-vector and not-infected vector mosquito species can have a negative impact on malaria parasite transmission because of apparent competition mediated by host defensive behavior . The effect of apparent competition is a functional response that may be associated with host tolerance to mosquito bites . When the host tolerance threshold is reached , mosquito bites are avoided by defensive responses from the host . The presence of non-vector and not-infected vector mosquitoes seems to propitiate a larger number of unsuccessful bites , with few Plasmodium-infective bites . The vector competition effect could also occur within species . For example , when there is more larval habitat available ( during the wet season ) , hatch rates increase , making the proportion of nulliparous females larger than that of parous females . Host defensive behavior was observed for blacklegged ticks that are killed when feeding on the blood of opossums and squirrels [34] . Consequently , diffuse competition is a protective mechanism against infective bites and should therefore be considered a major factor in studies related to the dynamics of malaria transmission . In considering that Plasmodium species infection can affect the feeding behavior of anthropophilic mosquitoes [59] , it would be important to understand how the mechanism of diffuse competition can be applied to malaria control strategies in endemic tropical regions . The abundance of wild warm-blooded animals can decrease the transmission of Plasmodium species . Such animals can act as dead-end hosts , diminishing the chances of infective bites in humans , which can be used as an indirect method of malaria control . This might represent a dilution effect mechanism present in natural ecosystems that have a high abundance of warm-blooded animal species . Dilution effect mechanisms [29] were observed by Swadle and Calos [32] , Johnson et al . [31] , and Suzán et al . [33] for West Nile fever , schistosomiasis , and hantavirus infections , respectively . However , a low- to medium-level abundance of dead-end hosts can create a neutral situation in which the dilution effect is either unimportant [60] or harmful [28] . Using a computer simulation , Allan Saul showed that the dilution effect ( zooprophylaxis ) can be harmful when a small number of dead-end hosts potentiate malaria parasite transmission by providing blood-feeding opportunities to vectors [28] . Our model predicts that few wild warm-blooded animals can serve as blood sources for mosquito species , increasing the vector population and Plasmodium species dissemination . This can be seen in the non-linear unimodal relationship between the abundance of non-hosts and the critical threshold level ( ) , as depicted in Figure 4 . This finding is supported by the work of Randolph and Dobson , who stated that the dilution effect applies only to species-rich host communities in which there is variable reservoir competence [61] . In addition , hunting activities that are allowed for traditional human communities in natural protected conservation units can reduce vertebrate abundance , whereas it increases the density of vegetation and the abundance of invertebrates , resulting in the so-called “empty forest” effect [62] and increasing the chances of malaria parasite transmission . Having the present model as a starting point , two new avenues can be pursued for studying dynamics of malaria transmission in tropical forests . In respect of a hypothesis suggesting that non-human hosts may be reservoirs of malaria-parasites [15] , the present model can be extended by means of new compartments along with theirs parameters representing the role of susceptible and infective primates . Moreover , the present model assumes that all host species have the same tolerance to mosquito bites . Considering that animals may have more tolerance to mosquito bites than humans , this assumption can be unlikely and thus dilution effect herein may predict a underestimated blocking-transmission impact because of ( more ) intolerant dead-end hosts . It is therefore important to evaluate how primates as Plasmodium-reservoirs and tolerance of warm-blooded animals to mosquito bites may affect , positive or negatively , dilution effect predictions in the dynamics of malaria transmission . Plasmodium-infected An . cruzii were found within human domiciles during epidemics occurring in the municipalities of Blumenau , Brusque , Joinvile , and Florianópolis , all located within the Atlantic Forest region , in the 1940s and 1950s . One determinant of the malaria burden in those days was the rapid increase in the population of susceptible humans , which reached 800 , 000 in a short period of time [21] . Another determinant was that humans were immunologically naïve to Plasmodium species infection . Consequently , while clearing native forest for agriculture and cattle farming , they lived in the nearby jungle , which increased the contact between humans and infective mosquitoes . It is likely that more recent malaria epidemics in the Amazon Forest occurred because of ecological and social determinants similar to those present in the Machadinho settlement project in the state of Rondônia between 1984 and 1995 . Castro et al . observed that the prevalence of malaria increased rapidly in the early stages of settlement and subsequently decayed , reaching a low level 11 years later , which represents the general pattern of frontier malaria in the Amazon [37] . One way of avoiding malaria epidemics in tropical regions ( mainly in the Amazon ) is clearing large areas of forest and rapidly establishing agriculture or farming in order to limit the exposure of new settlers to infective mosquito bites [37] . This is in consonance with the traditional approach of forest clearing used in the Atlantic Forest in the 1950s [21] . In contrast , the results of the approach taken in the present study suggest that biodiversity contributes to disease control and thus ecosystems in tropical forests can be managed to sustain an equilibrium between high levels of biodiversity and the over-encroachment of human populations . Furthermore , diffuse mosquito vector competition can be considered a novel measure of vector control , especially because some Anopheles vector species seem not to be susceptible to indoor residual insecticide spraying and treated bed nets , which are currently the most successful strategies in Africa [8] . Contrary to what has long been believed , forest conservation and malaria control are not incompatible , and biodiversity issues should be included in the World Health Organization Malaria Eradication Research Agenda in order to achieve the desirable goals of biological conservation and maintenance of low malaria endemicity . Although releasing non-vector mosquitoes is not a practical alternative as vector control , conservation of the natural ecosystems may hinder transmission of malaria-parasites . The main application of the present model is to provide a formal framework in which biodiversity conservation and control of the human population size in protected areas are measures that can be taken to control transmission in any malarial endemic settings . The effect of mosquito vector diffuse competition means that policies of removal of native vegetation to eliminate malarial vectors , which were practiced in the past [21] , have their shortcomings because they may also decrease non-vector community that buffers malarial transmission . For rural malaria , which includes Anopheles gambiae malarial dynamics in Africa , the mosquito vector diffuse competition is also a plausible underlying mechanism because it supports high transmission rates when native fauna is locally depleted by forest removal . Dead-end parasite transmission ( dilution effect ) , by the framework herein proposed , was shown to be highly dependent on host tolerance . Consequently , there are two general predicted scenarios , i . e . , 1 ) this mechanism may favour parasite decrease if the most tolerant host is a dead-end and 2 ) it may increase the vector population if tolerant hosts are present . It is noteworthy that these scenarios are not mutually exclusive . According to the subliminal message in Smith and colleagues' work [63] , scientists of the present century should go beyond the Ross-Macdonald's Theory in order to have better insights on the ways that make possible the control of malarial transmission . In addition , the present model also makes qualitative predictions , and not just a correction in the value of , that are very distinct from the Ross-Macdonald ( R-M ) model , e . g . , the behavior of when ( i . e . , human population ) increases: it decreases in the R-M model , but it increases in the dynamics of the present model because greater implies higher vector-host contacts , leading to increase of parasite dissemination . The present model constitutes an essential step for understanding the dynamics of malaria transmission in tropical forest ecosystems that can provide the service of hindering malaria epidemics , allowing to reconcile malaria control with conservation of biodiversity .
|
Plasmodium vivax malaria is a neglected infectious disease that can cause severe symptoms and death in tropical regions . It is associated with an estimated 80–300 million cases of malaria worldwide . Brazilian tropical rain forests are home to a rich community of animals that can participate in the dynamics of malaria transmission . In this study , we used real data and computer simulation to study two aspects of biodiversity ( an increase in the abundance of wild warm-blooded animals; and an increase in the abundance of non-malarial mosquitoes ) and the effects they have on malaria outbreaks . We found that both aspects can help prevent malaria outbreaks in tropical forests . We also found that a decrease in the abundance of wild warm-blooded animals can increase the population of malarial mosquitoes and thus increase the chances of malaria outbreaks . Forest conservation and malaria control are not incompatible and thus biodiversity issues should be included in the World Health Organization Malaria Eradication Research Agenda in order to achieve the desirable goals of biological conservation and maintenance of low malaria transmission .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"ecology",
"epidemiology",
"biology"
] |
2013
|
Biodiversity Can Help Prevent Malaria Outbreaks in Tropical Forests
|
Alarm communication is a key adaptation that helps social groups resist predation and rally defenses . In Asia , the world’s largest hornet , Vespa mandarinia , and the smaller hornet , Vespa velutina , prey upon foragers and nests of the Asian honey bee , Apis cerana . We attacked foragers and colony nest entrances with these predators and provide the first evidence , in social insects , of an alarm signal that encodes graded danger and attack context . We show that , like Apis mellifera , A . cerana possesses a vibrational “stop signal , ” which can be triggered by predator attacks upon foragers and inhibits waggle dancing . Large hornet attacks were more dangerous and resulted in higher bee mortality . Per attack at the colony level , large hornets elicited more stop signals than small hornets . Unexpectedly , stop signals elicited by large hornets ( SS large hornet ) had a significantly higher vibrational fundamental frequency than those elicited by small hornets ( SS small hornet ) and were more effective at inhibiting waggle dancing . Stop signals resulting from attacks upon the nest entrance ( SS nest ) were produced by foragers and guards and were significantly longer in pulse duration than stop signals elicited by attacks upon foragers ( SS forager ) . Unlike SS forager , SS nest were targeted at dancing and non-dancing foragers and had the common effect , tuned to hornet threat level , of inhibiting bee departures from the safe interior of the nest . Meanwhile , nest defenders were triggered by the bee alarm pheromone and live hornet presence to heat-ball the hornet . In A . cerana , sophisticated recruitment communication that encodes food location , the waggle dance , is therefore matched with an inhibitory/alarm signal that encodes information about the context of danger and its threat level .
Animals have evolved sophisticated abilities to communicate predator threats . These warning signals are found in a wide range of organisms , such as insects , birds , and primates [1–3] . In some vertebrates , these signals can be referential , encoding information about objects or events external to the signaler , such as the identity of the predator , and eliciting appropriate responses [4–6] . More commonly , individuals can increase signaling or modify their signals to indicate threat urgency . Chuck alarm calls in Richardson’s ground squirrel ( Spermophilus richardsonii ) increase when the alarm stimulus is closer and elicit greater vigilance [7] . Modulation of individual signals or varying call sequences also occurs . Predators that are closer , larger , and faster elicit chicken alarm calls that are shorter , louder , and lower in frequency [2] . Mexican chickadees ( Parus sclateri ) give higher-pitched alarm calls in riskier situations [8] . Redfronted lemur ( Eulemur fulvus rufus ) alarm vocalizations increase in frequency in response to higher threat arousal levels , and higher frequencies elicit longer orienting responses [3] . Squirrel monkeys ( Saimiri sciureus ) also increase call frequencies in response to greater threats , and higher frequency or louder calls also result in longer orientation responses [9] . Alarm calls of the social mongoose ( Suricata suricatta ) vary acoustically depending upon the predator type and urgency level [10] . However , to date , no such graded individual alarm signals have been identified in social insects , although many social insect species are highly cooperative and depend upon signaling to coordinate key aspects of their collective behavior [11–17] . Nearly all social insects can use alarm pheromones to communicate danger [1] . Termites [15] , treehoppers [16] , and ants [17] can also use vibrations to signal alarm . However , there is no evidence that these individual signals are graded according to threat level , perhaps because the collective response of the colony provides the overall , finely tuned response , with multiple alarm signals additively reflecting the level of danger and eliciting an appropriate reaction . The European honey bee , Apis mellifera , does possess an inhibitory signal , the stop signal , which can be elicited by peril to reduce recruitment to a dangerous location [18] . For example , A . mellifera foragers produce stop signals to inhibit waggle dancing [19–21] for dangerous food sources . A stop signal is a 300–400 Hz vibrational signal with a duration of approximately 150 ms [20 , 22] that a worker usually delivers while butting its head into the body of the receiver , causing the receiver to momentarily freeze [12 , 19 , 23 , 24] . Attacks by wasp and spider predators [12] and conspecifics [18] elicit stop signals , which inhibit waggle dancing . However , stop signals are also used in other inhibitory contexts that are not associated with danger . House-hunting bees speed up nest site decision-making with stop signals that inhibit recruitment for competing nest sites [20] . Food source overcrowding also triggers stop signaling [19 , 22 , 25] . In A . mellifera , the stop signal is thus best understood as an inhibitory signal that reduces waggle dancing . Nevertheless , stop signals may possess additional functions if they occur in other bee species , particularly if there is an evolutionary need for a warning signal against common and deadly predators . The Asian honey bee species , Apis cerana , is an excellent model for studying the effects of predator threats on colony signaling , because these bees require a coordinated defense against common hornet predators . A . cerana occurs throughout southern and eastern Asia , with a geographic range extending from India to China and Japan [26] . Throughout this range , hornets such as Vespa velutina [27] and the larger hornet , Vespa mandarinia ( Fig 1 ) , can attack A . cerana foragers ( Tan , personal observation ) and directly attack the nest [28–30] . These nest attacks pose a severe danger to colony survival , because the armor of both hornet species resists bee stings . Hornets typically wait outside the nest entrance , killing nest defenders until they are depleted , and then enter the nest to harvest the brood [29] . Up to 30% of a colony’s workers can die during a single attack upon a poorly defended colony [31] . Apis cerana has , therefore , evolved a remarkable strategy of forming a ball of bees around the attacker and heating it to death [29] . It is unclear exactly what triggers heat-balling , though alarm pheromone [31] and , potentially , other signals may be involved . We thus chose to study A . cerana , a native honey bee species that is also an important pollinator of agricultural and native Asian plants [32 , 33] . Hornets attack A . cerana in two contexts: during foraging and at the nest . We therefore tested if workers would produce stop signals in both situations . We hypothesized that predators posing a greater threat to individuals or the colony would elicit stop signals with graded changes in duration , frequency , or both . Our goals were to determine ( 1 ) if A . cerana has a “stop signal” that inhibits waggle dancing , ( 2 ) if such stop signals are elicited by attacks upon foraging bees and the nest , ( 3 ) if a more dangerous predator changes signaling characteristics and signal number , ( 4 ) if these signals are referential , and ( 5 ) if stop signals influence heat-balling .
The large hornet ( V . mandarinia ) elicited stronger forager aversion ( Fig 1A ) and greater colony mortality ( Fig 1B ) than the small hornet ( V . velutina ) . Colonies allocated foraging according to predator danger ( F2 , 49 = 834 . 26 , p < 0 . 0001 , all pairwise differences significantly different , Tukey’s Honestly Significant Difference [HSD] test , p < 0 . 05 ) . During nest attacks , 6-fold more bees heat-balled the large hornet as compared to the small hornet ( F 1 , 14 = 399 . 45 , p < 0 . 0001 ) , which is what one would expect given that the surface area of an ellipsoid surrounding a hornet is 6-fold greater for the large ( 0 . 91 cm2 ) as compared to the small hornet ( 0 . 15 cm2 ) . The larger hornet resulted in greater bee mortality: 13-fold more bees died in the heat ball around the large hornet as compared to the small hornet ( F 1 , 16 = 275 . 83 , p < 0 . 0001 ) . Foragers that were attacked by the large but not the small hornet remained in the nest significantly longer upon their return ( attack phase effect: F 1 , 57 = 93 . 58 , p < 0 . 0001 ) as compared to when they were not attacked ( Fig 2 ) . Treatment ( hornet type effect: F 1 , 55 = 20 . 25 , p < 0 . 0001 ) and the interaction treatment*phase ( F 1 , 57 = 36 . 57 , p < 0 . 0001 ) were significant , and the colony accounted for 3% of model variance . Similarly , foragers attacked by the large hornet waited longer before unloading their food ( phase effect: F 1 , 57 = 76 . 04 , p < 0 . 0001 , 10% colony effect ) . Treatment was significant ( F 1 , 55 = 22 . 41 , p < 0 . 0001 ) , and the interaction treatment*phase was significant ( F 1 , 57 = 9 . 87 , p = 0 . 003 ) . Foragers attacked by the large hornet also spent less time unloading their collected food ( phase effect: F 1 , 57 = 14 . 35 , p = 0 . 0004 ) . Treatment ( F 1 , 55 = 30 . 42 , p < 0 . 0001 ) and the interaction treatment*phase ( F 1 , 57 = 8 . 42 , p = 0 . 005 ) were significant ( 9% colony effect , Fig 2 ) . Attacks reduced waggle dancing ( phase effect: F 1 , 57 = 18 . 97 , p < 0 . 0001 . Treatment and the interaction treatment*phase were not significant ( <1% colony effect , Fig 2 ) . Attacks by both hornet species elicited stop signals . A stop signaler typically produced a vibrational signal by lunging at a receiver and making contact with its head against the receiver . All receivers were motionless while the vibrations were delivered , exhibiting the classic freezing response seen in A . mellifera [12] . There was a significant effect of phase ( F 1 , 57 = 96 . 55 , p < 0 . 0001 ) , but treatment and the interaction treatment*phase were not significant ( 12% colony effect , Fig 2 ) . Although large hornets appeared to elicit more stop signals per forager than small hornets , this difference was not significant ( Tukey’s HSD test , p > 0 . 05 ) . We then examined the effects of predator attack on colony-level signaling . When hornets attacked foragers at the feeder ( Fig 3A ) , colony level waggle dancing decreased by 0 . 5-fold ( treatment: F2 , 93 = 12 . 55 , p < 0 . 0001 ) . There was a significant effect of location ( feeder versus nest attacks: F2 , 190 = 347 . 68 , p < 0 . 0001 ) and the interaction treatment*location ( F4 , 190 = 11 . 06 , p < 0 . 0001 , 9 . 3% colony effect ) because most dancing occurred on the dance floor ( Z1 ) . Large and small hornet attacks equally reduced waggle dancing ( Tukey’s HSD test , p > 0 . 05 ) . Attacks upon foragers increased colony-level stop signal production ( treatment: F2 , 93 = 83 . 73 , p < 0 . 0001 ) . Stop signaling occurred primarily on the dance floor ( Z1 ) , and , thus , there was a significant effect of location ( F2 , 190 = 226 . 38 , p < 0 . 0001 ) and the interaction treatment*location ( F4 , 190 = 17 . 67 , p < 0 . 0001 , <1% colony effect ) . Attacks by the large hornet upon foragers elicited 7 . 7-fold more stop signals ( SS large hornet forager ) than the control treatment and 2 . 3-fold more stop signals than those elicited by the small hornet ( SS small hornet forager , Tukey’s HSD test , p < 0 . 05 , Fig 3A ) . Colony attacks by hornets elicited similar changes ( Fig 3B ) . Most waggle dancing occurred on the dance floor ( significant location effect: F2 , 158 = 102 . 49 , p < 0 . 0001 ) . Nest attacks by the large hornet decreased colony waggle dancing by 0 . 4-fold as compared to the control treatment ( Tukey’s HSD test , p < 0 . 05 ) , but the control and small hornet treatments were not significantly different ( Tukey’s HSD test , not significant [NS] ) . There was no significant effect of treatment , but the nearly significant interaction treatment*location ( F4 , 158 = 2 . 25 , p = 0 . 07 , 0 . 5% colony effect ) reflected the Tukey’s HSD test results . Interestingly , colony attacks also elicited stop signaling ( treatment: F2 , 77 . = 156 . 63 , p < 0 . 0001 , Fig 3B ) . Attacks by both hornets upon the nest increased stop signaling by 111-fold as compared to the control no-attack phase . Overall , attacks by the large hornet elicited 2 . 4-fold more stop signals than the small hornet , and 20 . 2-fold more signals than the control treatment ( Tukey’s HSD test , p < 0 . 05 ) . There was a significant effect of location ( F2 , 158 = 19 . 98 , p < 0 . 0001 ) and the interaction treatment*location ( F4 , 158 = 15 . 22 , p < 0 . 0001 , <1% colony effect ) . Stop signals elicited by the small hornet attacking the nest ( SS small hornet nest ) occurred mainly in the nest . Stop signals elicited by the large hornet attacking the nest ( SS large hornet nest ) covered a wider range and occurred on the dance floor and in the nest . The stop signals produced by A . cerana inhibited waggle dancing ( Fig 4 ) . We first examined bees waggle dancing for natural food sources that received stop signals elicited by unknown natural causes ( SS naturally elicited ) : 42% of these waggle dances ended with a stop signal in the last two deciles of the dance ( significantly different from a uniform distribution , χ92 = 33 , p = 0 . 0001 ) . We then conducted a bee-produced stop signal playback experiment . We trained bees to sucrose feeders and tested the effect of stop signals on waggle dancers who were not exposed to the predator . These waggle dancers were not attacked: they were recruiting for a nearby , same-scented feeder and , therefore received stop signals [18] . Such waggle dancers also stopped dancing after receiving stop signals elicited by small hornet ( χ92 = 71 . 56 , p << 0 . 0001SB ) or large hornet ( χ92 = 276 . 40 , p << 0 . 0001SB ) attacks . Crucially , stop signals elicited by the large hornet ( SS large hornet forager ) were significantly more inhibitory than those elicited by the small hornet ( SS small hornet forager , χ92 = 90 . 36 , p << 0 . 0001SB ) . The skew , a measure of stop signal effectiveness , was 5-fold greater in magnitude for SS large hornet forager as compared to SS small hornet forager ( Fig 4 ) . The stronger inhibitory effect of SS large hornet forager as compared to SS small hornet forager corresponded to significant differences in signal fundamental frequency ( Fig 5A ) . The vibrational fundamental frequency of stop signals was 47 Hz higher for the large as compared to the small hornet ( attacker effect: F2 , 279 = 17 . 83 , p < 0 . 0001; experiment and attacker*experiment interaction NS , 21% colony effect ) . Hornet attacks upon the nest elicited 1 . 4-fold longer stop signals than hornet attacks on bees at a feeder ( experiment effect: F1 , 279 = 35 . 28 , p < 0 . 0001 , Fig 5B ) . However , stop signal durations were not significantly influenced by predator type ( attacker effect: F1 , 279 = 3 . 39 , p = 0 . 07 , attacker*experiment interaction NS , 12% colony effect ) . Colony entrance attacks triggered stop signaling by returning foragers and guard bees ( treatment effect: F 2 , 190 = 344 . 19 , p < 0 . 0001 , Fig 6A ) . We marked these bees with colored starch powder so that we could track them inside the nest . None of these bees came into physical contact with the hornet , but could see and smell its presence at the nest entrance . The large hornet elicited 8-fold to 18-fold more stop signals from foragers and guards , respectively , than the small hornet ( Tukey’s HSD test , p < 0 . 05 ) . The small hornet elicited 2 . 4-fold more stop signals per bee from foragers than from guards , leading to a significant effect of worker type ( F 1 , 190 = 6 . 98 , p = 0 . 009 ) . The interaction treatment*worker type was not significant ( F 2 , 190 = 2 . 73 , p = 0 . 07 , <1% colony effect ) . Receivers of these stop signals did not go out to attack the hornet . Instead , they increasingly stayed inside the nest according to the threat level communicated by stop signals ( Fig 6B ) . To avoid disturbing stop signal receivers , we used different colors of starch powder to pre-mark a number of foragers ( bees carrying pollen or nectar ) as they entered the nest and before we presented the treatment ( control , small hornet , or large hornet ) at the nest entrance . All stop signal receivers were inside the nest when the treatment was presented and , therefore , had no direct knowledge of the treatment . Interestingly , all stop signals elicited by nest attack were received by foragers , some of which were waggle dancing inside the nest . Signalers did not target guard bees . SS large hornet nest and SS small hornet nest respectively increased the probability of foragers remaining inside the nest by 4 . 8-fold and 2 . 3-fold as compared to control bees ( treatment effect: F 2 , 13 = 33 . 21 , p < 0 . 0001 , 18% colony effect ) . All treatments resulted in significantly different responses ( Tukey’s HSD test , p < 0 . 05 ) . What triggered heat-balling ? We compared colony responses to three types of targets presented at the nest entrance: clean filter paper , a dead small hornet cleaned of cuticular hydrocarbon odors , and a live small hornet . Bees could add sting alarm pheromone during the attack process , but we also tested the effect of initially presenting alarm pheromone ( resulting in six treatment types ) . There was a significant effect of treatment on bee approaches to the hornet ( F 5 , 172 = 361 . 88 , p < 0 . 0001 ) such that the live hornet with alarm pheromone elicited a 5-fold to 166-fold greater response than any other treatment ( Tukey’s HSD test , p < 0 . 05 , Fig 7A ) . There was no significant colony effect on approaches ( F 2 , 172 = 0 . 92 , p = 0 . 40 ) . Similarly , there was a significant effect of treatment on bee heat-balling of the hornet ( F 5 , 10 = 868 . 00 , p < 0 . 0001 , Fig 7B ) . Bees would heat-ball a live hornet ( but no other targets ) regardless of alarm pheromone presence . However , alarm pheromone strongly increased heat-balling of live hornets by 7 . 7-fold . There was no significant effect of colony identity on heat-balling ( F 2 , 10 = 1 , p = 0 . 40 ) . In all cases , the dead hornet cleaned of odors elicited the same responses as the clean filter paper ( Fig 7 , Tukey’s HSD tests , p < 0 . 05 ) . Thus , hornet odor and honey bee sting alarm pheromone were the main attack triggers , with alarm pheromone eliciting the strongest effect .
Graded individual alarm signals are well documented in vertebrates [3–5 , 35] but were not previously known in social insects . Apis cerana stop signals varied in two ways that reflected both predator threat and attack context ( Fig 5 ) . SS large hornet consistently elicited a stronger inhibitory response than SS small hornet , whether in the context of nest or forager attacks . Large hornets elicited stop signals with a significantly higher fundamental frequency than small hornets in the context of forager attacks and nest attacks . Given how animal alarm systems commonly function [7 , 8] , this frequency difference most likely reflects predator threat level , not predator identity . These higher frequency signals had a significantly stronger inhibitory effect on waggle dancing than the lower frequency signals ( Fig 4 ) , even though the waggle dancers in this bee-produced playback experiment did not encounter any predators at their feeder . In A . mellifera , bees are more sensitive ( have a lower freezing response threshold ) to higher-frequency stop signals [12] . Such increased sensitivity in A . cerana could explain the greater inhibitory effect of the higher frequency signals elicited by the larger hornet . Stop signal pulse duration encoded attack context . Nest attacks resulted in significantly longer signals than forager attacks ( Fig 5 ) , and these longer signals were more effective at inhibiting nest departures ( Fig 6B ) . In this experiment , signal receivers did not directly experience the predator because they were inside the nest . We cannot exclude the possibility that stop signal receivers smelled predator or honey bee alarm pheromone odors that may have entered the nest . However , the increased reluctance of receivers to leave the safety of the nest after receiving a SS large hornet nest as compared to a SS small hornet nest matches the increased signaling of senders , who penetrated deeper into the colony and produced more signals for the bigger threat , the large hornet ( Fig 3B ) . We originally hypothesized that SS nest would activate defenders to heat-ball the hornet . This did not occur ( Fig 6B ) . Instead , defenders were activated by honey bee sting alarm pheromone and the presence and smell of a live hornet ( Fig 7 ) . These data provide the first detailed experimental evidence for the role of bee alarm pheromone in heat-balling . The movements of the live hornet could have contributed to initial bee attraction , but once heat-balling is initiated , hornet motions are likely not detectable within the mass of buzzing bees ( Fig 1B ) . Signals can evolve to serve new purposes and acquire new functions . We suggest that this has happened with stop signals in A . cerana . Both A . cerana and A . mellifera share stop signals that provoke the same proximate response—freezing of the receiver [12 , 19]—and have similar frequencies and durations . A . mellifera stop signals have an average fundamental frequency of 329 Hz and a mean duration of 170 ms [22] , whereas A . cerana stop signals have somewhat higher mean fundamental frequencies of 502 and 550 Hz ( small and large hornets , respectively ) and durations of 178 and 258 ms resulting from forager and nest attacks , respectively ( Fig 1 ) . Towne [36] similarly reported that A . cerana stop signals have higher fundamental frequency ( 445 Hz , but within the range of what we measured ) than those of A . mellifera . In both species , attacked foragers produce stop signals that inhibit waggle dancing [19 , 20] . However , A . cerana has formidable nest enemies—different species of giant Asian hornets—and has evolved stop signals that allow the colony to respond appropriately: inhibit recruitment for dangerous food ( SS forager ) or prevent departure from the safe nest when a predator is outside ( SS nest ) . Moreover , SS small hornet and SS large hornet communicated graded levels of danger and elicited appropriate receiver responses . Such sophisticated alarm signals , previously only known in vertebrates , are therefore also used by insects .
For all experiments , we used three A . cerana cerana colonies housed in observation hives in a room at Yunnan Agricultural University , Kunming , China from July to December 2012 ( Experiment 1 ) and August to November 2014 and 2015 ( all other experiments ) . Colonies 1 , 2 , and 3 each contained approximately 5 , 500 bees as determined by Liebefelder photographic estimation [37] during these time periods . The observation hives ( 55 . 4 x 17 x 64 cm ) each contained two combs ( 43 . 5 x 23 cm ) connected by a 25 cm long tube with a 2 . 2 cm inner diameter through the wall to the outside . Upon entering the nest , all bees were shunted by a wood and beeswax divider to one side of the lower comb , so that all forager activity could be viewed ( method of [19] ) . We divided this lower comb into two equal-sized zones: closest to the nest entrance ( Z1 ) and further from the nest entrance ( Z2; Fig 3 ) . Most waggle dances occurred in Z1 [38] . The glass covering one side of the nest was opened , allowing observers to record bee sounds . A door in the observation room allowed bees to escape , but , like , A . mellifera [18] , the A . cerana foragers soon adjusted to the open hive and entered and exited through its entrance tube . To record stop signals , an observer used a shielded microphone ( Movo™ LV1 Lavalier Microphone ) mounted on a thin metal rod connected to a RadioShack™ Mini Amplifier ( model 277–1008 ) and recorded with manual audio gain on a Sony™ HDR-PJ790 camera . The nest monitor reported the number of waggle dance circuits and stop signals to a data recorder . Monitors were extensively trained to recognize the stop signal with a headphone splitter that allowed all experimenters to hear the microphone output . At the end of the first 15 trials , we reviewed the collected videos with each monitor to ensure identification accuracy . The stop signal is relatively easy to recognize because it has a characteristic sound frequency , is delivered by the signaler head-butting the receiver , and causes the receiver to immediately freeze for the duration of the signal . Once trained , monitors could detect the signal based upon its sound alone , as verified by video reviews and the Fourier analysis of identified stop signals . Video data was viewed for additional measurements: the amount of time that foragers waited to unload their collected food ( unloading wait time ) and the total time each forager spent inside the nest per return trip ( nest visit time ) . To record behaviors , the observer sequentially scanned three zones ( Ent , Z1 , and Z2 ) for 1 min each and then repeated the scans so that each zone was scanned four times during a trial . A scan consisted of placing the microphone near , but not blocking , the nest entrance ( Ent ) or holding the microphone approximately 1 cm away from bees on the comb and moving in a zigzag pattern , up and down , for zones Z1 and Z2 . We conducted one trial per day with each colony . Stop signal fundamental frequency and duration were measured with Raven Pro v . 1 . 4 software from Fast Fourier Transform spectrogram analysis . For accuracy , we measured at least two stop signals per bee , but we calculated the average stop signal fundamental frequency and duration per bee to avoid pseudoreplication . We trained 50 bees from each colony to a 30% sucrose solution feeder located 35 m from the focal colony by gently capturing departing foragers at the hive entrance in a vial and releasing them slowly at the training feeder ( Experiment 1A ) . Bees from each colony were trained to a different location to avoid competition for the same food source . The feeder consisted of a 70 ml vial ( 8 cm high ) inverted over a circular plastic disk with 18 feeding grooves through which the sucrose could flow . After being filled with sucrose solution , the vial was inverted on a blue plastic square . Each feeder could accommodate 30 foragers without crowding . We then removed the training feeder and set out three identical feeders spaced 30 cm apart and equidistant from the focal colony . We attached live hornets ( see below ) to wires positioned 10 cm above each feeder . The control feeder had only a clean , bare wire . Foragers then chose between the three feeders , whose positions were randomly swapped every 5 min . We counted the total number of bees landing to feed during a 10 min trial ( only one trial conducted per day , maximum of 11 . 4 bees/min ) . Foragers could recruit without constraint and , thus , forager numbers reflected colony allocation decisions . Upon reaching the array , foragers chose individually , and , thus , the number of forager visits reflected both recruitment and individual feeder choices . To determine the danger posed by hornet predation at the colony entrance , we presented hornets ( large or small ) at the nest entrance ( Experiment 1B ) . Bees rapidly heat-balled the hornet , and , after 5 min , we placed the entire ball inside a plastic bag and allowed the bees to escape one-by-one from a small opening . We counted the number of escaping bees and the number of dead bees that remained in the bag . We used each hornet only once . To estimate the minimum surface area that heat-balling bees would need to cover to completely encapsulate a hornet , we calculated the surface area of the smallest ellipsoid into which the hornet would fit . We used the different hornets to attack foragers trained to a feeder filled with 2 . 5 M sucrose solution ( 65% sucrose w/w ) and placed about 50 m away from the nest . To facilitate training , we added 3 μl of citral ( Jinjingle Inc , Shanghai , China ) to a circular piece of filter paper placed under the feeder [14] . We recorded the responses of individually tracked bees inside the nest ( individual responses: Experiment 2A; colony-level responses: Experiment 2B ) . To allow accurate data recording , individual and colony-level responses were measured in separate trials . We marked all trained foragers with numbered tags ( Opalich Zeichenplättchen ) attached to their thoraces with resin to identify them and verify their colony origin . We trained approximately 20 foragers , resulting in approximately ten bees foraging at any given time without crowding on the feeder . We removed additional foragers with an aspirator to maintain a consistent level of visitation . Other bees were allowed to feed and return to the colony . There was no fighting between bees from different colonies ( a trigger for stop signals [18] ) because all bees were marked , and we immediately captured any bees that came from the wrong colony . To avoid depleting the colony of foragers , we released all captured bees at the end of all trials on each day . We trained a new set of bees for each trial . To test the effect of predator attacks on stop signaling , we captured foraging V . velutina ( small ) and V . mandarinia ( large ) hornets near our field site with an insect net and tethered a single hornet to the end of a 1 m long wood rod with a stiff wire wrapped between the hornet’s thorax and abdomen . The control treatment consisted of an identical rod with a similar wire , but with no hornet , held 1 cm away from a foraging bee . We used a different hornet for each trial . Hornet attack consisted of a hornet leg or mandible making contact with a bee . This contact typically lasted for a fraction of a second because the bees immediately fled . After each trial , we washed the rod with laboratory detergent followed by 100% ethanol to remove potential odors and let it dry in the sun for the remainder of the day . After use , hornets were returned to a holding cage and not reused for several days . We used each hornet approximately twice over all trials . To determine individual responses ( Experiment 2A ) , we recorded the within-nest behavior of focal foragers on return trips before and after attack . To determine the colony-level response ( Experiment 2B ) , a tethered hornet attacked each of five foragers visiting the feeder . Each bee was attacked for approximately 2 s over a 1 min interval . If a bee flew away after a hornet attack , we waited for it to return before attacking it again . This was repeated for a trial duration of 12 min . The control treatment consisted of the experimenter replicating the motions of a clean rod close to , but not touching , foragers . A V . velutina hornet typically attacks an A . cerana colony by hovering close to the nest entrance [30] . To simulate this , the experimenter held the tethered hornet close to , but not touching , bees entering and exiting the nest . In a preliminary experiment using only the small hornet , V . velutina , we used two different controls . In the “rod-only control , ” we tested the effect of the rod apparatus: the experimenter attached one end of a clean rod with a wire but no hornet on a tripod 10 cm away from the colony entrance . In the “no-rod control , ” we simply measured nest behaviors in the absence of any rod at the entrance . The presence of the live hornet clearly disturbed the colony , as expected . Thus , each set of trials began with the no-rod control , followed by the rod-only control , and ended with the hornet treatment . We conducted ten trials with each colony for a total of 30 trials . The results of this preliminary experiment with two control treatments were essentially the same as the results of the full experiment with both large and small hornets ( Fig 3 ) . Small hornet attacks increased stop signaling ( treatment: F2 , 85 = 28 . 19 , p < 0 . 0001 ) , and more stop signals were produced in the nest entrance ( location: F2 , 174 = 28 . 99 , p < 0 . 0001 ) , leading to a significant interaction between treatment*location ( interaction: F4 , 174 = 18 . 97 , p < 0 . 0001 ) . Colony accounted for 8 . 2% of model variance . Just as in our final experiment , small hornet attacks at the colony entrance had no effect on colony waggle dancing ( NS ) . In addition , there were no significant differences in waggle dancing or stop signaling elicited by our no-rod or rod-only controls . In our full experiment with large and small hornets , we therefore used just the rod-only control , which we hereafter call the “control . ” In this full experiment , we began each trial with the rod-only control followed by attacks using the small hornet or large hornet . With each colony , we conducted one trial in the morning and one trial in the afternoon , randomly alternating the hornet species used in each trial . Cleaning and hornet usage were the same as in Experiment 2 . We randomly selected waggle dancers recruiting for natural resources ( pollen or nectar ) and recorded the complete visits of these foragers inside the nest , beginning each recording when a bee entered the nest and ending it when it left . We noted the dance circuits at which they received stop signals , since the dance circuit is the natural unit of replication and will vary in duration for food sources at different distances from the nest [14] . In A . mellifera [18] and in A . cerana ( based upon our results ) , stop signalers will target waggle dancers that visit a feeder that smells identical to the one that the stop signaler is visiting . We therefore trained a separate group of marked foragers to a different feeder ( see training methods in Experiment 2 ) that was never attacked by hornets and tested the response of these waggle dancers to stop signals elicited by large or small hornets . Randomly selected marked foragers were attacked with a hornet , while other marked foragers ( non-attacked ) were trained to a feeder located 3 m away so they would not have direct experience of attacks . Both feeders had the same citral scent so that stop signalers would target the waggle dancers from the non-attacked feeder [18] . When foragers from the non-attacked feeder returned to waggle dance , they received stop signals from attacked bees . To determine which types of workers produce SS nest ( Experiment 6 ) , we presented treatments ( large hornet , small hornet , or control ) at the nest entrance . Hornets were slowly waved in front of the nest entrance but did not contact any bees . Guard bees and forager bees were dusted on the thorax with colored cornstarch dust ( multiple colors , Henan Ruoya Company , China ) . We used colored cornstarch because it could be quickly applied , did not disturb the bees , and was clearly visible over the observation period . Guard bees were defined as bees that stayed at the colony entrance . Foragers were bees that brought back pollen or nectar into the nest . We recorded how many stop signals each bee produced over 5 min and then removed the bee to avoid scoring the same one twice . To determine the effects of SS nest upon signal receivers ( Experiment 7 ) , we marked equal numbers of guard bees and returning foragers with colored cornstarch as in Experiment 6 , but without a treatment ( large hornet , small hornet , or control ) at the nest entrance . Marked bees , therefore , had no experience of a hornet outside the nest . Once bees were marked , we separately presented the treatments at the nest entrance . We then scanned each marked bee with the microphone to determine if it received a stop signal . Once a bee received a stop signal , we tracked it for 5 min to see if it stayed inside or exited the nest ( as verified by a monitor standing outside the nest entrance ) , and then removed it to avoid potential pseudoreplication . We chose 5 min because our preliminary analysis showed that about 85% of marked bees would move toward the nest exit , in the absence of hornet attack , within 5 min . At the end of each trial , we removed the marked bees to avoid scoring the same bee in a subsequent trial . We tested the effects of odor and predator presence upon heat-balling of V . velutina . There were three target types ( clean 5 cm diameter filter paper , de-scented hornet , and live hornet ) . To half of these targets , we initially added a one-bee equivalent of A . cerana sting alarm pheromone from the focal colony , resulting in six different treatments . Each trial consisted of the successive presentation of each these six treatments , in random order . De-scented hornets were dead hornets that we repeatedly rinsed with dichloromethane to remove their cuticular odors and then thoroughly dried before presentation . This de-scenting procedure was effective because bees treated de-scented hornets and clean filter paper identically ( Fig 7 ) . We used a different hornet per trial . The monitor then counted the number of bees that inspected the treatment ( approached but did not land on the hornet or heat-ball ) . If a ball formed , this was scored as “yes . ” We used only V . velutina because V . mandarinia nest attacks resulted in significantly higher mortality ( Fig 1B ) , and we did not wish to excessively weaken the colonies . For Experiments 1 and 8 , we used a Univariate Repeated-Measures Analysis of Variance ( ANOVA ) with colony as the repeated measure , because we were determining colony responses to hornets at the feeder or at the nest entrance . To analyze individual responses in Experiment 2 , we compared the behaviors of each forager before and after it was attacked and , therefore , used Repeated-Measures Analysis of Variance ( ANOVA , REML algorithm ) with bee identity ( the repeated measure ) , treatment ( large or small hornet ) , and phase ( not attacked or attacked ) . We initially included the trial time of day but removed it because there was no significant effect . To analyze the effect of hornet attacks upon colony waggle dancing and stop signaling ( Experiments 2 and 3 ) , we used Repeated-Measures Analysis of Variance ( ANOVA , REML algorithm ) with trial ( the repeated-measure ) and treatment ( control , small hornet , or large hornet ) . For Experiments 4 and 5 , we divided waggle dancing into deciles to facilitate comparing dances with different numbers of waggle circuits . We used chi-square tests to compare the distribution of stop signals received by waggle dancers with the null expectation of a uniform distribution ( method of [20] ) . If stop signals cause bees to stop waggle dancing , stop signals should occur more often near the end of dances . We applied the Sequential Bonferonni correction ( k = 2 , SB indicates significance ) because we tested each distribution separately and then tested if they differed from each other . For Experiment 6 , we used ANOVA and tested the effects of treatment ( control , small hornet , or large hornet ) and worker type ( forager or guard ) . In Experiment 7 , we used ANOVA and tested the effect of treatment ( control , SS small hornet nest , and SS large hornet nest ) . We also used ANOVA to determine the effect of predator type ( large or small hornet ) and attack context ( feeder or nest attacks ) upon the average stop signal fundamental frequency and duration per bee . We log-transformed the number of waggle circuits , number of stop signals , unloading wait time , unloading time , and nest visit time to meet parametric assumptions . We used Tukey’s HSD test to compare between treatments . Colony was a random effect in all models unless otherwise specified ( Experiments 1 and 8 ) . All other effects were fixed . Nonsignificant results ( p > 0 . 05 ) are reported as NS , unless the p-values are close to 0 . 05 . Data is deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . cf426 [39] .
|
Asian honey bees are attacked by formidable predators , giant hornets , at food sources and at their nests . We show that one species of honey bee , Apis cerana , has evolved an alarm signal , the stop signal , which warns nestmates of this danger . The stop signal consists of a brief vibrational pulse that encodes information about the danger level in signal frequency and the danger context in signal duration . Each danger level and danger context results in a distinctive signal . Receivers of these signals respond appropriately , stopping recruitment for the dangerous food location or remaining inside the safety of the nest , according to the encoded danger level and context . This is the first known example of such a complex alarm signal in an insect and demonstrates a new level of sophistication in bee communication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"acoustics",
"waggle-dancing",
"classical",
"mechanics",
"honey",
"bees",
"vibration",
"animals",
"animal",
"signaling",
"and",
"communication",
"signal",
"inhibition",
"animal",
"behavior",
"zoology",
"bees",
"hymenoptera",
"behavior",
"alarm",
"pheromones",
"insects",
"arthropoda",
"physics",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"pheromones",
"physical",
"sciences",
"cell",
"signaling",
"organisms",
"acoustic",
"signals"
] |
2016
|
Honey Bee Inhibitory Signaling Is Tuned to Threat Severity and Can Act as a Colony Alarm Signal
|
There is a considerable contrast between the various functions assigned to Broca's region and its relatively simple subdivision into two cytoarchitectonic areas ( 44 and 45 ) . Since the regional distribution of transmitter receptors in the cerebral cortex has been proven a powerful indicator of functional diversity , the subdivision of Broca's region was analyzed here using a multireceptor approach . The distribution patterns of six receptor types using in vitro receptor autoradiography revealed previously unknown areas: a ventral precentral transitional cortex 6r1 , dorsal and ventral areas 44d and 44v , anterior and posterior areas 45a and 45p , and areas op8 and op9 in the frontal operculum . A significant lateralization of receptors was demonstrated with respect to the cholinergic M2 receptor , particularly in area 44v+d . We propose a new concept of the anterior language region , which elucidates the relation between premotor cortex , prefrontal cortex , and Broca's region . It offers human brain homologues to the recently described subdivision of area 45 , and the segregation of the ventral premotor cortex in macaque brains . The results provide a novel structural basis of the organization of language regions in the brain .
For more than a century , Broca's region in the posterior part of the inferior frontal gyrus has been considered essential for speech production [1] . Effortful , telegraphic speech , impairment in articulation and melodic line , semantic and phonemic paraphasias are some of the symptoms associated with lesions of this region and subsequent Broca's aphasia [2] , [3] . Mohr et al . [4] , however , showed that an infarction limited to Broca's region does not cause chronic speech production deficits , and thus , differs from the clinical characteristics in Broca aphasia . They concluded that Broca's aphasia is observed after damage that extends beyond Broca's region . Broca's pioneering study illustrates on the one hand the power of the clinico-anatomical approach , i . e . , relating language functions to a brain region , but also demonstrates its limitations . Consequently , the anatomical correlates of Broca's region cannot be identified by lesion studies alone . According to Brodmann's map [5] , the posterior part of the inferior frontal gyrus represents Broca's speech region . Brodmann's areas 44 and 45 at the opercular and triangular parts of the inferior frontal gyrus are its putative cytoarchitectonic correlates [6] , [7] . Neighboring areas include premotor area 6 at the ventral precentral gyrus , dorso-lateral prefrontal areas 9 and 46 , area 47 at the orbital part of the inferior frontal gyrus , and the anterior insula ( Figure 1 ) . Brodmann's map became a widely distributed anatomical reference for the interpretation of functional imaging studies although it represents only a schematic 2-D sketch of a putative “typical” human brain; i . e . , it considers neither intersubject variability in brain anatomy nor interhemispheric asymmetries . In contrast to the rather simple parcellation of the inferior frontal lobe shown in Brodmann's map , recent functional imaging studies suggest a complex segregation of Broca's region and neighboring areas of the inferior frontal cortex [8]–[16] . The whole region is involved in various aspects of language including phonological and semantic processing , action execution and observation , as well as music execution and listening ( for an overview see e . g . , [17]–[20] ) . A meta-analysis suggested that the opercular part ( area 44 ) is particularly involved in syntactic processing [21] . However , activation during processing of syntactically complex sentences was also assigned to area 45 ( triangular part ) in studies using semantic plausibility judgment tasks or sentence picture-matching tasks [22] , [23] . Other studies showed activation in area 44 in production [10] and comprehension [11] , [12] . A recent study crossing the factors of semantics and syntax demonstrated that area 44 and more anterior areas ( 45/47 ) were active during sentence comprehension; area 44 carried the main effect of syntactic complexity independent of semantic aspects , whereas semantic relatedness , as well as its interaction with syntax , was located more anteriorly [24] . In addition , the deep frontal operculum was shown to be segregated from the inferior frontal gyrus during processing of syntactic sequences [25] . Finally , activations during motor tasks were also observed near Broca's region , e . g . , during imagery of a motion task [14] . In many cases , the Brodmann map does not enable a localization of functional clusters of activations , in particular when they are found buried in the sulci , where architectonic borders have not been mapped . The localization of activation clusters using 3-D probabilistic cytoarchitectonic maps of areas 44 and 45 [26] , and the adjoining motor areas [27] , demonstrated that some of the clusters did not only overlap with area 44 , but with the neighboring Brodmann area 6 [14] . A frequent finding in neuroimaging is a functional activation spot covering the adjoining border regions of two or more Brodmann areas , which cannot be assigned unequivocally to a cytoarchitectonic area . This situation may be caused by methodical problems of generating functional activation maps ( e . g . , spatial normalization to a template , smoothing , mislocalization of the BOLD signal due to venous flow ) or by biological reasons ( e . g . , intersubject variability ) . Beside these arguments , it must also be asked whether Brodmann's map adequately represents the cytoarchitectonic segregation of this region , or whether uncharted cortical areas lead to the observed mismatch between functional data and cytoarchitecture as provided by Brodmann's map . This line of argument is further supported by architectonic studies in the macaque brain . Recently , a new map of the ventral motor-prefrontal transitional region of the macaque cortex has been proposed; it showed that area F5 consists of three subareas: F5c , F5p , and F5a [28] , [29] . Area F5 plays a major role in the mirror neuron system and has been interpreted as a putative correlate of human area 44 [30] , whereas other authors disagreed [31] , [32] . The complex segregation of the macaque ventral frontal cortex ( and area F5 in particular ) as compared to the rather simple subdivision of the human cortex provides further arguments to question Brodmann's parcellation . Quantitative receptor autoradiography , a method that demonstrates the inhomogeneous regional and laminar distribution patterns of neurotransmitter receptor binding sites in the brain [33]–[35] has been proven to be a powerful mapping tool [34] , [36]–[38] . The quantitative analysis of the density of multiple receptors in each cortical area highlights the regionally specific balance between different receptor types , and the differences between cortical areas . It reveals a functionally relevant parcellation , since receptors play a crucial role in neurotransmission [34] . Our aim was , therefore , to establish a receptor-based architectonic parcellation of the posterior inferior frontal cortex with focus on Broca's region , its right hemispheric homologue , and the adjoining areas on the frontal operculum , as well as the ventral premotor cortex . We studied the distribution patterns of six different receptor binding sites of four neurotransmitter systems: glutamatergic AMPA and kainate receptors , GABAergic GABAA receptors , cholinergic muscarinic M1 and M2 receptors , and noradrenergic α1 receptors in autoradiographs of eight human brains ( Table 1 ) . Neighboring sections were stained for cell bodies in order to identify the cytoarchitecture in this region . Observer-independent receptor and cytoarchitectonic mapping methods [34] combined with multivariate statistics were applied to analyze the similarity and dissimilarity of receptor patterns between the cortical areas . As a result , three previously unknown areas and a further segregation of the classical Broca areas 44 and 45 were found . The study leads to a new organizational concept of the cortical areas in Broca's region . It demonstrates that motor cortex , Broca's region , and prefrontal areas differ in their regionally specific receptor expression patterns , and thus in their signal processing properties .
Both opercular areas op8 and op9 were dysgranular , i . e . , they showed a faint but recognizable layer IV ( Figure 2 ) . In this respect , they were similar to area 44 [26] , but different from area 45 , which has a well-developed inner granular layer ( i . e . , granular type of a cortical area ) . Sporadically , large pyramidal cells were found in layer III of area op8; they were smaller , however , than those of area 44 . The columnar and laminar arrangement was less regular in area op8 than in area 44 . Compared to the dorsally adjoining area 45 , op9 showed a higher cell density , and a less regular cellular distribution . Layer III of area op9 contained pyramidal cells that were smaller and less frequent than those in area 45 . In contrast to the neighboring , purely agranular ventral area 6 , area 6r1 was almost dysgranular; it displayed a subtle layer IV ( Figure 2 ) . In comparison with rostrally adjacent , typical dysgranular area 44 , layer IV of area 6r1 was even thinner and not continuous . Large pyramidal cells in deep layer III were found that were similar to those of areas 6 and 44 . Similar to area 6 , the laminar differentiation of area 6r1 was weak , i . e . , all cortical layers from layer II to VI showed an approximately similar cell packing density . Receptor architectonic borders were identified by differences in density and lamination patterns of the receptor binding sites using an observer-independent method ( Figure 3; Table 2 ) [39] . Area 44 was divided by receptorarchitectonic differences into two areas—a more dorsal 44d and a ventral 44v . Additionally , area 44v appears more posterior than 44d , and 44d reached out to more anterior levels than 44v . Most pronounced differences between both areas were found in muscarinic M2 , AMPA , and α1 ( Figures 4b , 4c , 7 and 8 ) receptors; the remaining receptors and the cytoarchitecture did not clearly separate these two areas ( Figures 7 and 8 ) . The posterior border of area 44v with caudally adjacent area 6r1 was particularly well delineated by kainate ( Figure 3b ) , GABAA , and α1 receptors . The supragranular layers of area 44v have considerably higher densities of glutamatergic AMPA and GABAergic GABAA receptors compared to those of the adjacent area op8 ( Figure 4c and 4d ) . The borders between areas 44v and op8 were found at precisely the same localization in all receptor types indicative of this border ( Figure 4b–4d ) . The dorsally adjacent area of the inferior frontal junction region ( ifj1 ) had lower receptor densities of M2 , AMPA , and GABAA receptors than area 44d ( Figure 4b–4d ) . The border between area 45 and area 44 was detected by all receptors . The receptor densities revealed a subdivision of area 45 into an anterior ( area 45a ) and a posterior ( area 45p ) part , indicated by a lower density of M1 and AMPA receptors ( Figure 5c and 5e ) in the supragranular layers of 45p compared to 45a . Furthermore , the receptor density of the noradrenergic α1 receptor in 45p was lower than in 45a ( Figure 8 ) . The laminar distribution pattern in area 45 was similar to that of area 44 ( Figure 5 ) , but lower mean ( averaged over all cortical layers ) densities of the α1 ( Figure 8 ) , AMPA , and M1 ( Figure 5 ) receptors clearly separated 45p from 44d . 45p had a higher concentration of M2 , kainate , and α1 receptors than the dorso-rostral neighboring area ifs1 ( Figure 4f–4h ) . The ventral border of area 45p with area op9 was indicated by higher M2 and kainate receptor densities ( Figure 4f and 4g ) . 45a had higher M1 , kainate , AMPA ( Figure 5c–5e ) , and α1 receptor densities in the supragranular layers than the rostrally adjacent prefrontal cortex . The border between area 6r1 and area 6v1 was revealed by higher M2 ( Figure 6 ) and lower α1 ( Figure 8 ) receptor densities in 6r1 , whereas the border of area 6r1 with 44v was indicated by changes in kainate ( Figure 3 ) and α1 receptors . Cytoarchitectonic borders coincided with changes in the laminar distribution patterns of several or all receptor binding sites . For example , the border between areas 44 and 45 was identified in all six receptor types ( AMPA , kainate , GABAA , M1 , M2 , and α1 ) . However , not all borders could be demonstrated by changes in the laminar distribution patterns of all receptors; e . g . , the border between areas 6r1 and 6v1 was reflected by changes in M2 , α1 , and kainate receptors , but less well by GABAA and M1 receptors . The border between areas 44d and 44v was labeled by α1 ( Figure 8 ) and muscarinic M2 ( Figure 7 ) receptors , but less visible in the autoradiographs of kainate and GABAA receptors ( Figure 7 ) . The topography of areas and their spatial relationship is illustrated in a series of four coronal sections of a complete hemisphere ( Figure 8 ) . The border between ventral area 6 and caudally adjoining area 4 was located in the anterior wall of the central sulcus or the posterior portion of the precentral gyrus . Area 6 always occupied the free surface of the precentral gyrus . In some cases , it reached the lateral fissure . The receptor distribution showed a subdivision of the ventral part of area 6: two new areas , 6v1 and 6v2 , were defined in addition to area 6r1 ( Figure 8 ) . Both areas were agranular , and showed the typical cytoarchitectonic laminar pattern of area 6 as described by Brodmann [5] . They differed , however , in their receptorarchitecture , e . g . , by the noradrenergic receptor ( Figure 8 ) . Dorsally to 6r1 , area 6v1 was found , which differed itself from the more dorsally adjoining premotor area 6v2 by a lower α1 receptor density ( Figure 8 , level 40 ) . Rostrally of areas 6v1 and 6v2 , area 6r1 was located within the precentral sulcus ( Figure 6 ) . Area 44 had common caudal borders with 6r1 , 6v1 , and 6v2 . Medio-ventrally , 6r1 was adjacent to the new opercular area op6 ( Figure 8 , level 40 ) . Area 6r1 separated area 44v from the ventral and more posterior parts of area 6 on the free surface of the brain ( Figure 9 ) . 44v adjoined 6r1 rostrally and covered the free surface of the opercular part of the inferior frontal gyrus . The position of the border between both areas varied in the anterior wall of the inferior precentral sulcus . The dorsal border of 44d was found in the ventral wall of the inferior frontal sulcus; the dorsal neighbors were ifj2 ( at more caudal levels ) and ifj1 ( at more rostral levels ) . The ventral border of 44v with the opercular area op8 was located at varying positions deep in the frontal operculum ( levels 32 and 26 of Figure 8 ) . Area 45 occupied the triangular part of the inferior frontal gyrus anterior to area 44 . The border between areas 44 and 45 ( Figure 5 ) was found either within the ascending branch of the lateral fissure or on the free cortical surface of the inferior frontal gyrus , e . g . , between the diagonal sulcus and the ascending branch as illustrated in Figures 8 and 9 . The ventral border of 45a with the opercular area op9 ( Figures 4f–4h and 8 at level 19 ) was located at varying positions at the entrance to the Sylvian fissure . Areas op8 and op9 were regularly found ventral to 44v and 45a , respectively . Area 47 occupies the orbital part of the inferior frontal gyrus; it reached only the most rostral part of area 45 , and was located rostral to area op9 . Thus , the part of area 47 at the border to area 45 was most likely area 47/12l as described by Öngür et al . [40] . Area 45 bordered dorsally to areas within the inferior frontal sulcus; an example ( area ifs1 ) is shown at level 19 of Figure 8 . Area ifs1 differed in its receptor pattern from dorsally adjacent lateral prefrontal areas , and was restricted to the depths of the inferior frontal sulcus . Each of the areas in the inferior frontal and precentral gyri showed a distinct receptor pattern as defined by six receptor types . A canonical analysis of receptor densities in all brains and hemispheres demonstrated differences and similarities in the receptor distribution pattern , and quantified receptor architectonic differences by multivariate distances ( Figure 10 ) . The hierarchical cluster analysis showed that the prefrontal area 47 was most different from all the other areas , i . e . , areas 4 , 6 , 44 , 45 , 6r1 , op8 , and op9 ( Figure 10 ) . On subsequent levels of the cluster tree , area 4 differed from the remaining areas . In a next step , areas 6 and 6r1 appeared in one cluster , separated from areas op8 , op9 , 44 , and 45 . On the lowest level , a distinction into two subclusters was found: one comprising areas op8 and op9 , and the other areas 44 and 45 . Interhemispheric differences in receptor densities were tested in three steps . First , we tested the left–right difference of all areas and receptors together using a discriminance analysis ( Wilks Lambda ) . The densities differed significantly between the left and the right hemispheres: the overall p-value indicated a significant effect of hemisphere on the receptor density ( p = 0 . 0091 ) . Second , this overall interhemispheric difference ( left over right ) was mainly caused by the cholinergic muscarinic M2 receptors . It showed a left-larger-than-right asymmetry , as demonstrated by a subsequent univariate F-test ( p = 0 . 003; Table 3 ) . Left–right differences of each of the remaining receptors did not reach significance ( p>0 . 05 ) if tested for each receptor type separately ( Table 3 ) . Third , if the areas were studied separately , M2 receptor densities of areas 44 , 45 , 6v1 , and 6r1 were left > right , whereas area 4 showed an inverse pattern ( Figure 11 ) . Among these areas , the left–right difference for area 44 was most pronounced ( p<0 . 05 ) .
The cerebral cortex is subdivided into structurally and functionally distinct cortical areas . Areas 44 and 45 of the anterior speech zone , Broca's region , are supposed to represent the cytoarchitectonic correlates . Homologues of these two areas have been described in nonhuman primates . Comparative studies in macaque brains provided evidence , however , that a simple subdivision of this region into two areas is not sufficient and obscures the highly differentiated organization: ( i ) area 45 is parcellated into an anterior and a posterior part , which differ in their connectivity [41] , [42]; ( ii ) the transitional zone from motor cortex to Broca's region contains areas within F5 , possibly involved in different aspects of motor control and cognitive functions [28] , [29] . Thus , we hypothesized that Broca's region of the human brain shows a more complex segregation than assumed until now . The present study provided a combined analysis of six transmitter receptors and cytoarchitecture in Broca's region and the frontal operculum in order to test this hypothesis . The ventral premotor cortex and neighboring prefrontal areas have also been included in order to achieve a more comprehensive view of the inferior frontal cortex and its segregation from the neighboring motor and prefrontal cortex . The selection of the areas of the present study aimed to consider the relevant regions , and to provide an anatomical correlate of different concepts regarding the functional segregation of the inferior premotor and neighboring Broca region . Activations in the vicinity of areas 44 and 45 have been reported not only in language , but also in motor tasks [13] , [19] , in experiments focusing on the integration of semantic information from speech and gestures [43] , and other tasks requiring cognitive control [44] , [45] . For an overview about the role of motor and premotor cortices in language processing see [46] . A recent study argued that the human action observation—action execution mirror circuit—is formed by the inferior section of the precentral gyrus plus the posterior part of the inferior frontal gyrus ( plus the inferior parietal lobule ) [47] . As a consequence , parts of the ventral area 6 and area 44 would belong to the mirror system . The inferior frontal cortex , including Broca's region and the ventral premotor cortex , has been conceptualized as a region representing complex , systemic dependencies , regardless of modality and use: Fadiga and coauthors have speculated that this capacity evolved from motor and premotor functions associated with action execution and understanding , such as those characterizing the mirror neuron system [20] . Others proposed that the role of this region is associated with complex , hierarchical or hypersequential processing [48] . Morin and Grèzes provided arguments , on the basis of a review of 24 fMRI studies examining activations in areas 4 and 6 , that the ventral precentral gyrus with area 6 , and not area 44 , shares the visual properties of mirror neurons found in area F5 of the macaque brain [32] . The present receptorarchitectonic study resulted in a novel parcellation of the inferior frontal cortex . Three new areas , op8 , op9 , and the ventral precentral transitional area 6r1 , were identified . Their borders were proven by significant changes in the laminar patterns of cyto- and receptorarchitecture using an algorithm-based method for the detection of borders [39] . Both opercular areas , op8 and op9 , were separated from the dorsally adjoining areas 44 and 45 by their receptor distribution pattern . Previous studies have shown that areas of similar functions show similar receptor patterns and differ from those with other properties [34] . The higher the functional similarity between two cortical areas , the more similar are their receptor distribution patterns [35]; similarities in receptor architecture between areas 44 & 45 on the one hand , and areas op8 & op9 on the other , suggest a corresponding functional segregation . Indeed , functional representations of hierarchically and nonhierarchically structured sentences [25] correlate with the clustering based on receptor architecture: Whereas the deep frontal operculum ( where op8 and op9 are located ) was activated during the processing of nonhierarchically and hierarchically structured sequences , areas 44 and 45 were only activated during the processing of hierarchically structured sequences that mimicked the structure of syntactically complex sentences in natural languages [25] . A diffusion-weighted magnetic resonance imaging study revealed a separation of Brodmann area 44 , 45 , and the deep frontal operculum on the basis of differences in their connectivity [49] . The analysis of the receptor distribution patterns using hierarchical clustering supports the notion that areas 44 and 45 are closely related . It disagrees with those concepts , which attributed Broca's region solely to either area 44 [50] or area 45 [51] , or to a cortical assembly combining areas 44 and 45 with area 47 [52] . Area 47 was most distinct from any of the analyzed areas as shown in the cluster analysis , thus suggesting a different functional involvement . The present data , therefore , imply that it is not meaningful to attribute activation clusters obtained in functional imaging studies to a region labeled as “45/47 , ” since these are two independent , structurally and functionally , completely different cortical areas . The newly described area 6r1 showed cyto- and receptorarchitectonic features that places it in between area 44 and area 6 . The area was called 6r1 in order to underline that it is located rostrally from premotor area 6; “1” indicates that this is the first area of a group of areas that we expect to be located rostrally to the precentral area 6; this belt of areas is located at the transition of the motor domain to the prefrontal cortex . Because of the higher microstructural similarity of area 6r1 with the classically described Brodmann area 6 than to 44 , it was labeled as “6r1 . ” When analyzing the neighborhood of area 6r1 it became obvious , that the ventral part of area 6 consists of several areas , not yet described in the human brain . At least two more areas , 6v1 and 6v2 , have been identified in the present study on the basis of receptor and cytoarchitectonic criteria . This finding supports data of a recent study analyzing the connectivity of the premotor cortex in the human brain [53] . Studies of the macaque brain already resulted in detailed parcellation schemes ( for an overview of parcellation schemes see figure 1 in Belmalih et al . [28] ) . However , the topography and the sulcal pattern of the ventral frontal cortex differ considerably between macaque and human brains . There are , on the other hand , also similarities of the present parcellation of the inferior frontal cortex with a parcellation found in a recent study in macaque monkeys [28] . The authors described an area F5a in the inferior arcuate sulcus bordering area 44 . F5a may correspond to area 6r1 not only by its location but also by its cytoarchitectonic features . Even though area F5a is part of the agranular frontal cortex , it shows transitional features displaying granular cells as well as a relatively prominent layer V [28] . Further cytoarchitectonic studies will be necessary to compare the subdivisions of macaque F5 with human 6r1 in detail . If the abilities associated with Broca's region have evolved from premotor functions [54] , area 6r1 may be interpreted as some kind of “transitional” area between the motor cortex and Broca's region . The identification of area 6r1 implies that area 44 does not border the ventral premotor area 6 over its full extent as supposed by other maps [5] , [41] . Future cytoarchitectonic mapping studies would help to understand the extent of the inferior frontal lobe areas and its intersubject variability . New areas were also found in dorsa-caudally adjacent areas of area 44 . Two areas , ifj1 and ifj2 , were distinguished ( Figure 7 ) , which are located immediately rostrally to premotor area 6 . Both were found at the junction of the inferior frontal and the precentral sulcus , and , therefore correspond to the previously described inferior frontal junction region [55]–[57] . In contrast to earlier observations , however , here we identified two new areas instead of one , which had been hypothesized on the basis of functional imaging experiments , for example during task switching [56] , [58] . The functional difference between ifj1 and ifj2 remains to be further elucidated . Additional new neighboring areas ( e . g . , ifs1 ) were located in the depths of the inferior frontal sulcus where , according to Brodmann's map , areas 46 or 9 would be expected ( Figures 4 and 7b at level 19 ) . The present analysis of the complete coronal sections demonstrates that a series of small areas occupies the sulcus . These areas in the inferior frontal sulcus are different by their receptorarchitecture from the dorsally adjacent areas of the dorso-lateral prefrontal cortex , and , therefore , have not been labeled as areas 46 and 9 , but ifs1 , etc . The analysis and mapping of these new areas , again , represents an independent research project , which would exceed the present study . We provided evidence for a further parcellation within area 44 and area 45 . Differences in the laminar receptor distribution patterns of AMPA and M1 receptors argue for a subdivision of area 44 into a ventral and dorsal part extending earlier cytoarchitectonic findings [26] . A dorso-ventral subdivision of area 44 is a putative correlate of functional differentiation within this area as indicated by recent imaging studies: Molnar-Szakacs et al . [59] reported activations in the dorsal part of area 44 during observation and imitation of actions , whereas the ventral part was activated during imitation , but not during observation of actions . The ventral , but not the dorsal part , was activated during the imagery of movement [14] . Finally , an activation in the ventral part of area 44 was found for syntactic processing during language production [10] and comprehension [25] , whereas the dorsal opercular part ( where 44 is found ) was involved in phonological processing [9] . The laminar receptor distribution patterns subdivided area 45 into an anterior and a posterior part on the basis of differences in the density of noradrenergic α1 M1 , AMPA GABAA receptors . The subdivision of area 45 agrees with a recent study comparing the cytoarchitectonic organization in the human and macaque cortex [60]: Petrides and Pandya divided area 45 into a more anterior part ( area 45 A ) and a more posterior part ( area 45 B , located anterior to area 44 ) using the width of layer II as the distinguishing feature ( being narrower in area 45 A than in 45 B ) . This finding was further supported by demonstrating differences in connectivity [41] . The outcome of the present study is a considerably detailed parcellation of Broca's region and the immediately surrounding cortex . Some of the new units described here can be assigned to regions covered by Brodmann areas and defined by his nomenclatural system [5] . In such cases , we keep Brodmann's numbering system and define the new units by Brodmann's number and an additional letter and/or number ( e . g . , 6r1 , 44a , 44p ) . In other cases , new cortical units could not be reliably assigned to a Brodmann area , e . g . , op8 and op9 . Since our new parcellation is based on an observer-independent approach and statistical tests of the significance of regional differences , we will call all cortical units “areas . ” The question , however , of how a cortical unit is defined as “area , ” and what makes it special as compared to a unit called “subarea , ” or an intra-areal specialization , remains . Examples of intra-areal specializations would be somatotopies in sensory and motor areas and ocular dominance columns , i . e . , structures that are regionally specific to a certain degree , but subserve a common function . Currently , the concept of a “subarea” is vaguely defined , and is used inconsistently in the literature . Therefore , we adopt the term “area” throughout the article . A central question to any study devoted to Broca's region is that of lateralization . Several studies have provided evidence that cytoarchitecture [26] , [50] , [61]–[64] , fiber tracts [65] , and macroscopical anatomy of this region are asymmetric [66]–[68] . For overviews see [69] and [70] . These structural asymmetries were interpreted as putative correlates of functional lateralization . The present study revealed significant interhemispheric differences in the receptor concentrations when all six receptor types were taken together . A subsequent analysis was performed in order to identify the receptor type that contributed most to this finding . The cholinergic M2-receptor showed the only significant left–right difference . Interhemispheric differences of receptors in Broca's region have not been reported up to now . In conclusion , the novel parcellation of the ventro-lateral frontal cortex and Broca's region provides a new anatomical basis both for the interpretation of functional imaging studies of language and motor tasks as well as for homologies between human and macaque brains . It will , therefore , contribute to the understanding of the evolution of language . The analysis of the receptor distribution sheds new light on the organizational principles of this region . This direction is a further step from a rigid and exclusively cytoarchitectonic parcellation scheme as introduced by Brodmann 100 years ago [71] towards a multimodal and functionally relevant model of Broca's region and surrounding cortex .
Adult post mortem brains of body donors were removed from the skull within less than 24 h post mortem in accordance with legal requirements ( Table 1 ) . None of the subjects had clinical records of neurological or psychiatric disorders . Six hemispheres were dissected into coronal slabs of approximately 30 mm thickness ( Figure S1 ) . Tissue blocks containing the posterior part of the inferior-frontal cortex were dissected from six hemispheres of three brains and sectioned horizontally . The tissue was frozen and stored at −70°C . Serial sections ( thickness 20 µm ) were prepared at −20°C using a large-scale cryostat microtome . The sections were thaw mounted onto glass slides ( Figure S1 ) . The following receptor binding sites were studied: glutamatergic AMPA and kainate receptors , GABAergic GABAA receptors , cholinergic muscarinic M1 and M2 receptors , and noradrenergic α1 receptors ( Table S1 ) . Alternating brain sections were incubated with the receptor-specific tritiated ligands only , the tritiated ligands , and respective nonradioactive compounds ( for measurement of nonspecific binding ) , or were stained for the visualization of cell bodies [72] . Thus , a group of serial sections at the same sectioning level demonstrates the different receptor types , and the regional cytoarchitecture ( Table S1; for details see Zilles et al . [35] ) . Since nonspecific binding was less than 10% of the total binding in all cases and receptor types , the total binding was accepted as an estimate of the specific binding . The labeled sections were coexposed with plastic standards of known concentrations of radioactivity ( Amersham ) to β-sensitive films . The films were developed after 10–12 wk of exposure depending on the receptor type , and digitized using the KS400 image analyzing system ( Zeiss ) . The grey value distribution in the autoradiographs is nonlinearly correlated [35] with the local concentrations of radioactivity ( Figure S1 ) , which represent the regional and laminar distribution of receptor binding sites . Therefore , the known concentration of radioactivity of the coexposed standards ( Figure S1d , bottom right ) enables the nonlinear transformation of grey values into receptor binding site concentrations in fmol/mg protein ( linearized images ) . For improved visualization of the regionally different receptor concentrations , the linearized images were contrast enhanced , smoothed , and pseudo-color coded in a spectral sequence ( Figure S1f ) . Neighboring sections were stained for cell bodies to demonstrate the cytoarchitecture . Rectangular regions of interest ( ROIs ) containing area 44 and 45 of Broca's region and neighboring areas were defined . Images ( 1 , 376×1 , 036 pixels; spatial resolution 1 . 02 µm per pixel ) of the ROIs were acquired using a microscope equipped with a digital camera ( Axiocam MRm , Zeiss ) and a scanning stage . A high-resolution image of the total ROI was then assembled from the individual tiles employing the KS 400 system ( Zeiss; Figure 3a I ) . Grey level index ( GLI ) images of the ROIs were calculated by adaptive thresholding with a spatial resolution of 16×16 µm . The resulting GLI image ( Figure 3a II ) represents in each pixel the local volume fraction of cell bodies [73] . Borders between cortical areas were identified in the receptor autoradiographs as well as in the cell body–stained sections using an algorithm-based approach and multivariate statistical analysis [39] . Therefore , laminar profiles of the GLI distribution were extracted in the cell body–stained sections using MATLAB-based software ( MATLAB 7 . 2 ) ( Figure 3a III ) . Laminar profiles were also obtained for the binding site densities in the autoradiographs ( Figure 3b III ) . A feature vector was calculated for each profile , which described the shape of each profile , i . e . , the cyto- or receptorarchitecture [39] . Differences in the shape of the profiles were quantified by a multivariate distance measure , the Mahalanobis distance . A subsequent Hotelling's T2 test with Bonferroni correction for multiple comparisons was applied for testing the significance of the distance . Profiles sampled from one and the same cortical area were similar in shape , resulting in small Mahalanobis distances . Profiles sampled from different sides of a cortical border differed in shape and resulted in large distances . To improve the signal-to-noise ratio , distances were calculated not between single profiles , but blocks of ten to 20 adjacent profiles . The position of a significant maximum in the Mahalanobis function was interpreted as a cortical border , if it was found for different block sizes ( Figure 3b VI ) , and if it was reproduced in a similar position in adjacent sections . These criteria allowed the rejection of borders caused by artifacts due to tissue processing , or blood vessels . For each receptor , the density averaged over all layers of a cortical area was calculated in a set of sections/autoradiographs of each hemisphere separately . These mean receptor densities were averaged over all hemispheres resulting in a mean areal density value for each area and receptor type . The density values of all six receptors studied were combined into a receptor feature vector for each area . A hierarchical cluster analysis ( MATLAB 7 . 2 ) was performed in order to analyze receptor architectonic similarities and dissimilarities between the different areas ( Euclidean distance , Ward linking ) . The higher the similarity between two cortical areas , the smaller was the Euclidean distance between their feature vectors . A one-way ANOVA analysis ( Systat 12 ) was performed to test for interhemispheric differences in receptor densities of all areas and receptors together . The factor “hemisphere” had two levels: left and right . Cases with missing values were excluded from the analysis . A post hoc univariate F test was performed in order to identify receptor types that contributed mostly to overall interhemispheric differences . Finally , we tested interhemispheric differences for each cortical area and receptor . The p-level was set to 0 . 05 .
|
Broca's region is involved in many aspects of language processing in the brain . Such detailed functional diversity , however , is in contrast to its classical anatomical subdivision into only two cortical areas . Since the regional distribution of neurotransmitter receptors has been proven to be a powerful indicator of functional segregation , we revised the subdivision of Broca's region by analyzing the distribution of six different receptor types in the human brain . On the basis of these results , we propose a novel map of Broca's and neighboring regions with several , previously unknown areas . Moreover , a significant left-sided interhemispheric asymmetry of receptors was found , mainly for the cholinergic muscarinic M2 type . This asymmetry correlates with the well-known left-sided dominance for language . Finally , we present a model of the molecular organization of the anterior human language region and neighboring prefrontal and motor areas on the basis of similarities in their receptor patterns . This model contributes to our understanding of the relation between motor areas and classical Broca region . Our results are important for future studies of the functional segregation and the role of mirror neurons in the human brain , and are relevant for revealing homologies between human and macaque brains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/cognitive",
"neuroscience",
"radiology",
"and",
"medical",
"imaging/magnetic",
"resonance",
"imaging",
"neuroscience/motor",
"systems",
"neurological",
"disorders/neuroimaging",
"neuroscience/neuronal",
"signaling",
"mechanisms",
"radiology",
"and",
"medical",
"imaging",
"computational",
"biology/systems",
"biology"
] |
2010
|
Broca's Region: Novel Organizational Principles and Multiple Receptor Mapping
|
HTLV-1 infection is endemic in Brazil . About 1 to 2% of the Brazilian population is estimated to be infected , but most infected HTLV-1 individuals do not know about their own infection , which favors the continuity of sexual and vertical virus transmission . In addition , HTLV-1 associated central nervous system diseases and their pathophysiologic mechanisms are not fully understood . This study aimed to evaluate the correlation of spinal cord metabolism , viral and inflammatory profiles with features of neurological presentation in HTLV-1 infected individuals . This is a cross-sectional study of a cohort including 48 HTLV-1 infected individuals clinically classified as asymptomatic-AG ( N = 21 ) , symptomatic-SG ( N = 11 ) and HAM/TSP-HG ( N = 16 ) and a nested case-control study with HTLV-1 infected individuals-HIG ( N = 48 ) and HTLV-1 non infected controls-CG ( N = 30 ) that had their spinal cord analysed by Positron Emission Tomography with 18F-Fluordeoxyglucose ( 18F-FDG PET/CT ) . HTLV-1 infected individuals had 18F-FDG PET/CT results analyzed with clinical and demographic data , proviral load , cytokines and chemokines in the blood and cerebrospinal fluid ( CSF ) . 18F-FDG PET/CT showed hypometabolism in the thoracic spinal cord in HTLV-1 infected individuals . The method had an accuracy of 94 . 4% to identify HAM/TSP . A greater involvement of the thoracic spinal cord was observed , although hypometabolism was also observed in the cervical spinal cord segment in HTLV-1 infected individuals . Individuals with HAM/TSP showed a pro-inflammatory profile in comparison to asymptomatic and symptomatic groups , with a higher level of Interferon-inducible T-cell alpha chemoattractant ( ITAC/CXCL11 ) , IL-6 , IL-12p70 in the plasma; and ITAC , IL-4 , IL-5 , IL-8 ( CXCL8 ) and TNF-alpha in the CSF . Using regression , thoracic spinal cord SUV ( standardized uptake value ) and CSF ITAC level were identified as the HAM/TSP predictors in the multivariate model . 18F-FDG PET/CT imaging showed spinal cord hypometabolism in most HTLV-1 infected individuals , even in the asymptomatic HTLV-1 group . Thoracic spinal cord hypometabolism and CSF-ITAC levels were identified predictors of HAM/TSP . Our findings suggested that in most HTLV-1 infected individuals there was compromise of central nervous system ( CNS ) structures despite of the lack of clinical symptoms . To explain the found hypometabolism , the role of microcirculatory and metabolic factors in the pathogenesis of neurological diseases associated with HTLV-1 infection must be further investigated . It is paramount to evaluate the central nervous function and to compare the performance among HTLV-1 infected individuals considered asymptomatic to the uninfected controls .
The Human T-cell lymphotropic virus ( HTLV-1 ) was associated with tropical spastic paraparesis ( TSP ) and HTLV-1 associated myelopathy ( HAM ) in the eighties [1–2] . Both diseases were recognized as the same neurological disorder in 1988 and called HAM/TSP [3] . Since then , a wide range of inflammatory , neoplastic , and infectious diseases have been associated with HTLV-1 infection [4] . Different neurological impairments have been described in HTLV-1 infection without meeting the criteria defined for HAM/TSP , although enough to compromise labor capacity and social life of infected individuals [5–6] . The combination of specific virus characteristics and the varied elicited individual immune response by the host leads to HAM/TSP in only 3% of infected individuals [7] . Female gender , transmission pathway , high proviral load and proinflammatory immune response are risk factors for the development of neurological disease [8] . The pathophysiological mechanisms of central and peripheral nervous system impairment caused by HTLV-1 are not fully understood . Many years may elapse from the first clinical symptoms to a clear diagnosis of HAM/TSP . There is a total absence of accurate markers to define the established neurological disease associated with HTLV-1 . In order to have a clear diagnosis of HTLV-1-associated myelopathy ( HAM/TSP ) , it is necessary a long follow-up and exclusion of other causes of myelopathy [9–10] . The most common and non-specific finding observed in thoracic spinal cord magnetic resonance imaging ( MRI ) is atrophy of the thoracic spinal cord , which appear several years after disease onset [11] . In a study conducted in our cohort ( HTLV Research Interdisciplinary Group—GIPH ) , visual inspection of spinal cord MRI had low sensitivity for diagnosis of HAM/TSP in early and intermediate phases of the disease [12] . Some authors have reported that magnetic resonance imaging was more sensitive to evidence the presence of atrophy and focal lesions in the spinal cord , which also demonstrated a relationship between the duration of the disease and the degree of spinal cord atrophy [13–15] . HTLV-1 infection is chronic over decades , and may evolve with milder neurological impairment of defined HAM/TSP . The differential diagnosis of HTLV-1 milder neurological impairment is often neglected or confused . In order to evaluate central nervous system involvement , we designed this study to analyze blood and cerebrospinal fluid immunomarkers and thoracic spinal cord 18F-FDG PET/CT . Neuronal activity and function are measured indirectly through metabolism of glucose by 18F-FDG PET/CT [16] . PET has been used for the differential diagnosis of primary , opportunistic and neoplastic CNS lesions in human immunodeficiency virus ( HIV ) carriers . In HIV-infected individuals with cognitive impairment the 18F-FDG PET has shown subcortical hypermetabolism in the basal ganglia , striatum and thalamus , with discretely increased metabolism in studies following the introduction of combined antiretroviral therapy [17] . We hypothesized that in the early stages of HTLV-1 spinal cord impairment , 18F-FDG PET/CT would show a metabolic increase due to inflammation . On the other hand , in advanced phases of HAM/TSP , a degenerative phase with neuronal loss , a lower metabolism would be present and the asymptomatic individuals would have a metabolism close to that of uninfected controls . The data was analyzed using a regression model to determine predictive markers of the HAM/TSP .
Subjects of this study were grouped according to their clinical neurological conditions . Significant statistical difference was not observed between groups with respect to age and follow-up time at GIPH . There was a significant difference regarding gender due to the higher proportion of female in the symptomatic and HAM/TSP group . The degree of neurological impairment was measured using the Expanded Disability Status Scale ( EDSS ) [18] , which showed a significant difference between infected HTLV-1 groups and a strong correlation with the Ambulation Index [19] . Demographic data are summarized in Table 1 . Neurological symptoms and signs were statistically different between HTLV-1 infected groups . Prevalent concomitant clinical diseases , smoking and alcoholism were not statistically different between HTLV-1 infected groups , except sicca syndrome , with 19% prevalence in the HAM/TSP group . Only two HAM/TSP patients were in use of low-dose corticosteroids . The prevalence of depression was investigated in HTLV-1 infected individuals using the Geriatric Depression Scale– 15 items ( GDS -15 ) [20] and may be underestimated , since individuals on psychotropic drug treatment were not invited to participate in this study . Fig 1 shows the percentage of symptoms and neurological signs , systemic diseases and social habits in each of the HTLV-1 infected groups . In the studied group , HAM/TSP patients evolved with slow progression . The disease duration in years ranged from one to 35 years , with a mean of 12 years ( SD 10 ) in the HAM/TSP group and ranged from 6 months to 9 years , with a mean of 3 years ( SD 3 ) in the symptomatic group . A reduction in metabolism in the spinal cord was observed in the 18-F FDG PET/CT images in HTLV-1 infected groups compared to uninfected controls and the degree of hypometabolism correlated with the degree of neurological impairment . Thoracic spinal cord SUV and EDSS score had a negative correlation rs = — . 526 , R2 = 0 . 28 , p < . 001 . Significant statistical difference was not observed between gender with respect to cervical ( p = 0 . 514 ) and thoracic ( p = 0 . 602 ) spinal cord SUV . Thoracic and cervical SUV were significant difference between the four groups , and between the HTLV-1 infected and the control group ( Fig 2 ) . In the multiple comparison between groups , post ROC analysis , there was significant difference between the control group compared to asymptomatic , p = 0 . 041 ( CI 95% , 0 . 01–0 . 65 ) and HAM/TSP , p = 0 . 006 ( CI 95% , 0 . 10–0 . 78 ) in the cervical spinal cord segment and between the control group and all HTLV-1 infected groups: asymptomatic group , p = 0 . 043 ( CI 95% 0 . 005–0 . 415 ) ; symptomatic , p < 0 . 001 ( 95% CI 0 . 232–0 . 740 ) ; and HAM/TSP , p < 0 . 001 ( CI 95% 0 . 273–0 . 719 ) in the thoracic spinal cord segment . The asymptomatic and HAM/TSP groups also showed a significant difference in the thoracic spinal cord SUV , p = 0 . 012 ( CI 95% 0 . 047–0 . 525 ) . Thoracic spinal cord SUV of the symptomatic group was similar to that observed in HAM/TSP group , p = 1 . 00 . Thoracic spinal cord SUV presented accuracy of 94 . 4% , 85 . 7% and 81 . 6% , in the correct classification of groups: HAM/TSP and control ( p < 0 . 001; CI 95% 0 . 89–1 . 00 ) , HAM/TSP and asymptomatic ( p = 0 . 001; CI 95% 0 . 72–0 . 97 ) and HTLV-1 infected and control ( p < 0 . 001 CI 95% 0 . 72–0 . 91 ) , respectively . In the ROC curve , thoracic spinal cord SUV ≤ 1 . 15 discriminated individuals of HAM/TSP group with 75% sensitivity and 100% specificity in relation to the control group and with 76 . 5% sensitivity and 85 . 7% specificity in relation to the asymptomatic group and cervical spinal cord SUV ≤ 1 . 32 discriminated HAM individuals with 68 . 8% sensitivity and 80% specificity in relation a control group . Cervical spinal cord SUV ≤ 1 . 42 discriminated between HTLV-1 infected and control groups , with accuracy of 72 . 2% , 64 , 6% sensitivity , 70% specificity , p = 0 . 001; CI 95% 0 . 60–0 . 84 . Cervical spinal cord SUV was not able to discriminate between the HTLV-1 infected groups . Another point observed in the cervical and thoracic SUV was a progressive reduction in the standard deviation as the neuronal impairment increased , higher in control group and lower in HAM/TSP group . Blood ( p = 0 . 566 ) and CSF ( p = 0 . 721 ) proviral load distribution did not show a gender difference . There was a statistically significant difference in the mean proviral load of blood and CSF among the three HTLV-1 infected groups: 142 ( SD 173; Me 90; N 20 ) asymptomatic; 29 ( SD 42; Me 13; N 11 ) symptomatic; 336 ( SD 301; Me 292; N 14 ) HAM/TSP in the blood and 515 ( SD 1035; Me 0; N 18 ) asymptomatic; 50 ( SD 105; Me 0; N 11 ) symptomatic; 1228 ( SD 1086; Me 946; N12 ) HAM/TSP in the CSF , p < 0 . 001 , copies/103 cells ( Fig 3 ) . Positive correlation between blood and CSF proviral load was observed , rs = . 810 , R2 = 0 . 66 , p < . 001 ( Fig 3 ) . Proviral copies in CSF were not detected in 10/21 asymptomatic ( 55 . 6% ) and 7/11 symptomatic ( 72 . 2% ) , although were detected in all HAM/TSP individuals . In these individuals with proviral load not detectable in the CSF , the proviral load in blood samples was also low: 15 . 7 ± 24 . 3 ( 0–67 ) copies/103 cells in the asymptomatic; 25 . 2 ± 49 . 6 ( 0–146 ) copies/103 cells in the symptomatic . On the other hand , CSF proviral load was higher than blood proviral load in all individuals with detectable CSF proviral load , except in an individual of the HAM/TSP group in which CSF proviral load was 33% lower than blood . In the other subjects with proviral load detectable in the CSF ( N = 22 ) , the ratio between CSF and blood proviral load showed an average increase of 9 ± 19 . 6 ( 1 . 38 to 95 . 64 ) times , median of 3 . 5 times . There was no statistically significant difference between the groups with regard to CSF and blood proviral load ratio ( p = 0 . 670 ) : N = 8 , 4 . 6 ± 3 . 1 ( 1 . 4–9 . 4 ) times , median of 3 . 1 times in the asymptomatic group; N = 3 , 5 . 3 ± 5 . 2 ( 1 . 4–11 . 2 ) times , median of 3 . 2 times in the symptomatic; and N = 11 , 13 . 2 ± 27 . 5 ( 2 . 1–95 . 6 ) times , median of 4 . 2 times in the HAM/TSP group . Blood proviral load ≥ 193 . 5 and CSF proviral load ≥ 399 . 5 discriminated individuals between HAM/TSP and asymptomatic groups , with accuracy of 74 , 1% , 71 , 4% sensitivity , 75% specificity , p = 0 . 018; CI 95% 0 . 573–0 . 909 and accuracy of 81 . 5% , 91 . 7% sensitivity , 72 . 2% specificity , p = 0 . 004; CI 95% 0 . 660–0 . 969 , respectively . The distribution of cytokines in relation to gender did not present significant statistical difference in HAM/TSP group . Plasmatic Fracktal , IFN-gamma , IL-12p70 , IL-13 , IL-17 A , IL-1 beta , IL-2 , IL-21 , IL-6 , IL-7 , TNF-alpha , CSF Fracktal and IL-7 presented a statistically significant difference ( p < 0 . 05 ) in relation to gender in asymptomatic group and plasmatic IL-17A , IL-21 and CSF Fracktal in the symptomatic group . The cytokines that presented significant statistical difference between HTLV-1 infected groups were: ITAC ( r = 0 . 43 ) , IL-12p/70 ( r = 0 . 26 ) , IL- 17 A ( r = 0 . 28 ) in the plasma , and ITAC ( r = 0 . 41 ) , IFN-gamma ( r = 0 . 25 ) , IL-5 ( r = 0 . 33 ) , IL-8 ( r = 0 . 34 ) and TNF-alpha ( r = 0 . 32 ) in the CSF ( Table 2 ) . The cytokines and chemokines that presented significant statistical difference between asymptomatic and HAM/TSP groups were: plasmatic ITAC ( p = 0 . 003 ) , CSF ITAC ( p = 0 . 001 ) and IL-8 ( p = 0 . 010 ) . Symptomatic and HAM/TSP groups showed statistical difference only CSF ITAC ( p = 0 . 003 ) . Asymptomatic and symptomatic groups did not present significant statistical difference in relation to the cytokines and chemokines analyzed . Only plasma ITAC correlated with ITAC in CSF ( N = 45; rs = 0 . 56 , p < 0 . 001 ) . Plasmatic ( N 47 ) and CSF ( N 46 ) ITAC correlated with disease time ( rs 0 . 43; p = 0 . 003 and rs 0 . 38; p = 0 . 009 ) , ambulation index ( rs 0 . 44; p = 0 . 001 and rs 0 . 46; p = 0 . 001 ) , respectively , but not correlated with thoracic SUV . Plasmatic ITAC ( N = 44; U = 132 . 0 , p = 0 . 034 , r = −0 . 32; Me: 10 . 52 and 8 . 85 ) , IL-5 ( N = 44; U = 305 . 5 , p = 0 . 029 , r = 0 . 33; Me: 0 . 37 and 0 . 51 ) and CSF ITAC ( N = 43; U = 930 . 0 , p = 0 . 006 , r = 0 . 41; Me: 71 . 51 and 45 . 87 ) presented significant statistical difference in relation to high or low blood proviral load , respectively . However , when only the asymptomatic group was analyzed , there was no statistically significant difference of the cytokines in relation to high or low blood proviral load . EDSS correlated strongly with: disease time ( N 48; rs 0 . 95; p < 0 . 001 ) ; ambulation index ( N 48; rs 0 . 81; p < 0 . 001 ) ; thoracic SUV ( N 48; rs 0 . 50; p < 0 . 001 ) ; CSF ITAC ( N 46; rs 0 . 45; p 0 . 002 ) and CSF IL-8 ( N 46; rs 0 . 42; p 0 . 003 ) ( Fig 4 ) . A moderate EDSS correlation occurred with: CSF proviral load ( N 41; rs 0 . 40; p 0 . 011 ) ; plasma ITAC ( N 47; rs 0 . 37; p 0 . 012 ) CSF IL-5 ( N 46; rs 0 . 35; p 0 . 019 ) ; CSF IFN-gamma ( N 46; rs 0 . 34; p 0 . 021 ) and plasma IL- 17 A ( N 47; rs -0 . 35; p 0 . 016 ) . In this analysis , only the asymptomatic and HAM/TSP groups were considered , since the subjects of the symptomatic group were considered clinically undetermined . Univariate analysis indicated that female gender , thoracic spinal cord SUV , blood proviral load , plasmatic IL-6 , CSF ITAC , IFN-gamma and IL-8 were predictors to HAM/TSP , when taken as independent risk factors . Only thoracic spinal cord SUV and CSF ITAC fit in the multivariate model , with a correct classification between asymptomatic and HAM/TSP individuals of 81 . 3% , as shown in Table 3 .
In this study we sought to better understand the HTLV-1 clinical manifestations and its correlations with imaging and immune features . We enrolled and classified infected individuals according to their neurological status in asymptomatic , symptomatic and HAM/TSP groups . These groups were statistically different regarding neurological symptoms and signs , EDSS score and gender , but showed no difference regarding age and distribution of systemic diseases . Female gender was more frequent in the symptomatic ( 4 . 5:1 ) and HAM/TSP groups ( 4 . 3:1 ) . This higher proportion of female gender in the symptomatic and HAM/TSP groups is explained by the higher incidence and prevalence of HAM/TSP in females , as observed in other epidemiological studies [21] . For this reason , statistical analyzes of the variables described in this paper were also analyzed in relation to gender , with the purpose of evaluating a possible gender influence , which was not found . First , we discussed two questions . Are there HTLV-1 infected individuals without CNS impairment ? Is HTLV-1 related CNS impairment restricted to the thoracic spinal cord ? Analyzing this same population ( Schütze et al . , 2017 ) , we divided the sample according to the EDSS score ( 0; 1–2; 2 . 5–10 ) and processed each PET brain image using the statistical parametric mapping toolbox . Gray and white brain matter density image were generated for each subject . Analysis was able to classify with the accuracy of 85 . 7% ( p = 0 . 001 ) , 75% ( p = 0 . 024 ) and 85% ( p = 0 . 003 ) groups with EDSS score 0 ( asymptomatic ) , 1–2 . 5 ( intermediate ) and > 2 . 5 ( HAM/TSP ) , respectively , in relation to the control group . Diffuse cerebral hypometabolism was observed in HTLV-1 infected groups , including those with zero EDSS score compared to controls [22] . Other studies have shown that additional neurological manifestations are present in HTLV-1 infection other than HAM/TSP and that other segments of the central nervous system beyond the thoracic spinal cord , such as: brain and cervical spinal cord are altered even in infected HTLV-1 considered asymptomatic . [5–6 , 13–15 , 22] . The results of 18-F-FDG PET/CT associated with clinical data lead us to conclude that in most HTLV-1 infected individuals there is involvement of the evaluated structures of CNS , despite lack of clinical symptoms . The 18F-FDG PET/CT study shown hypometabolism in the cervical and thoracic segments of the spinal cord in all the three groups of HTLV-1 infected individuals including the group considered asymptomatic compared to controls . The 18-F FDG PET/CT was able to show greater involvement of the thoracic spinal cord , with excellent accuracy in the discrimination between HTLV-1 infected individuals ( 81 . 6% ) and HAM/TSP patients ( 94 . 4% ) from control group , as well as between HTLV-1 infected asymptomatic and HAM/TSP groups ( 85 . 7% ) . Currently , the major pathophysiological mechanism considered to explain CNS involvement in HAM/TSP is an immuno-mediated chronic inflammatory process in response to HTLV-1 infection , which damages nearby CNS components [7] . We expected that 18F-FDG PET/CT showed in the initial phase of neurological impairment , prior to the neurodegenerative process , a metabolic increase due to inflammation . However , HTLV-1 infected individuals showed hypometabolism in 18F-FDG PET/CT in comparison to the control group , including HTLV-1 infected individuals with none neurological symptoms . CNS 18F-FDG PET/CT hypometabolism was observed in brain small-vessel and neurodegenerative diseases such as in preclinical stages of Alzheimer’s disease . This hypometabolism finding is compatible with GLUT-1 expression reduction , a metabolic disorder and/or a vascular-neuronal dysfunction coursing with consequent breakdown of the blood-brain barrier and degeneration [23–24] . This hint lead us to rethink the process to explain the lesions of CNS in HTLV-1 infection . The 18F-FDG behaves like glucose in the vascular and interstitial compartments and is transported to the intracellular compartment by passive transport , using glucose transporter proteins ( GLUT ) , especially GLUT-1 , present in most tissues and in the CNS . In the cell , 18-F FDG is phosphorylated and trapped in the intracellular compartment allowing the measurement of tissue metabolism [25] . PET/CT hypometabolism may result from low tissue metabolism , hypoperfusion due to low tissue blood supply , GLUT-1 reduction or blockage of glucose entry to the intracellular environment [23] . Why asymptomatic and symptomatic infected HTLV-1 have spinal cord hypometabolism despite of not presenting neurological impairment on physical examination ? Why the symptomatic group have hypometabolism in a similar degree to the HAM/TSP group ? Multiple mechanisms may be responsible for hypometabolism and we do not know for sure what causes this process in the pre-clinical and advanced stages of HAM/TSP . As already described above , in other pathologies demonstrating 18F-FDG PET/CT hypometabolism , this finding is apparently associated with microvascular changes , loss blood-brain barrier homeostasis and its consequences [23–24] . In addition , pathological and 18F-FDG PET studies have shown that less perfused areas of the central nervous system ( watershed ) are more vulnerable to HTLV-1 infection [26–27] and that GLUT-1 is important in the cell-entry mechanisms of HTLV-1 associated the vascular endothelial growth factor ( VEGF ) , Neuropilin-1 ( NRP-1 ) and heparan sulfate proteoglycans ( HSPG ) [28] . These areas of the central nervous system with lower perfusion may have higher VEGF expression , with higher HSPG and NRP-1 exposure on the endothelial cell surface , allowing HTLV-1 cell-entry [29] . These changes would promote imbalance in angiogenic homeostasis and the rupture of the local blood-brain barrier [30] . The breakdown of the blood-brain barrier allows the passage of infected and uninfected lymphocytes into the central nervous system tissue with subsequent local immune response , inflammation , demyelination and axonal degeneration [30] . However , this is a speculative hypothesis and we know that further studies are still necessary to have a clue to explain the mechanisms for the observed CNS and spinal hypometabolism . High proviral load is a known risk factor for HAM/TSP development [31–32] . The individuals with HAM/TSP seem to have a higher blood and CSF proviral load , but there are not a cut-off point and reproducibility between the studies [33] . In this study , blood and CSF proviral load were not predictors of HAM/TSP in multivariate regression . There was a strong unidirectional positive correlation between the blood and CSF proviral load , with an average increase of CSF proviral load of nine times and median of 3 . 5 times over the blood proviral load , but there is none difference among the three HTLV-1 infected groups and , no correlation between blood and CSF proviral load and thoracic spinal cord SUV . In the other hand , symptomatic group had the lower blood and CSF proviral load and their thoracic spinal cord SUV was similar to that observed in HAM/TSP group ( p = 1 . 00 ) . This finding suggests a possible incipient neurological involvement in individual with low proviral load . This evidence reinforces the lack of association between proviral load and spinal cord hypomethabolism . About the immune profile , there is no specific immunological marker for diseases associated with HTLV-1 infection [7 , 34] . The cytokines and chemokines evaluated did not differ between asymptomatics and symptomatics , but both groups showed differences compared with HAM/TSP . HAM/TSP have higher rate of plasmatic ITAC , CSF ITAC and IL-8 . ITAC is induced by INF-gamma and it plays an important role in leucocyte trafficking , principally acting on activated CD4 + Th1 cells , CD8 + T cells and NK cells [35] . IL-8 has chemotactic properties on neutrophils and other granulocytic cells , and is also related to angiogenesis [36] . In this study , IL-17A and IL-21 , Th17 pathway activity biomarkers , had lower levels in the HAM/TSP compared to asymptomatic and symptomatic . Th17 pathway is involved in distinct pathophysiological mechanisms of inflammatory , infectious , autoimmune and neoplastic diseases [37 , 38] . We then observed that HAM/TSP group showed CSF inflammatory Th1 profile , with apparent plasmatic Th17 pathway suppression compared to asymptomatic group . Rosa et al . 2018 published an analysis of the cytokine and chemokine profile in the plasma and CSF of this same population classified into two EDSS-based groups: EDSS score 0 and > 0 . Data analysis also demonstrated a Th1 pro-inflammatory profile in patients’ CSF with EDSS score > 0 [39] . In the regression analysis , several independent predictors did not adhere to the multivariate model . It is possible that this was due to the small sample number and the multiple correlations observed between cytokines , chemokines and proviral load . Thoracic spinal cord SUV and CSF ITAC that adhered to the multivariate model were jointly good predictors of HAM/TSP , correctly classifying 81 . 3% individuals into asymptomatic and HAM/TSP groups . The results of multivariate logistic regression predicted that for a decrease of each unit of thoracic SUV , there was an increase of 99 times at risk of HAM/TSP and for each unit of increase of CSF ITAC , the risk of HAM/TSP increases 0 . 33 times . We are aware of limitations of our study , such as the small sample size , and the not HTLV-1 serological testing in PET/CT control group . Regarding this last point , we considered a very low risk of HTLV-1 infection in the control group given that the estimated prevalence of this infection in the general Brazilian population is around 1% [4] . However , possible HTLV-1 infected individuals in this group would reduce the magnitude of the effect observed in 18F-FDG PET/CT . In conclusion , HTLV-1 infection involves the CNS of most infected individuals even in absence of clinical symptoms . The thoracic spinal cord is definitely affected by HTLV-1 , but other CNS segments are also compromised . 18F-FDG PET/CT seems a promising diagnostic tool to evaluate HTLV-1 CNS commitment , especially when associated to high CSF-ITAC levels might explain clinical outcome in different individuals . Our findings suggest that HTLV-1 infection may promote CNS vascular-neuronal dysfunction , propitiating breakdown of CNS protection barriers , lymphocytic infiltration with pro-inflammatory response that contributes concomitantly to neuronal damage . CNS segments with lower blood flow are more susceptible to vascular-neuronal dysfunction , which may be related to HTLV-1 cell-entry mechanisms . Genetic or acquired pathologies related to microcirculatory angiogenesis may be a possible factor to explain the development of HAM/TSP in specific and familial population groups . Further investigation is necessary to assess possible microcirculatory impairment and immune mediated mechanism in HAM/TSP pathophysiology . Our findings showed new venues for studies about potential metabolic and microvascular involvement in the neurological impairment of HTLV-1 infected individuals .
All the procedures performed in the study , involving human participants , were approved by the Research Ethics Committee of the two participating institutions: Hemominas Foundation and Federal University of Minas Gerais , according to the 1964 Helsinki Declaration and its subsequent amendments . Informed written consent was obtained from all participants included in the study . This is a cross-sectional study of a cohort of HTLV-1 infected individuals and a nested case-control study between HTLV-1 infected and not infected controls in relation to spinal cord analysis by 18-F FDG PET/CT . The sample was non-probabilistic and composed of 50 individuals participating in the GIPH cohort , Belo Horizonte , Minas Gerais , Brazil . The GIPH cohort has a record of 1 , 200 participants among former blood donors diagnosed with HTLV-1 and 2 infection , their sexual partners , relatives , patients referred , and controls . It is an open cohort started in 1997 . The sample for this study was a group of 300 HTLV-1 infected cohort participants who underwent neurological evaluation performed between 2007 and 2014 . Participants were admitted in the period from January 2013 to October 2015 and data was collected and analyzed until December 2016 . Two of the 50 subjects invited for this study were excluded . One due to HIV co-infection and the second although showed positive ELISA test for HTLV-1 , confirmatory WB was inconclusive and qualitative PCR in blood was negative in two tests . The 48 HTLV-1 infected individuals who remained in the study were subdivided in three groups , according to neurological impairment , observed in the first appointment . Complementary tests were performed up to 60 days after this appointment . The group referred to as asymptomatic ( AG ) was formed by participants who did not present any symptom , or just few or common symptoms observed in the general population , insufficient to characterize neurological impairment . All individuals classified asymptomatic had a normal neurological physical examination ( Fig 1 ) . The symptomatic group ( SG ) consisted of individuals with symptoms ( Fig 1 ) and normal neurological physical examination . The HAM/TSP group ( HG ) had individuals that fulfilled the established diagnostic criteria for HAM/TSP ( Fig 1 ) [8–9] . The control group was paired for sex and age with the case-group . The control group was composed by 10 healthy individuals and 20 individuals with prostate and colon cancer without CNS involvement , that were submitted to 18-F FDG-PET/CT imaging for other reasons . All individuals enrolled gave written informed consent and allowed their PET/CT image to be used in scientific research . The sample ( AG and HG ) evaluated in this study is representative when considering the magnitude of the effect between the symptoms and signs observed in asymptomatic ( <10% ) and HAM/TSP group ( > 50% ) , for α = 0 . 05 ( bilateral ) and β = 0 . 20 . Sixteen individuals per group were predicted . The same cannot be attributed to the symptomatic group . The inclusion criteria were: HTLV-1 infection confirmed by Western blotting ( WB HTLV 2 . 4 , Genelabs Diagnostics , Singapore ) or real-time PCR; four or more years of formal education , minimum of 18 and maximum of 70 years old . Co-infection evidenced by positive serological tests for human immunodeficiency virus , hepatitis C and B virus , Chagas disease , syphilis , neurological impairment or disease due to another etiology were exclusion criteria . The study was performed in four steps: Clinical—Individuals of the GIPH cohort , eligible for the project , were called for an individual appointment . In this first contact they were informed about project objectives and tests . Those interested in participating signed the Term of Consent and then underwent a neurological appointment , neuropsychological assessment and blood collection to perform serology of possible co-infections . The clinical and neurological data collected in the appointment were systematized and recorded into a database built in the Epidata 3 . 1 software . Complementary tests–Blood and CSF proviral load , cytokines , chemokines and 18F-FDG PET/CT of the whole body were performed at the Molecular Imaging Center ( CTMM/UFMG ) . The participants were instructed to fast for at least 6 hours . First , capillary glycemia was measured , venous access was obtained and used to collect blood for proviral load , cytokines and chemokines assays . Venous access was maintained with 0 . 9% saline until the end of the procedure . Subsequently , participants received a 18F-FDG radiotracer bolus , 5 . 18 MBq/Kg , by intravenous administration . Participants were kept resting for 50 minutes in a room with dimmed light and low stimulation , prior to acquisition of PET/CT images in a time interval of approximately 10 minutes , and reconstructed in a 192 x 192 x 47 matrix using the OSEM ( Ordered Subsets Expectation Maximization ) algorithm , with 2 iterations and 20 subsets . Attenuation correction was performed using computed tomography ( CT ) images . A specific protocol was performed for targeted analysis of the central nervous system , especially the spinal cord . Standardized uptake value ( SUV ) of the thoracic and cervical spinal cord was measured . In the thoracic spinal cord , SUV was measured on T6 , T8 and T10 levels , and at the cervical spinal cord on C5 , C6 and C7 levels . The spinal cord levels were located using the CT image , having the sagittal plane to define the levels and axial plane to locate the medulla in the central canal of the spinal column . The SUV was measured in an oval area with 12 mm and 6 mm diameter , lateral-lateral and anterior-posterior , respectively ( Fig 5 ) . The PET software provided the average SUV measured in the oval area of the particular spinal cord segments . SUV average of the three cervical and three thoracic spinal cord segments were used in the statistical analysis . After the acquisition of PET/CT images , participants underwent a lumbar puncture with local anesthesia for collection of 8 to 10 ml of CSF . Immunobead assay Milliplex MAP human high sensitivity T cell assay ( EMD Millipore ) was used to quantify the expression levels of 19 cytokines in plasma and CSF . The HSTCMAG-28SK kit was designed to detect the following analytes: ITAC , GM-CSF , Fractalkine , IFN-gamma , TNF-α , MIP-3α , IL-1b , IL-12 , IL-13 , IL-17A , IL-2 , IL-4 , IL-5 , IL-6 , IL-7 , IL-8 , IL-10 , IL-21 and IL-23 . Plates were analyzed using a Luminex100/200 system . Median fluorescence intensity ( MFI ) was converted to concentration ( in pg/ml ) using an equation derived from the standard range of each analyte using Milliplex Analyst Software . Each sample was measured in duplicate . Cytokines in the blood and CSF results were not obtained in some HAM/TSP ( blood 1 and CSF 2 ) due to technical difficulties in collecting the material or in the processing of the test . Proviral load in blood and cerebrospinal fluid material were immediately centrifuged at 3 , 000x g for 5 minutes to obtain the buffy coat layer and the cell pellet , respectively , used to obtain DNA . The DNA was extracted using the QIAamp DNA Blood Kit ( Qiagen GmbH , Hilden , Germany ) , following manufacturer’s instructions . The quantification of proviral load was performed by real-time PCR using SYBR Green . Real-time PCR was performed on the ABI Prism 7300 Sequence Detector System ( Applied Biosystems ) . The proviral charge value was calculated as [ ( average number of copies pol/average number of albumin copies/2 ) ] x 10 , 000 and expressed as the number of proviral copies/10 , 000 cells . The proviral load in blood and CSF were not obtained in some participants due to technical difficulties during collection of the material or the processing of the test: asymptomatic ( blood 1 and CSF 3 ) and HAM/TSP ( blood 2 and CSF 4 ) . Demographic , clinical and laboratory data were collected and analyzed using the SPSS 22 . 0 . The descriptive analysis of the groups included the distribution of frequency and percentage of categorical variables , measures of central tendency according to the best suitability for quantitative variables . The comparative analysis between groups included X2 test or Fisher test for categorical variables , t test or ANOVA for quantitative variables with normal distribution , Kruskal-Wallis and Mann-Whitney test for non-parametric data . Pearson or Spearman correlation coefficients were used according to the parametric or non-parametric distribution of the data , respectively . The ROC curve was used to calculate the accuracy , sensitivity , specificity and cutoff of some variables . In order to define which variables would form the best predictor model for HAM/TSP , univariate and multivariate regression were performed . Statistical significance was considered when p ≤ 0 . 05 , except for multivariate entries where p = 0 . 10 and Bonferroni correction where p = 0 . 0167 were considered . The graphics were edited using the software: GraphPad Prism 7 . 0d and Microsoft Exel 2008 for Mac , version 12 . 3 . 1 .
|
For the past 30 years , human T-cell lymphotropic virus type-1 ( HTLV-1 ) has been isolated and associated with neoplastic , inflammatory , and infectious diseases . It is known that the neurological disorder associated with HTLV-1 comprises HTLV-1-associated myelopathy ( HAM/TSP ) or any other isolated signals and symptoms . Despite all the knowledge accumulated so far , the association of neurological diseases to HTLV-1 infection remains difficult and neglected . We designed this study in order to assess the degree of neurological impairment associated with HTLV-1 infection through a metabolic evaluation with 18F-FDG PET/CT . Our results evidenced a more pronounced hypometabolism in the spinal cord of individuals with neurological impairment , but also evidenced hypometabolism in asymptomatic HTLV-1 infected individuals . We believe that areas of the CNS with lower circulatory and perfusional balance are more vulnerable to HTLV-1 infection . Mechanisms of cellular entry of the virus may be associated with loss of microcirculatory homeostasis and predisposition to a breakdown of the blood-brain barrier in these areas . Further studies are still necessary to shed light on the mechanisms associated with brain and spinal hypometabolism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"methods"
] |
[
"cell",
"motility",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"cytokines",
"nervous",
"system",
"pathogens",
"immunology",
"microbiology",
"neuroscience",
"retroviruses",
"viruses",
"developmental",
"biology",
"rna",
"viruses",
"molecular",
"development",
"spinal",
"cord",
"medical",
"microbiology",
"htlv-1",
"microbial",
"pathogens",
"blood",
"plasma",
"chemotaxis",
"immune",
"system",
"neuroanatomy",
"anatomy",
"blood",
"central",
"nervous",
"system",
"cell",
"biology",
"viral",
"pathogens",
"physiology",
"chemokines",
"biology",
"and",
"life",
"sciences",
"cerebrospinal",
"fluid",
"organisms"
] |
2018
|
Spinal cord hypometabolism associated with infection by human T-cell lymphotropic virus type 1(HTLV-1)
|
At the early stage of infection , human immunodeficiency virus ( HIV ) -1 predominantly uses the CCR5 coreceptor for host cell entry . The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS . The presence of a ‘fitness valley’ separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs . Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process . We have developed a phylogenetic pipeline for the molecular clock analysis , ancestral reconstruction , and visualization of deep sequence data . These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples ( median 7 time points ) from 8 untreated subjects with chronic HIV infections ( Amsterdam Cohort Studies on HIV-1 infection and AIDS ) . We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days . HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm . We determined that the first mutations contributing to CXCR4 use emerged about 16 ( per subject range 4 to 30 ) months before the earliest predicted CXCR4-using ancestor , which preceded the first positive cell-based assay of CXCR4 usage by 10 ( range 5 to 25 ) months . CXCR4 usage arose in multiple lineages within 5 of 8 subjects , and ancestral lineages following alternate mutational pathways before going extinct were common . We observed highly patient-specific distributions and time-scales of mutation accumulation , implying that the role of a fitness valley is contingent on the genotype of the transmitted variant .
Human immunodeficiency virus type 1 ( HIV-1 ) enters into a host cell by binding the CD4 receptor and one of two HIV coreceptors , CCR5 or CXCR4 . Most HIV-1 variants manifest preferential binding to one or the other coreceptor , a phenotype that is referred to as HIV coreceptor usage or tropism . HIV populations are predominantly CCR5-using at the start of infection and switch to being CXCR4-using in roughly 50% of HIV subtype B infections before progressing to AIDS [1] , [2]; this proportion varies substantially among HIV subtypes with the highest reported in subtype D [3] . This HIV coreceptor switch is clinically significant because it is associated with accelerated deterioration of the CD4+ T-cell population and rate of progression to AIDS [1] , [2] . In addition , a new class of antiretroviral drugs ( HIV coreceptor antagonists ) inhibit HIV infection by competitively binding the CCR5 coreceptor . A patient carrying detectable CXCR4-using variants is essentially not responsive to this class of drugs [4] . Despite its clinical significance , the biological determinants underlying the evolution of the HIV coreceptor switch remain poorly understood [5] . HIV coreceptor usage is a genetically complex phenotype . The primary genetic determinant is the third variable region ( V3 ) of the HIV gp120 envelope glycoprotein comprising a disulfide-bonded loop that varies between 30 and 40 amino acids in length . The presence of basic residues at V3 reference positions 11 and 25 is strongly predictive of CXCR4 usage [6] but there are many exceptions to this rule . Although as few as one or two amino acid replacements in V3 may be sufficient to change coreceptor usage [7] , the earliest detectable CXCR4-using viruses in vivo tend to carry additional compensatory mutations in V3 [8] . The effects of mutations in V3 can also be modulated by mutations within other regions of the HIV envelope glycoprotein [9] . Furthermore , the V3 region is targeted by both the cellular and humoral immune responses and undergoes extremely rapid host-specific adaptation [10] , which may influence evolution of CXCR4 use . Consequently , CXCR4 use could potentially evolve through a series of intermediate genotypes ( mutational pathways ) that are unique to each individual . The nature of the mutational pathway to evolving CXCR4 usage is postulated to be a significant determinant of the limited incidence of the HIV coreceptor switch [5] . If CCR5- and CXCR4-using genotypes are separated by intermediate genotypes of reduced fitness , then the traversal of this ‘fitness valley’ is a chance event that might never occur over the course of an HIV infection . Negative selection prevents intermediate genotypes from reaching substantial frequencies in the population . As a result , any lineage must rapidly accumulate multiple mutations to reach the CXCR4 genotype before going extinct; this process is known as ‘stochastic tunnelling’ [11] . In contrast , if the pathway passes through intermediates of progressively greater fitness , then CXCR4 usage evolves by the gradual and deterministic accumulation of mutations that would unfold at a similar rate in all individuals ( Figure 1 ) . Reconstructing the evolutionary history of CXCR4 usage within individuals would lend important insight into which model better explains the evolution of HIV coreceptor tropism and hence the genetic determinants of HIV pathogenesis . Specifically , we want to determine whether the dynamics of the evolution of HIV coreceptor use in these subjects was consistent with a gradual ( immediate and slow ) or fitness valley model ( delayed and rapid; Figure 1 ) . Recently , an exceptional set of HIV genetic sequence data was collected for the purpose of identifying putative evolutionary intermediates in eight chronically-infected subjects from the Amsterdam Cohort Studies on HIV infection and AIDS ( ACS ) whose virus populations had undergone an HIV coreceptor switch [12] . These data were generated by ‘deep sequencing’ , an application of next-generation sequencing technology for large-scale automated clonal sequencing of individual nucleic acids along a fixed interval [13] . By generating thousands of clonal sequences per amplicon , deep sequencing can provide a detailed sample of the genetic variation in a virus population . Accordingly , it has been used with success to reliably detect drug resistant HIV variants at frequencies as low as 2% in the population [14] , [15] . Based on deep sequence data generated from serial samples of HIV at three month intervals , Bunnik and colleagues [12] were able to observe HIV sequence variants that were intermediate of the predominant CCR5- and CXCR4-using variants in these samples as determined by a minimum spanning tree , the shortest acyclic graph connecting all sequences in the sample where distance was quantified by the number of nucleotide differences ( Hamming distance ) . While a minimum spanning tree can provide a useful visual representation of the genetic diversity in a sample , it should not be construed as representing the evolutionary history of the sample . First , a minimum spanning tree can only traverse the set of observed sequences . It does not attempt to reconstruct the ancestors from which the observed sequences descended . This limitation is problematic if a significant amount of the evolution of HIV coreceptor use takes place prior to the time of the first sample , or between subsequent samples . Second , a minimum spanning tree does not explicitly incorporate a time dimension . Connections in the graph are drawn between sequences irrespective of time of sampling , which can make it exceedingly difficult to interpret the minimum spanning tree with respect to time . For instance , it is difficult to assess from a minimum spanning tree the rate at which evolution has unfolded , which is a prerequisite to differentiate between the gradual and fitness valley models of the HIV coreceptor switch . In this study , we apply a phylogenetic modelling framework to these data with the direct objective of reconstructing the evolutionary history of HIV coreceptor use within subjects over time . Like a minimum spanning tree , a phylogeny is an acyclic graph that connects observed sequences . Generally speaking , however , a phylogeny is a bifurcating tree that relates observed sequences at its tips by incorporating latent nodes that represent a hierarchy of the most recent common ancestors of the sample ( Supporting Figure S1 ) . Put another way , a phylogeny directly models the evolutionary relationships among observed sequences . It can therefore be used as a template to fit models of sequence evolution that are typically implemented as a continuous-time Markov model [16] . Furthermore , efficient algorithms have been developed for extracting maximum likelihood reconstructions of ancestral character states from the combination of the phylogeny and a model of evolution [17] . The use of phylogenetic methods enables us to take advantage of the known dates of sampling in these data to directly estimate the rates of HIV evolution along lineages within each subject over time . This practice is known as reconstructing a phylogeny with ‘dated tips’ [18] , which enables one to measure the heights of ancestral nodes in the tree in units of real time ( such as days or years ) . By reconstructing ancestral sequences at these nodes , we can deduce that one or more mutations have occurred along the branch between two nodes from differences in the corresponding sequences [19] and thereby estimate when each mutation first arose in the population . This is crucial information for quantifying the dynamics of virus evolution over time . Here we show how the phylogenetic analysis of the serial deep sequence data from the ACS cohort can reconstruct the dynamics of HIV coreceptor usage evolution in each population , ranging from the estimated start of infection to the last date of sampling .
The Amsterdam Cohort Studies on HIV-1 infection and AIDS ( ACS ) have been conducted in accordance with the ethical principles set out in the Declaration of Helsinki , and written informed consent was obtained prior to data and material collection . The study was approved by the Academic Medical Center institutional medical ethics committee . The individuals included in our present study were men who have sex with men participating in the ACS who were seropositive and asymptomatic at enrolment into the cohort between 1988 and 1994 [20] . Blood samples were obtained at approximately 3 month intervals from 8 participants who had at least three negative MT-2 assays in the 12 months prior to their first positive MT-2 assay result . MT-2 is a human lymphoblastoid cell line that is highly susceptible to infection by CXCR4-using HIV , which is manifested by the formation of multinucleate cells ( syncytia ) . We will refer to the time of the first positive MT-2 syncytium induction ( SI ) assay as ( ‘time zero’ ) . Viral loads associated with these samples were reported in a previous study , where we have retained the anonymized identifiers for study participants ( DS1 to DS8 ) [12] . Over the course of follow-up , all individuals in the present study were eventually verified as having a CXCR4-using infection using an in vitro recombinant virus assay ( Trofile ) and by deep sequencing [12] . Virus sequences were classified by genotype ( env V3 region ) as CXCR4-using by the geno2pheno ( g2p ) algorithm [21] . The g2p algorithm is a support vector machine-based classifier trained on a database of predominantly clonal V3 sequences labelled with HIV coreceptor tropism as determined by cell-based assays . It yields a predictive score that is conventionally mapped to an empirical false positive rate ( FPR ) distribution for interpretation . Based on previous studies [4] , [22] , we use an FPR cutoff of 3 . 5 to classify sequences as CXCR4-using . At this cutoff , the algorithm predicts that 3 . 5% of CCR5-using sequences would be misclassified as CXCR4-using . In this study , the range of samples subjected to deep sequencing has been expanded from the range reported in [12] ( 12 to 0 months relative to ) to encompass up to 24 months prior and up to 6 months subsequent to ( 24 to 6 months ) . Using a NucliSENS easyMAG ( bioMérieux ) , HIV RNA was extracted from 500 L from previously frozen serum samples and eluted in 60 L of buffer . Three aliquots of 4 L eluate each ( 12 L total ) were transferred to triplicate RT-PCR amplification reactions using the SuperScript II OneStep RT-PCR system with Platinum Taq High Fidelity enzyme ( Invitrogen ) . These amplicons in triplicate were independently amplified in second-round PCR reactions using the Expand High Fidelity PCR system ( Roche ) with primers that were specific to the interval of the HIV-1 genome surrounding the env V3 region ( HXB2 reference nucleotides ( 7085–7372 ) [12] and which incorporated unique 10bp sequence ‘tags’ ( also known as barcodes ) for multiplexed pyrosequencing [23] . The triplicate second-round amplicons were pooled in equal quantities for deep sequencing on Roche/454 GS-FLX or GS Junior platforms as previously described in [4] . The diluting effects of this experimental protocol will inevitably reduce the number of nucleic acids in the sample represented by copies available for clonal sequencing by the 454 platform . This dilution could have a detrimental effect on phylogenetic reconstruction . In the extreme case , if all templates being sequenced were descended from a single copy of HIV RNA in the blood specimen , then the phylogeny would only reflect genetic divergence due to sequencing error . Suppose that there were copies of HIV RNA in 1 mL of serum . This number was immediately halved as 500 L was used for extraction . The number of nucleic acids that entered the RT-PCR step was determined in part by the efficiency of viral RNA extraction by the NucliSENS easyMAG , which we estimated to be about 44% ( Supporting Text S1 ) . Hence , we expect that about 0 . 22 nucleic acids would have been present in the extraction eluate . Using 12 L from 60 L of eluate for RT-PCR would have further reduced the number of nucleic acids to 0 . 044 . Subsequent dilution due to variation in amplification rates among initial nucleic acids would have been ameliorated by carrying out RT-PCR in triplicate . Therefore , we estimate an approximate 20-fold dilution factor due to sample processing . Viral loads reported from these chronic untreated subjects were generally high with a median of 17 , 000 HIV RNA copies/mL [12] . Consequently , we estimate templates in the amplicon available for sequencing would have been derived from about 850 HIV RNA copies in the original specimen . We randomly subsampled 50 sequences per time point for phylogenetic reconstruction ( see below ) . Using a Poisson approximation validated by simulation , we estimated that the median per-molecule probability of resampling ( appearing twice or more in a sample of 50 sequences ) was about 0 . 17% . The raw sequence output ( ‘reads’ ) generated by Roche/454 GS-FLX or GS Junior platforms was processed by a Ruby script that sorted reads by region , tag and primer; trimmed low quality bases from the start and end of reads ( according to quality scores reported by Roche/454 GS software version 1 . 1 ) ; and temporarily collapsed identical reads into unique sequence entries annotated by read count . We retained the entire read lengths ( averaging about 250 bp ) for phylogenetic analysis; in a previous study , the reads had been clipped to the V3 region ( 105 bp ) [12] . The resulting files were processed using a custom sequence alignment module implemented in HyPhy [24] . This module was designed to compensate for the high rate of insertions and deletions ( indels ) introduced by pyrosequencing-based platforms by aligning all three reading frames of each sequence against a reference protein sequence ( HXB2 gp120 residues 278–375 ) . We assumed that true HIV coding sequences maintained a single reading frame along their entire length , such that any frame-shifts represented nucleotide indel errors induced by pyrosequencing . This algorithm is described in Supporting Text S2 . The resulting sequences were grouped by patient , re-expanded by read counts , and annotated by sample dates in units of days since January 1 , 1990 . For each patient , 50 sequences were randomly subsampled without replacement from every sampling time point ( averaging 3642 reads per sample ) for a median of 350 sequences total . This step was necessary to reduce the number of all possible trees to a level at which a Markov chain Monte Carlo ( MCMC ) sample could converge to the posterior distribution within a feasible amount of time ( see below ) . A multiple sequence alignment was generated for each set of sequences using MUSCLE version 3 . 8 . 31 [25] with diagonal optimization and a single iteration , and refined manually using the alignment viewer Se-Al ( Andrew Rambaut , http://tree . bio . ed . ac . uk/software/seal/ ) . The final sequence alignments , annotated by specimen and collection date , have been deposited in the public Genbank database ( accession numbers JX561243-JX564138 ) . Additionally , the unprocessed short read data have been deposited to the European Nucleotide Archive ( study accession number ERP001795 , run accession numbers ERR169842-ERR169899 ) . We used BEAST [26] to reconstruct dated-tips phylogenies from these data . BEAST uses a Bayesian MCMC procedure to sample trees from the posterior distribution given the sequence data and a prior distribution that is usually set to the coalescent model . Each alignment representing serial samples of HIV sequences from a given patient was converted into a BEAST XML format using a custom Python script . These conversions were based on a template XML file with the following settings: Tamura-Nei [27] nucleotide substitution model with rate variation across sites modelled by a discretized gamma distribution with 4 rate categories , and with substitution rates and bias parameters unlinked between codon positions 1 and 2 and position 3 [28]; an uncorrelated lognormal molecular clock; a Bayesian skyline model with 3 population size classes; and a chain length of steps with chain states written to log files at regular intervals of steps . These settings were chosen on the basis of preliminary runs on these data and previous experience [29] . Chains were seeded with a random coalescent tree . All chain samples were executed in parallel on a Beowulf cluster using BEAST version 1 . 6 . 1 with a native-compiled likelihood core . We ran two replicate chains for each XML file to assess convergence . We assessed the effect of subsampling 50 sequences per time point by running additional chain samples on a second set of randomly subsampled sequence alignments and observed no qualitative differences in results based on phylogenetic or ancestral sequence reconstruction ( see Supporting Figure S2 ) . Convergence in chain samples was assessed using Gelman and Rubin's convergence diagnostic as implemented in the R package coda [30] . This diagnostic reports a potential scale reduction factor ( PRSF ) that is a conservative estimate of the ratio between the pooled variance across replicate chains to the variance within chains [31] . Values of PRSF that are substantially greater than 1 indicate a lack of convergence such that the chain samples are still influenced by their initial values , such as when a chain becomes trapped on a local optimum . If the upper confidence interval in estimation of PRSF exceeded 1 . 25 for replicate chains , then we ran additional chain samples from the same BEAST XML file for a longer number of steps and re-evaluated their PRSF . Plots of posterior traces from replicate chain samples are provided as Supporting Figure S3 . Newick string representations of 100 trees were extracted from the log files at regular intervals following a burn-in period of 20% ( steps by default ) . A Muse-Gaut [32] codon substitution model crossed with a general time-reversible model of nucleotide substitution [33] was fit to every tree in the thinned sample for a given sequence alignment using maximum likelihood heuristics implemented in HyPhy [24] . Branch lengths in each tree , which were expressed in units of days , were constrained to scale by a global factor when estimating the expected number of substitutions per codon site . Constraining the codon tree to remain congruent to the input tree not only speeds up computation but also preserves the relative differences in branch lengths inferred under a molecular clock . For a given fitted codon model and tree sample , ancestral sequences were generated by sampling 100 character states from the posterior distributions reconstructed at every node of the tree [34] . This approach is similar to the hierarchical Bayes approach to ancestral reconstruction [35] that integrates over the uncertainty in estimation of tree parameters ( such as tree topology and branch lengths ) . Because codon substitution models were originally developed to compare non-synonymous and synonymous rates of substitution , stop codons are not permitted . As a result , it was necessary to censor any stop codons in the sequence alignments with gap characters , which are conventionally handled as fully ambiguous codons . Codon substitution models generally do not model insertions or deletions ( indels ) , and gaps are typically handled as missing data that can be resolved into any codon with equal probability . As a result , insertion polymorphisms in the observed sequences would be propagated to all ancestors when reconstructing sequences from a fitted codon model . This approach is not adequate for our purposes because HIV populations within hosts commonly contain legitimate indel polymorphisms in the env gene , and sequence length variation in the HIV-1 env V3 region can influence the HIV coreceptor tropism phenotype . Indeed , predictive models of HIV coreceptor tropism often incorporate the presence or absence of indels in V3 relative to a reference sequence [21] . Accordingly , we implemented a method to reconstruct indel character states in the ancestral codon sequences . First , we identified and encoded indel polymorphisms in the observed sequences as integer values using an algorithm implemented in Python ( see Supporting Text S3 ) . In brief , an indel polymorphism was defined as a contiguous interval in the alignment containing one or more codon gaps . This polymorphism may be comprised of two or more character states corresponding to the respective lengths and location of each codon insertion or deletion within the gapped interval . We encoded these indels by integer values in decreasing order of prevalence , such that 0 represented the most common character state . Next , we fit a model of indel evolution to the resulting alignment of integer-valued sequence encodings . The evolution of indels can be modelled as a finite-state continuous-time Markov process akin to those used to model the evolution of nucleotide and amino acid sequences [36] . Because the maximum number of character states in any indel polymorphism did not exceed 3 in these data , we used the following instantaneous rate matrix:which assumes that states ( 1 ) and ( 2 ) are derived from ( 0 ) , the most prevalent state , and that there are no transitions directly between ( 1 ) and ( 2 ) . Rates are scaled arbitrarily to the rate of transition from state ( 0 ) to state ( 1 ) . When indel rates were assumed to be reversible ( equal rates of insertion and deletion affecting the same codons ) , we applied the constraints and ; otherwise , indel evolution was non-reversible . Character frequencies were computed dynamically by setting all values in the right-most column of to 1 and extracting the last row of the matrix inverse . Both reversible and non-reversible models were fit using maximum likelihood heuristics in HyPhy to each sampled tree with branch lengths constrained to scale by a global factor to preserve the molecular clock characteristics of the tree as was applied to fitting the codon model ( see above ) . Since the reversible model is a special case of the non-reversible model , we calculated the likelihood ratio test statistic ( ) to select between the fitted models . Computing across replicate ancestral reconstructions , the reversible model was rejected only for subject DS2 ( mean and interquartile range , [7 . 1 , 11 . 1] , [0 . 004 , 0 . 28] ) . We proceeded with the ancestral reconstruction of indel polymorphisms using the respective best models for each subject data set . For each tree , 100 ancestral indel character states were sampled from the resulting posterior distributions at all internal nodes of the tree . These reconstructions were applied to the ancestral codon sequence reconstructions by overwriting nucleotides with gap characters according to the indel reconstructions . In total , we generated sets of ancestral reconstructions for each patient ( 100 trees100 replicate samples ) . HIV coreceptor tropism predictions were generated for all ancestral sequences using the g2p algorithm and time-stamped by their heights in the trees , which we measured in units of days since an arbitrary date in the past . For every tree in the sample , we tallied the relative frequencies of every clade ( the subset of tips that descend from a given ancestral node ) and then calculated the product of these frequencies for clades represented in each tree . The tree that maximized this product was taken as the most representative point estimate for the sample ( the maximum credibility tree ) [26] . For each replicate ancestral reconstruction , we mapped only the mutations in the V3 region that were predicted to increase the probability of CXCR4 usage ( according to the g2p algorithm ) to branches in the maximum credibility tree . We recorded the relative frequencies of these branches and identified the replicate that maximized the sum of these frequencies . In this context , we used the sum rather than the product to avoid penalizing replicates for mapping mutations to greater numbers of branches . We refer to the replicate that maximized this sum as the maximum ancestral reconstruction credible set ( MARCS ) . Each maximum credibility tree and corresponding MARCS was rendered in PostScript to visualize trends in the reconstructed evolution of HIV within patients . Consider be the set of all ancestral nodes corresponding to the ends of branches to which one or more mutations in the MARCS was mapped . always included the root of the tree . Every node in was plotted with its position representing its location on the timeline of the infection ( ranging from the MRCA on the left to the last sampling time on the right ) , and its position representing the midpoint of the vertical positions of all its descendant extant sequences in the maximum credibility tree . For every node in , a line was drawn back to its most recent ancestor in . Consequently , any intervening branches that did not contain any mutations contributing to the evolution of CXCR4 usage were collapsed in this visualization . Each branch between members of was rendered in a colour representing the FPR prediction from the g2p algorithm for the ancestral sequence reconstructed at the right-most ( most recent ) node . In addition , each branch was labelled with all reconstructed mutations contributing to CXCR4 usage that mapped to this branch . These procedures were automated using a HyPhy/Python pipeline . Because these visualization strategies focused only on ancestral lineages accumulating mutations towards CXCR4 usage , we also visualized the overall diversification of HIV within hosts over time with respect to coreceptor usage predictions using 2-dimensional histograms . Across all 100 ancestral reconstructions on the maximum credibility tree , ancestral sequences were binned with respect to their predicted FPR value and time . The density of ancestral sequences in each bin , normalized for each time interval , was represented by degree of opacity for the bin's coloration in the 2-dimensional histogram . This visualization procedure was implemented in R using a modification of the hist2d function in the gplots package . The entire workflow from raw sequence data to visualization is presented as a flowchart in Supporting Figure S4 and scripts used in our analysis are available at http://hyphy . org/wiki/Emerge .
The root of a phylogenetic tree estimates the most recent common ancestor ( MRCA ) of all individuals represented at the tips of the tree . Because the time of sampling is known for every tip in the tree , we can use the time elapsed between samples to directly estimate the rate of evolution ( molecular clock ) and extrapolate back to timing branching events deeper in the tree . Since HIV tends to undergo a severe population bottleneck at transmission , the time to the MRCA ( ) can provide a reasonable estimate of the time of HIV infection [29] . The median estimates of for each subject ranged from 25 . 7 to 59 . 6 months prior to the time of the first positive MT-2 assay ( herein referred to as ) with an average of 41 . 3 months ( Figure 2 ) . Since the sample population consisted of longitudinal samples from infections undergoing an HIV coreceptor switch , initially determined by a transition from NSI ( non-syncytium inducing ) to SI phenotypes , we also extracted the times to the MRCA of all observed sequences that were predicted to be CXCR4-using according to the g2p algorithm with a false positive rate ( FPR ) cutoff of 3 . 5 . This ancestor will be referred to as the X4-MRCA . Generally , estimates of times to the X4-MRCAs ( ) were similar to the corresponding estimates of , indicating that CXCR4-using variants had emerged independently in multiple branches of the tree that did not converge until the MRCA ( Figure 2 ) . In one extreme exception , the was estimated in subject DS2 to be about 40 months after the median estimate and about 13 . 5 months prior to the ( Figure 2 ) . In this case , mutations contributing to CXCR4 usage were reconstructed along one lineage only ( see next section; Figure 3 ) . We used the coefficient of variation parameter ( ) from the uncorrelated lognormal clock model to assess whether rates of evolution varied throughout each tree . A posterior distribution of with a mode of zero indicates that one cannot reject a strict molecular clock model given the data [37] . For all subjects , the modes of the posterior distributions of were substantially above zero , indicating significant variation in rates of evolution over time . Median clock rates among subjects ranged from ( DS2 ) to ( DS3 ) mutations per nucleotide site per day . Variation in mean clock rate estimates among subjects was statistically significant ( one-way analysis of variance , , ) . Because the X4-MRCA was not necessarily itself a CXCR4-using variant , we reconstructed ancestral sequences throughout the phylogeny to determine the earliest predicted CXCR4-using ancestor for each subject . This was accomplished by sampling ancestral sequences from the posterior distributions reconstructed under combined models of codon and indel evolution at all internal nodes of each sampled tree . The times of the earliest ancestors predicted to be CXCR4-using by the g2p algorithm ( FPR3 . 5 ) ranged from 4 . 6 to 24 . 7 months before with a mean of 10 . 1 months ( Figure 2 ) . In other words , predicted ancestral CXCR4-using variants were on average present within a patient nearly one year prior to the first positive MT-2 assay . The time difference between the median estimate of and the earliest CXCR4-using ancestor ranged from 15 . 6 to 43 . 5 months with a mean of 31 . 4 months ( Figure 2 ) . Because none of the participants received antiretroviral therapy over the course of the study [12] , this result can be interpreted as estimating the expected waiting time for the first CXCR4-using variants to emerge as a product of the accumulation of genetic variation in an HIV infection in the absence of antiretroviral selection . These molecular clock results indicated that the evolution of CXCR4 usage could have unfolded over a period of one to several years within each subject . We carried out a detailed phylogenetic reconstruction of ancestral intermediates to characterize the dynamics of HIV coreceptor usage evolution over time . The evolution of CXCR4 use may require one to several amino acid replacements in V3 , the primary genetic determinant of HIV coreceptor tropism . Because we have reconstructed the sequences for every ancestral node in the sampled trees , it is possible to map specific mutations to the branches of any given tree based on discordances in the reconstructed sequences on either end of a branch . To facilitate interpretation , we generated tree visualizations for only the maximum ancestral reconstruction credibility set ( MARCS ) on the maximum credibility tree . Figure 3 displays only the single lineages in the maximum credibility trees for subjects DS2 and DS7 that accumulated mutations culminating in a predicted FPR value below 3 . 5 . The complete maximum credibility trees for all subjects are provided as Supporting Figure S5 . In summary , whether the dynamics of HIV evolution was consistent with either fitness valley or gradual models depended on which subject was being evaluated . Subject DS2 provides an unambiguous example of the gradual accumulation of mutations contributing to CXCR4 use in a succession of intermediate genotypes over a period of roughly 3 to 4 years , culminating in the mutation K25R to yield a genotype with a predicted FPR of 0 . 6 under the g2p algorithm ( Figure 3 ) . In the final sample from DS2 , 22% of the observed sequences were inferred to be descended from this ancestor . The first mutations to emerge ( T19A and T3R ) were mapped to a branch descending directly from the MRCA . Hence , the emergence of HIV coreceptor usage intermediates began soon after infection as predicted by the gradual model . On the other hand , multiple mutations occurred within a comparably narrow time interval of roughly half a year in subject DS7 ( Figure 3 ) and only after a delay of nearly two years after , which was consistent with the fitness valley model . For the remaining subjects , the emergence of mutations promoting CXCR4 use was generally more consistent with the gradual evolution observed for subject DS2 ( Figure S5 ) . We explored this trend in depth using additional visualization schemes reported below . In all cases , a substantial portion of HIV coreceptor evolution was mapped to ancestral lineages preceding the first sample , underscoring the importance of phylogenetic reconstruction . An intriguing feature of the maximum credibility tree maps is that predicted CXCR4-using ancestors emerged in more than one lineage in 5 of 8 subjects ( Supporting Figure S3 ) . The largest number of lineages attaining a CXCR4-using genotype was 5 in subject DS6 , although these lineages were related by a common ancestor preceding these endpoints by only about a year ( Figure S5 ) . We also observed evidence of parallel evolution along 3 lineages within DS8 that accumulated the mutations Q10R , S11G , H13R , D25K and the insertion −22A ( Figure S5 ) . In addition , the maximum credibility trees featured many lineages that accumulated a different set of mutations and failed to attain a CXCR4-using genotype before going extinct . For example , the main lineage leading to a CXCR4-using ancestor in subject DS1 accumulated the mutations L20M , K10R , G24E , Q25R and I27V with descendants comprising 44% of the last sample . A second lineage accumulated L20F , I27V , N29D and a deletion at Q25 to reach a predicted FPR of 5 . 7 , leaving descendants comprising 22% of an earlier sample before going extinct ( Figure S5 ) . Consequently , lineage-specific dynamics of HIV coreceptor usage evolution may become obscured at the population level by other lineages following divergent mutational pathways . To compare the locations of reconstructed mutations in the V3 region among subjects , we annotated the V3 amino acid sequence reconstructed at the root of the maximum credibility tree with the predominant mutational pathway , which was defined as comprising all mutations contributing to CXCR4 use that were mapped to the lineage with the largest proportion of descendants in the most recent sample ( Figure 4 ) . This visualization makes it clear that the predominant mutational pathways followed by each HIV population are highly divergent among subjects . The total numbers of mutations contributing to a CXCR4-usage prediction ranged from 4 to 13 ( median 9 ) mutations per pathway . Mutations occurred most often at V3 loop positions 11 and 25 ( in the predominant pathways of 7 and 8 subjects , respectively ) , followed by positions 13 , 19 , 20 and 32 ( Figure 4 ) . Insertions and deletions played a significant role in the evolution of CXCR4 usage . Specifically , the predominant pathways in DS6 and DS8 included a deletion at V3 positions 24–26 and an insertion at position 22 in DS8 serum , respectively , which we were able to reconstruct by incorporating a model of indel evolution into the ancestral reconstruction procedure ( see Methods ) . For all subjects , we quantified the time scale of CXCR4 usage evolution along the predominant mutational pathway . This time scale was measured by the estimated number of months between the start of the evolution of CXCR4 use ( the midpoint of the branch on which the first mutations promoting CXCR4 usage were mapped ) and the earliest CXCR4-using ancestor ( the midpoint of the earliest branch with a predicted FPR below 3 . 5 ) . These intervals ranged from 4 . 1 ( DS7 ) to 30 . 6 ( DS2 ) months with a mean of 16 . 6 months . To assess whether there was any association between these time intervals and the waiting time until the emergence of the first mutations promoting CXCR4 use , we calculated these quantities for all ancestral lineages in all subjects that eventually attained a predicted FPR below 3 . 5 . We found a significant negative correlation between the waiting time and time scale of evolution ( one-sided Pearson's , df , ) that is consistent with a trade-off between gradual and fitness-valley modes of evolution . However , this correlation is biased by the non-independent evolution of lineages within the same subject . If we adjust for this by averaging values across lineages per subject , the correlation remains negative but is no longer significant ( , ) . One of the drawbacks to the preceding visualization schemes is that they focus on the mutations or a subset of mutations comprising the MARCS . To summarize all replicate ancestral reconstructions on the maximum credibility tree for each subject , we generated 2-dimensional histograms summarizing the distributions of coreceptor usage predictions for all reconstructed ancestral sequences over time . Darker shaded cells represent higher densities in the corresponding intervals of reconstructed FPR predictions , normalized for a given time interval ( Figure 5 ) . In general , each distribution broadens over time ( from left to right ) as the accumulation of genetic variation manifests itself in the diversification of coreceptor usage predictions . The histograms derived from our analysis of sequence variation from subjects DS5 and DS7 are characterized by a distinct and rapid bifurcation into low FPR values from a main trunk of high FPR values ( Figure 5 ) . We have already determined that this dynamic in subject DS7 can be attributed to the rapid accumulation of mutations after a substantial delay ( Figure 3 ) that is consistent with the fitness valley model of HIV coreceptor usage evolution . The map of mutations to the maximum credibility tree reconstructed from DS5 sequences is also consistent with this interpretation ( Supporting Figure S5 ) . However , the reconstructed dynamics in the remaining six subjects exhibited finer gradation in FPR values over time ( Figure 5 ) . An interesting feature of these histograms is the emergence of lineages ( for example , in subjects DS3 and DS8 ) with a greater tendency for CCR5 usage than the MRCA , which represents the putative transmitted variant . Consequently , the predominant CCR5-using variant at the time of sampling is not necessarily representative of the CCR5-using ancestor from which CXCR4-using lineages are derived , which can only be revealed by ancestral reconstruction using phylogenetic methods .
Our findings indicate that a substantial fraction of the evolutionary history of HIV coreceptor usage preceded the first samples from these subjects by entire years , a significant amount on the time scale of HIV evolution . Consequently , the direct comparison of observed sequences , such as by a minimum-spanning tree [12] , is insufficient to determine whether these data support the transmission-mutation hypothesis , which stipulates a fitness valley separating CCR5- and CXCR4-using genotypes [5] . We have shown how phylogenetic methods can be used to reconstruct the ancestral HIV sequences from which the observed data descend . The availability of samples from different points in time enabled us to estimate the rates of HIV evolution using molecular clock models . In turn , this enabled us to date branches in the phylogeny down to the most recent common ancestor , and to date the emergence of specific mutations . We found that the reconstructed evolutionary dynamics of HIV coreceptor usage did not equivocally support the fitness valley postulated by the transmission-mutation hypothesis , although there were two cases ( DS5 and DS7 ) where dynamics were consistent with the presence of a fitness valley . Additionally , we found some evidence of a negative correlation between the time to the onset of the HIV coreceptor switch and the duration of the switch itself . Hence , the dynamics of the HIV coreceptor switch cannot be explained by a single model because it is dependent on the genotype of the transmitted variant , which determines the pathways available to evolve CXCR4 usage . In other words , these results suggest that not every HIV infection begins at the peak of a fitness valley with respect to HIV coreceptor usage . Furthermore , if the role of a fitness valley in shaping the evolution of HIV coreceptor usage is contingent on the genotype of the transmitted variant , as implied by our results , then the probability of the HIV coreceptor switch may be a heritable trait among transmitted virus lineages . This study makes use of next-generation sequencing to automate the process of sequencing individual nucleic acids from the sample population ( clonal sequencing ) , which is otherwise time-consuming and less scaleable . Specifically , we use an ‘ultra-deep’ application of next-generation sequencing , which generates thousands of reads from the same region of each nucleic acid to yield a large sample of genetic variation that is ideal for phylogenetic analysis . A potential hazard of this application is that the large number of reads may outnumber the actual number of HIV RNA copies from the specimen , leading to excessive resampling of genetic variation . We have estimated that sample processing would result in about a 20-fold dilution in the number of nucleic acids ( see Methods section ) . While this is substantial , the serum samples most likely contained high numbers of nucleic acids because they were drawn from treatment naïve subjects with chronic HIV infections . The median viral load previously reported from these subjects was 17 , 000 HIV RNA copies/mL [12] . We would therefore estimate that about 850 nucleic acids would be represented by copies at the sequencing stage of the sample processing protocol . Given that we used only 50 random sequences from each sample , the probability of subsampling was about 0 . 2% per molecule . Additionally , we directly measured the number of HIV RNA copies available for sequencing by using recently-developed ‘primer ID’ technique [38] . This technique employs a partially-degenerate primer in the reverse transcription reaction that causes each complementary DNA strand to be labelled by a random string of nucleotides . Consequently , sequences sharing the same primer ID will have been derived from the same nucleic acid at the initial phase of RT-PCR amplification . In other words , the number of unique primer IDs should correspond to the number of nucleic acids transferred from the extraction eluate to the RT-PCR reactions in triplicate . Because the original sera samples were too depleted , we reprocessed frozen HIV RNA extracts from four samples using a primer containing a degenerate 9-mer . These samples corresponded to two time points from DS8 at +3 and +6 . 8 months since the first positive MT-2 assay ( ) , one from DS2 at +6 months since , and one from DS6 at +3 months . Viral loads were previously reported to be approximately 1000 HIV RNA copies per mL at both DS8 time points [12]; no viral load measurements were available at the time points from either DS2 or DS6 . The numbers of unique primer ID sequences in the resulting deep sequence data were 400 , 317 , 2003 , and 343 , respectively . Hence , there was a low probability ( 1% ) that copies of the same nucleic acid were resampled in a random selection of 50 sequences per time point , even when the reported viral loads associated with these samples were relatively low . Molecular clock models are an important application of phylogenetic reconstruction . We have taken advantage of serial samples with known dates to calibrate the molecular clock , which enables us to reconstruct the evolutionary history of the HIV populations back in time [18] . For example , we extrapolated the time scale of HIV coreceptor usage evolution back to most recent common ancestors , which we estimated to have preceded the times of the first positive MT-2 assay by 2 to 5 years . There are some caveats to be aware of when interpreting estimates of from a molecular clock analysis . First , a strong selective sweep could conceivably replace the MRCA with a more recent ancestor . Published estimates of from individuals with known or estimated times of transmission , however , tend to be consistent with , or moderately overestimate , those times [29] , [39] . In addition , the high rate of recombination in HIV-1 can limit the effect of a selective sweep to a narrower interval of the genome , although it may also raise other issues related to phylogenetic reconstruction ( see below ) . Second , the nucleotide substitution models typically used for molecular clock analyses may become saturated for highly divergent lineages , causing one to underestimate the actual , although this effect has only been reported for the large-scale divergence of virus populations among hosts ( for example , dating the zoonotic origin of measles virus from rinderpest virus [40] ) . If saturation was present within hosts , it will have been ameliorated by our use of separate model parameters for the third codon position that is more susceptible to this effect [28] , [40] . Recombination can result in phylogenetic incongruence , in which different regions of a genome are related by different phylogenies . Although the sequences analyzed here were relatively short ( about 250 bp ) , we cannot rule out that within-host recombination within this interval may have interfered with accurate reconstruction of the phylogeny or ancestral sequences . For example , multiple lineages in the phylogeny reconstructed for subject DS8 accumulated the same mutations within the V3 loop ( S11G , D25K , Q10R and H13R; see Supporting Figure S5 ) that could conceivably have been transferred from the same parent lineage into different genomic backgrounds . However , this putative case of parallel evolution cannot be readily explained by recombination because many observed sequences derived from these lineages contained only intermediate subsets of these mutations , which would have required multiple recombination events at consistent breakpoints between the same lineages in a relatively short period of time . We used a Bayesian Markov chain Monte Carlo ( MCMC ) sampling procedure implemented in BEAST [26] because this is currently the best-maintained software for fitting a molecular clock phylogeny to serial samples of genetic sequence data . One of the disadvantages of this procedure , however , is that the analysis becomes unfeasible when the total number of sequences substantially exceeds 200 . This problem arises because the space of all possible trees becomes too large for an MCMC sampler to converge to the posterior distribution in a realistic amount of time . The use of serial samples can ameliorate this limit to some extent because it can constrain the range of trees to explore . We addressed this problem by limiting our analysis to only 50 randomly subsampled sequences per time-point . The resulting sample size should have been sufficient to characterize the principal trends in the dynamics of HIV evolution within these subjects; this is supported by the reproducibility of our results using a second set of random subsamples ( Supporting Figure S2 ) . Nevertheless , this is clearly a small fraction of the number of reads produced by next-generation sequencing; indeed , it is closer to the numbers yielded by conventional clonal sequencing . Thus , there remain considerable computational challenges to making full use of next-generation sequencing data from rapidly-evolving virus populations . For example , this problem may be amenable to recent innovations in sequential Monte Carlo methods , although development in this area is at an early stage [41] . It is important to note that our analysis was performed on samples from a retrospective study where the HIV infections were determined to have undergone a coreceptor switch by both phenotypic and genotypic assays . This pre-existing study criterion prevents us from drawing conclusions on the genetic determinants of whether an HIV infection will undergo a coreceptor switch . Further investigations will require a larger sample size including longitudinal samples from subjects without any positive SI or CXCR4-usage phenotype assay to identify the genetic determinants in the transmitted variant of the incidence and subsequent dynamics of the HIV coreceptor switch . Testing these hypotheses will require ‘time-stamped’ phylogenetic methods of ancestral reconstruction , including the analytical and visualization techniques we have developed in this study .
|
At the start of infection , human immunodeficiency virus ( HIV ) generally requires a specific protein receptor ( CCR5 ) on the cell surface to bind and enter the cell . In roughly half of all HIV infections , the virus population eventually switches to using a different receptor ( CXCR4 ) . This ‘HIV coreceptor switch’ is associated with an accelerated rate of progression to AIDS . Although it is not known why this switch occurs in some infections and not others , it is thought to be shaped by constraints on how HIV can evolve from one mode to another . In this study , we test this hypothesis by reconstructing the evolutionary histories of HIV within 8 patients known to have undergone an HIV coreceptor switch . Each history is recreated from samples of HIV genetic sequences that were derived from repeated blood samples by next-generation sequencing , an emerging technology that is rapidly becoming an essential tool in the study of rapidly-evolving populations such as viruses or cancerous cells . Because we have samples from different points in time , we can use models of evolution to extrapolate back in time to the ancestors of each infection . Our analysis reveals patient-specific dynamics in HIV evolution that sheds new light on the determinants of the coreceptor switch .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"sequence",
"analysis",
"medicine",
"infectious",
"diseases",
"hiv",
"evolutionary",
"modeling",
"population",
"genetics",
"biology",
"computational",
"biology",
"viral",
"diseases"
] |
2012
|
Reconstructing the Dynamics of HIV Evolution within Hosts from Serial Deep Sequence Data
|
Mycobacterium ulcerans , a slow-growing environmental bacterium , is the etiologic agent of Buruli ulcer , a necrotic skin disease . Skin lesions are caused by mycolactone , the main virulence factor of M . ulcerans , with dermonecrotic ( destruction of the skin and soft tissues ) and immunosuppressive activities . This toxin is secreted in vesicles that enhance its biological activities . Nowadays , it is well established that the main reservoir of the bacilli is localized in the aquatic environment where the bacillus may be able to colonize different niches . Here we report that plant polysaccharides stimulate M . ulcerans growth and are implicated in toxin synthesis regulation . In this study , by selecting various algal components , we have identified plant-specific carbohydrates , particularly glucose polymers , capable of stimulating M . ulcerans growth in vitro . Furthermore , we underscored for the first time culture conditions under which the polyketide toxin mycolactone , the sole virulence factor of M . ulcerans identified to date , is down-regulated . Using a quantitative proteomic approach and analyzing transcript levels by RT-qPCR , we demonstrated that its regulation is not at the transcriptional or translational levels but must involve another type of regulation . M . ulcerans produces membrane vesicles , as other mycobacterial species , in which are the mycolactone is concentrated . By transmission electron microscopy , we observed that the production of vesicles is independent from the toxin production . Concomitant with this observed decrease in mycolactone production , the production of mycobacterial siderophores known as mycobactins was enhanced . This work is the first step in the identification of the mechanisms involved in mycolactone regulation and paves the way for the discovery of putative new drug targets in the future .
Mycobacterium ulcerans , a slow-growing environmental bacterium , is the causative agent of Buruli ulcer , a severe infectious skin disease . This disease mainly occurs in humid tropical zones , especially in West African countries . Its incidence is increasing and this disease became the third mycobacteriosis after tuberculosis and leprosy has been declared as a ( re- ) emerging disease by the World Health Organization [1] , [2] . The massive tissue destructions forming large painless ulcers with undermined edges that could touch the bone tissue are induced by a bacterial toxin , the mycolactone , which remains , the main virulence factor [3] , [4] . This polyketide toxin has intense cytotoxic activity in vitro , affecting numerous cell types [5] , is thought to have immune-modulatory activities decreasing the efficiency of the immune system [6] and inhibits M . ulcerans uptake by phagocytes , which led to the interpretation that M . ulcerans was an extracellular pathogen . However , an intra-macrophage growth phase for those bacilli has been described [7] , showing that the production of a cytotoxic exotoxin can be conciliated with an intracellular lifestyle . The authors suggested that M . ulcerans probably turns off the mycolactone synthesis during the intra-macrophage growth . The genes encoding the six enzymes involved in the toxin synthesis are located on a giant plasmid [8] , [9] , [10] , [11] . As various mycobacterial species [12] , M . ulcerans produces membrane vesicles which are the main reservoir of mycolactone . Depending on its environment , these vesicles are engulfed in an extracellular matrix produced by M . ulcerans [13] . Based on the existence of foci of Buruli ulcer cases in people living in swampy or poorly drained areas in Africa , the aquatic environment has long been thought to be a reservoir for this mycobacterium [14] , [15] , [16] . However , the environmental ecology of M . ulcerans remained obscure for a long time due to the failure to isolate the bacteria in culture from the environment [13] , [17] , [18] , [19] , [20] , [21] , [22] . Nowadays , it is well established that the reservoir of M . ulcerans is localized in the aquatic environment where the bacillus may colonize different niches . Nevertheless , despite decades of research , the mode of transmission of M . ulcerans remains unclear and several hypotheses about potential reservoirs , vectors , and transmission mechanisms have been proposed [23] , [24] , [25] , [26] , [27] . A conceptual model of food web based on the role of water bugs as specific hosts and potential vectors of the bacilli has been proposed [21] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] . The role of human biting water bugs in M . ulcerans transmission was recently strengthened by the detection of viable bacilli in their saliva in an environmental study [29] . In aquatic environments bacteria are predominantly not free floating but grow as multi-species communities attached to submerged surfaces [36] , [37] . This seems to be the case for M . ulcerans which has been shown to grow as dense clusters on the surface of aquatic plants [22] . Furthermore , crude extracts from the green algae Rhizoclonium sp . and Hydrodictyon reticulatum have been shown to halve the doubling time of M . ulcerans in vitro . Aquatic plants , such as algae , are able to secrete many organic compounds , including polysaccharides , which are used by bacteria as substrates for growth [38] , [39] , [40] . While other mycobacteria have the ability to form biofilms on algal particulate matter , the growth stimulation effect seems to be specific to M . ulcerans [22] . Indeed , this stimulation has not been observed with other slow-growing mycobacteria , suggesting that M . ulcerans uses specific components of the extract to augment its growth . Based on these observations , we aimed to define the carbohydrates involved in M . ulcerans growth stimulation . This bacterium is a slow-growing microorganism with a doubling time of 3 . 5 days [41] and the finding of specific supplements capable of enhancing its growth rate would indeed be a great benefit for the scientific community and diagnostic centers . By selecting various algal components , we identified polysaccharides able to stimulate M . ulcerans growth in vitro . We describe for the first time a condition under which mycolactone , M . ulcerans' toxin , is down-regulated . Under this specific growth condition , the production of the mycobacterial siderophore , mycobactin appears to be activated .
The M . ulcerans 1615 strain ( Trudeau Collection Strain ) originally isolated from human skin biopsies from Malaysia [3] was used to study growth stimulation by various carbohydrates . Carbohydrates ( 0 . 33% ) were added to the BACTEC vials containing Middlebrook 7H12B medium as growth was monitored after inoculation of 2 . 104 bacilli as previously described [22] . For culture on solid medium , M . ulcerans 1615 strain was cultivated onto 7H10 solid medium supplemented with 10% OADC ( oleic acid , dextrose , catalase; Difco , Becton-Dickinson ) at 30°C for one month . When required , carbohydrates were added to the medium at the appropriate concentration . The M . ulcerans strains used to check the growth stimulation by starch were clinical strains isolated from Buruli patients from different endemic regions ( Table 1 ) . Among these strains , the M . ulcerans 1G897 strain was originally isolated from human skin biopsy from French Guiana [42] . Frozen aliquots of each strain were first inoculated onto Lowenstein-Jensen solid medium ( BD ) . Then , 30 days later , exponentially growing bacteria from agar plates were suspended in PBS and quantified by quantitative PCR using IS2404 primers as previously described [29] . Tubes containing BBL MGIT medium ( BD BACTEC ) supplemented with 7 . 5% of starch were completed with 0 . 8 ml of MGIT Growth Supplement/MGIT PANTA antibiotic mixture . Media were inoculated with 102 bacteria and the fluorescence reflecting the mycobacterial growth was regularly read with the BD MicroMGIT fluorescence reader . To quantify aggregation , a method relying on the measure of the optical density changes that occur in a non-shaking culture upon sedimentation of multicellular aggregates was used as previously described [43] . Briefly , bacteria were resuspended in PBS from agar plates and vortexed for two minutes to break the large aggregates . Unicellular mycobacteria were separated from aggregates by sedimentation ( 1 g ) and the optical density at 600 nm ( OD600 ) of the supernatant was measured ( Genesys20 , ThermoScientific ) over 15 minutes and compared to the OD600 of resuspended cultures where the aggregates were broken up by vortexing with glass beads . The aggregative index was calculated as the ratio between the two OD600 . Values are the mean of at least three independent experiments . For evaluation of the hydrophobic–hydrophilic character of the of M . ulcerans cell surface , adhesion to hexadecane , an apolar solvent , was employed as previously described [44] . The global electrical properties of the bacterial surfaces were assessed by measuring the electrophoretic mobility ( EM ) of bacteria that corresponds to the velocity of suspended cells under the influence of an applied electrical field [44] . Briefly , M . ulcerans strains was scraped from agar plates and resuspended in deionized water . The suspension was then diluted in different NaCl solutions with concentration ranging from 0 to 150 mM at constant pH = 7 . Electrophoretic mobility measurements of M . ulcerans were carried out using a Zeta meter ( Zetasizer Nano ZS , Malvern Instruments S . A . , Worcestershire , UK ) . All EM measurements were carried out 10 times at 25°C for each strain . Bacteria cultivated on 7H10 medium were scraped off the plates and resuspended in PBS . They were harvested by centrifugation ( 8 , 000 g , 10 min ) , and washed twice with PBS . The resulting pellets were fixed and processed for TEM as previously described [45] . To stabilize and better visualize lipids , 0 . 1% Malachite green was added to some of the samples during the aldehyde and osmium fixation steps [46] . 26-d-old M . ulcerans cultures ( 75 cm2 ) were scraped from 7H10 plates supplemented with 7 . 5% carbohydrates and resuspended in PBS . Bacterial suspensions were centrifuged ( 12 , 000 g , 30 min at 4°C ) and total lipids were extracted once with 2∶1 CHCl3/CH3OH and twice with 1∶2 CHCl3/CH3OH by incubation overnight at RT with stirring . After incubation , insoluble material was separated for arabinogalactan ( AG ) and lipoarabinomannan ( LAM ) preparation by spinning ( 3 , 500 rpm , 10 min ) , and the CHCl3/CH3OH was recovered , dried and further subjected to Folch wash . The Folch extracted total lipids were evaporated to dryness and dissolved in 200 µL of CHCl3 . The total lipids were analyzed by TLC using aluminum-backed , 250-µm silica gel F254 plates developed in five different solvent systems , 65∶25∶4 ( CHCl3/CH3OH/H2O ) , 20∶4∶0 . 5 ( CHCl3/CH3OH/H2O ) , 60∶30∶6 ( CHCl3/CH3OH/H2O ) , 98∶2 ( CHCl3/CH3OH ) and 98∶2 ( petroleum ether/ethyl acetate ) . After chromatography , TLCs were sprayed separately with CuSO4 followed by heating . Mycolactone quantification was performed from total lipids sample analysis by High Performance Liquid Chromatography ( HPLC ) as previously described [47] . The quantity of mycolactone was reported to the quantity of proteins extracted in the corresponding sample . One-way ANOVA analysis of variance was used to compare mean values between groups followed by Newman-Keuls multiple comparison test to detect significant mean differences between pairs of groups . 26-d-old M . ulcerans cultures ( 75 cm2 ) were scraped from plate and resuspended in PBS . Bacterial suspensions were centrifuged ( 12 , 000 g , 30 min at 4°C ) . The mycobacterial pellets were then resuspended in lysis buffer ( 8 M urea 100 mM HEPES , pH 8 . 0 ) . Following 20 sec sonication , bacteria were broken with 100-µm glass beads ( Sigma ) two times for 1 min at speed 30 using a bead-beater ( MM200 , Retsch ) at 4°C . Cell debris were removed by centrifugation at 8 , 000 g at 4°C and the supernatant were kept at −80°C . Proteins quantification was performed by using Micro BCA™ Protein Assay kit ( Thermo Scientific Pierce ) . Triplicate protein samples ( 400 mg ) were analyzed by NanoLC MS/MS analysis using an on-line system consisting of a micro-pump Agilent 1200 binary HPLC system ( Agilent Technologies , Palo Alto , CA ) coupled to an LTQ-Orbitrap XL hybrid mass spectrometer ( ThermoFisher , San Jose , CA ) ( see Material and Methods S1 ) . M . ulcerans cultures were scraped from plate and resuspended in PBS . An aliquot of 1 ml was mixed to 2 ml of RNAprotect Bacteria Reagent ( Qiagen ) and incubated for 5 min at RT . After centrifugation ( 12 , 000 g , 10 min at 4°C ) , pellets were stored at −80°C until use . Frozen pellets were resuspended in RLT Buffer ( Qiagen ) containing 1% β-mercaptoethanol and bacteria were broken with 100-µm glass beads ( Sigma ) two times for 1 min at speed 30 using a bead-beater ( MM200 , Retsch ) . After centrifugation at 12 , 000 g for 10 min at 4°C , the supernatants were collected and RNA were purified with a RNeasy Mini Kit ( Qiagen ) and eluted in RNase- and DNase-free water . RNA were treated with 10 U RQ1 RNase-Free DNase ( Promega ) for 30 min at 37°C , repurified using RNeasy Mini Kit , eluted in 35 µl of water . The first-strand cDNA was synthesized in 20 µl reaction volumes using 500 ng of total RNA , 500 ng of random primers ( Invitrogen ) and the SuperScript II Reverse Transcriptase ( Invitrogen ) . Quantitative real-time PCR was performed in 10 µl reaction volumes with iQ SYBR Green Supermix ( Bio-rad ) , 500 nM of primers ( Table S1 ) and 5 µl of 10 times diluted cDNA . Reactions were run on a PTC 200 thermocycler ( Bio-rad ) using the following program: 3 min at 95°C and 40 cycles of 10 s at 95°C and 1 min at 57°C . Analysis was performed utilizing the ΔΔCt method with the ppk gene as housekeeping gene as already used [48] , [49] . Bacteria grown on different carbohydrates were quantified by quantitative PCR using IS2404 primers as previously described [29] . Suspensions ( 30 µl ) containing 1 . 103 bacteria were injected subcutaneously into the tail of 6-week-old female Balb/c mice ( Janvier , Breeding Company , Le Genest , France ) . Mice tails were examined weekly over five months . For each condition , 7 mice were infected . To purify the orange pigment , approximately 2 mg of Folch extracted total lipids was subjected to preparative TLC using ( CHCl3/CH3OH; 98∶2 ) as the solvent system . The chloroform extracted material subjected to LC/MS analysis in the positive mode using the method developed by Sartain et al . [50] .
We have previously shown that crude extracts from green algae , Rhizoclonium sp . and Hydrodictyon reticulatum , halved the doubling time of M . ulcerans [22] . In order to identify the organic compounds secreted by these plants involved in this stimulation mechanism , we screened a variety of carbohydrates for their capacity to stimulate 1615 M . ulcerans strain in liquid broth . The bacterial growth was monitored with the BACTEC system with addition of various carbohydrates ( Figure 1A ) . We have chosen several monosaccharides which are retrieved in algae composition like glucose , mannose or galactose [51] , [52] . Disaccharides or polysaccharides derived from glucose were also tested . As control , a plant-unrelated disaccharide , lactose was tested . Some carbohydrates stimulated M . ulcerans growth and the growth index 500 corresponding to the midpoint of the growth curves was calculated in order to quantify this stimulation ( Figure 1B ) . Indeed , growth index 500 was ranged between 35 to 48 days with addition of maltopentaose , maltohexose , maltotetraose or maltotriose . In the control medium , this index was obtained around 70 days . Interestingly , all stimulating carbohydrates are polymers of maltose , a disaccharide composed of two units of glucose . Surprisingly , cellulose did not stimulate significantly bacterial growth . The difference between cellulose and the other glucose polymers lies on the nature of the O-glucosidic bond . Indeed , in cellulose the glucose units are β1–4-linked while the other polysaccharides derived from maltose have α-glucosidic bonds . Based on the observation that glucose polymers were able to stimulate M . ulcerans growth , we decided to check the stimulating effect of dextrin from maize starch , a polysaccharide with α-glucosidic bonds . We observed a similar stimulation with starch as with maltopentaose ( Figure S1 ) . Indeed , the ratio of growth index 500 of the maltopentaose compared to the control medium was 0 . 50 and the ratio for the starch medium was 0 . 56 , indicating a strong growth stimulation by both media . In order to check if this growth stimulation by starch is general to this species , six M . ulcerans strains isolated from Buruli ulcer patients have been grown in microMGIT ( Mycobacterium Growth Indicator Tubes ) tubes containing 7 . 5% of starch . This starch concentration is the optimal one for growth stimulation determined in our laboratory condition ( data not shown ) . In all cases , the time for bacterial cultures to become positive was significantly highly reduced in presence of starch compared to the regular medium ( Table 1 ) . The time gain ranged between 13 to 30 days . These results suggested that the growth stimulation by glucose polymers , especially starch , is not specific to one strain but is general to M . ulcerans species . In order to bring out some carbohydrates-linked modifications in the mycobacterial metabolism , large cultures of M . ulcerans were performed on 7H10 agar plates enriched with 7 . 5% maltopentaose or starch to obtain an important biomass . As controls , media complemented with monosaccharides ( glucose and maltose ) were added . Unexpectedly , we observed variations in M . ulcerans phenotype depending on culture media . Indeed , M . ulcerans colonies appeared orange on glucose- and maltose-containing plate ( Figure 2A ) . For maltopentaose and starch-rich media , wild-type yellow phenotypes , likely due to mycolactone production [9] , were noticed . Phenotypical changes observed with 7 . 5% glucose or maltose-enriched medium could be explained by variation of toxin production , as mycolactone-negative M . ulcerans strains have a white phenotype on agar plate [9] , or could result from other variations in cell wall composition . Another macroscopic difference was rapidly noticed by collecting bacterial colonies from plates . Indeed , cultures from glucose plate showed a reduced aggregation . Aggregative ability was thus quantified by measuring the decrease in optical density ( OD ) that occurs in non-agitated liquid cultures upon bacterial sedimentation [43] . Aggregates from M . ulcerans grown on 7H10 plate almost completely and rapidly sedimented ( Figure 2B ) . On the contrary , bacteria grown on glucose-containing plate displayed a dramatically decreased aggregation ( 12 . 8%±2 . 3 of bacterial aggregates were sedimented after 10 minutes against 79 . 3%±1 . 0 for the 7H10 medium ) . The aggregative ability of M . ulcerans grown on starch-containing plate was found to be indistinguishable from that of 7H10 condition . The difference in aggregative ability could reflect variation in cell wall properties ( hydrophobicity or charge ) . To investigate the possibility of modification in the mycobacterial cell wall hydrophobicity , adherence to hexadecane , an apolar solvent , was checked [44] , [53] . In three cases , high affinity to hexadecane was measured revealing elevated hydrophobic character . However , the affinity to hexadecane of M . ulcerans grown on glucose-supplemented medium was estimated to 86 . 72%±2 . 98 against 94 . 45%±1 . 52 and 93 . 52%±0 . 95 for bacteria grown on 7H10 and 7H10starch , respectively . These differences are statistically significant ( p-values<0 . 05 ) . The bacilli grown with glucose were thus slightly less hydrophobic compared to bacteria grown on 7H10 or 7H10starch . We then examined bacterial surface charge , which is a reflection of the moieties exposed on the outer envelope . The electrophoretic mobility ( EM ) , which is directly proportional to the zeta potential , which in turn reflects the overall charge that a particle acquires , was measured for M . ulcerans grown in the three conditions . The electrophoretic mobility of particles was measured at different ionic strength by changing the concentration of NaCl . The three samples exhibited a negative EM for all the ionic strengths studied ( Figure 2C ) . However , the absolute values of EM were significantly lower for 7H10 or 7H10starch bacteria than for 7H10glucose , showing that the 7H10glucose bacteria are less electronegatively charged . The phenotypical data on agar plate and cell surface properties seem to indicate that the cell wall composition was modified when mycobacteria were cultured in medium supplemented with glucose . We , therefore , sought out to establish whether such modifications induced cell wall alterations or other morphological changes . To this aim , M . ulcerans was cultured in medium supplemented with different sugars after which bacteria were fixed and processed for conventional transmission electron microscopy ( TEM ) . In normal medium , M . ulcerans is most frequently clumped in more or less large aggregates although isolated bacilli can also be found . Bacilli displayed a typical cytoplasmic membrane and cell wall , including a thin electron translucent layer beyond the peptidoglycan layer and an outermost electron-dense layer ( OL ) reminiscent of the one described by Zuber et al . [54] ( Figure 3A ) . In the case of clumped bacteria , the OL surrounded the entire clump rather than the individual bacilli ( Figure 3B ) . Another striking feature of in vitro growing M . ulcerans deserves special mention , viz . the presence of a large extra-cellular matrix ( ECM ) with which bacteria seemed to establish an intimate contact ( Figure 3B ) . The origin of the ECM is unknown but could result from shedding of the dense layer surrounding the clumps . The ECM usually appeared as densely-packed fibrillar material but it also displayed electron translucent zones that could be lipid-enriched . In order to detect the presence of lipids in the ECM , bacilli were fixed in presence of 0 . 1% Green Malachite . This dye stabilizes and stains lipids that become electron-dense and hence easy to visualize under the TEM [45] . With this method , many lipid-rich vesicles were found in the ECM . They consisted of a core of more or less densely packed lipids surrounded by a layer of very dense fibrous material resembling the dense OL surrounding the mycobacterial cell wall ( Figure 3C ) . The presence of this layer suggests that lipid-rich vesicles might originate from bacilli . A closer examination of bacilli indeed showed that lipids could be seen first between bacilli within a clump ( Figure 3B ) and then concentrated in discrete and increasingly larger blebs at the mycobacterial surface , where they were surrounded by the dense OL ( Figure 3D–F ) . Culturing of M . ulcerans in presence of starch , maltopentaose , maltose or glucose did not affect growth of mycobacteria in clumps or as individual bacilli nor induce visible alterations of the cell wall ultrastructure . Likewise , formation of the ECM and production of lipid-rich vesicles as well as the ultrastructural appearance of both structures were not affected by the presence of carbohydrates . TEM does not allow , however , to determine whether the nature and/or abundance of lipids in these vesicles varies according to the carbohydrate added to the medium . In an initial attempt to analyze changes in the cell envelope composition of M . ulcerans grown in the presence of different carbon sources , we focused on the lipoglycans , lipomannan ( LM ) and lipoarabinomannan ( LAM ) and the mycolyl-arabinogalactan-peptidoglycan ( mAGP ) complex constituting the cell wall core . These essential components insulate the bacteria from its environment and play diverse roles in the bacteria–host interactions [55] . The SDS-PAGE analysis revealed the presence of comparable amounts of LM and LAM in the various cell extracts ( Figure S2A ) . Likewise , the monosaccharide composition of mAGP complex isolated from the different bacterial cultures was compared and no significant differences were found ( Figure S2B ) . To analyze potential changes in the lipid composition of M . ulcerans grown in the presence of various sugars , total lipids were extracted and analyzed by TLC using solvent systems of different polarities . No significant changes were observed at the level of phosphatidylinositol mannosides ( PIM ) , phospholipids , phthiodiolone diphthioceranates and phenolphthiodiolone diphthioceranates ( DIP ) and triglycerides ( TAG ) ( Figures 4A and S2 ) . However , new glycolipid forms migrating in the region of TDM ( trehalose dimycolate ) and TMM ( trehalose monomycolate ) were observed in the bacteria grown in 7 . 5% glucose and maltose-enriched media ( Figure 4A ) . The compound produced by bacteria grown in 7 . 5% glucose-enriched medium and migrating at the level of TDM was identified by liquid chromatography-mass spectrometry as glucose monomycolate ( GMM ) ( Figure S4 ) . The glycolipid migrating above TMM in the bacteria grown in maltose- or , to a lesser extent , 7 . 5% starch-enriched media displayed the expected mass for either maltose monomycolate or trehalose-monomycolate ( the compounds share the exact same mass ) ( Figure S5 ) and was tentatively identified as maltose monomycolate based on its TLC migration properties . In order to check the toxin production in bacteria grown on carbohydrate-containing media , we performed HPLC quantification . As shown in Figure 5 , bacteria cultivated on 7 . 5% glucose-enriched medium displayed a drastically reduced mycolactone content compared to bacteria cultivated on 7H10 . The amount of mycolactone produced by the bacteria on glucose-rich medium was only 13 . 7% of that produced in 7H10 medium . A similar albeit less marked trend was observed in the bacteria grown on 7 . 5% maltose- or maltopentaose-enriched medium . In contrast , no statistical difference was observed in toxin production between bacteria grown on 7H10 and starch-supplemented 7H10 media . These results indicate a down-regulation of mycolactone production by some carbohydrates . To understand the regulation process of mycolactone , the transcript level of genes required in mycolactone biosynthesis has been analyzed by RT-qPCR . The expression of the mls genes required for the mycolactone core and side chain has been analyzed ( LM and KR domains ) as well as the expression of the three accessory genes ( mup038 , mup045 , mup053c ) ( Figure 6A ) . In Figure 6B , gene expressions are expressed as fold-changes relative to the 7H10 medium . In 7 . 5% glucose-enriched condition , mup038 , mup045 , mup053c and mls ( LM module ) genes were overexpressed compared to the 7H10 medium ( fold-changes of 1 . 87±0 . 28 , 4 . 97±0 . 70 , 5 . 95±1 . 50 and 9 . 51±4 . 30 , respectively ) ( Figure 6B ) . In maltose and maltopentaose media , mup053c and mls ( LM module ) genes were also overexpressed ( respective fold-changes of 2 . 09±0 . 45 and 2 . 64±0 . 86 for maltose , 3 . 88±0 . 91 and 3 . 79±0 . 99 for maltopentaose ) . Thus , paradoxically , the transcript level of the mycolactone biosynthesis genes seemed to be up-regulated under conditions where mycolactone content was found to be reduced . Quantitative proteomic approach by NanoLC MS/MS was utilized to determine the effect of carbohydrates on bacterial metabolism . The relative abundance of 1076 proteins ( 24 . 67% of total M . ulcerans proteins ) has been determined and ratios compared to the 7H10 medium were calculated ( Table S2 ) . Among the identified proteins , 822 ( 76 . 39% ) were not regulated in the five tested conditions . In the 254 remaining proteins , we selected the proteins quantified with at least two peptides and with a p-value<0 . 05 . This analysis defined 135 carbohydrate-response proteins of which 8 proteins were induced and 8 were repressed upon growth on 7 . 5% glucose , maltose , maltopentaose or starch-enriched medium . Depending on growth condition , the number of regulated proteins could vary from 27 to 111 ( Figure S6A , B and C ) . The repartition in functional class of proteins did not change in various conditions ( Figure S7A and B ) . Three proteins involved in mycolactone biosynthesis have been shown to be overproduced with glucose or maltose and , to a lesser extent , in 7 . 5% maltopentaose- or starch-enriched medium ( Table 2 ) . Indeed , Mup053c is overproduced in 7 . 5% glucose and maltose-enriched media with ratios of 7 . 67 and 3 . 26 respectively . For Mup045 , an overproduction was observed in all conditions . Proteomic data thus support the transcriptomic data and confirm the increased production of enzymes required for mycolactone synthesis: Mup053 ( in glucose and maltose media ) and Mup045 ( in glucose , maltose and maltopentaose media ) . Regarding the mlsA genes , the overexpression of the LM module in glucose medium was also confirmed by proteomics data indicating an overproduction of MlsA1 protein by a factor of 3 . 81 . These results indicated that mycolactone biosynthesis machinery seems to be overproduced in glucose or maltose-supplemented medium , while the production of its metabolite is reduced in both growth conditions . To further check if medium composition induced modification of M . ulcerans antigens production , sera from Buruli ulcer patients were screened by ELISA for reactive antibodies against proteins extract prepared from M . ulcerans grown in various conditions . Compared to the reaction against a lysate from M . ulcerans grown on 7H10 ( mean of 0 . 308 ) , the immune sera reacted significantly lower ( mean of 0 . 225 ) against a lysate from M . ulcerans grown in 7 . 5% glucose-enriched medium ( p<0 . 001 ) , indicating a difference in antigens production between these two culture conditions ( Figure S8 ) . In contrast , the reactivity obtained with a lysate of M . ulcerans grown on starch-supplemented medium ( 0 . 355 ) was significantly higher than the one for bacteria grown on 7H10 ( p<0 . 05 ) . Unfortunately , qualitative analysis by Western blot did not lead to the identification of putative antigens whose production would be regulated depending on culture conditions ( data not shown ) . In the proteomic analysis , it could be noticed that the secreted antigen 85-A FbpA is up-regulated four times in glucose or maltose conditions compared to other conditions ( 7H10 complemented or not with maltopentaose or starch ) ( Table S2 ) , meaning that variation in antigens expression occurs depending on experimental growth conditions . More accurate technics like partial fractionation of the bacterial extract or bidimensional analyses are needed to characterize the antigens detected by the antiserum . To determine whether toxin down-regulation affects bacterial ability to colonize its host , mice have been inoculated in the tail by 103 bacilli grown on 7H10 or 7 . 5% glucose- or starch-enriched medium . The inoculation site for bacterial load was examined weekly and the time of onset of clinical symptoms and symptom severity were noticed . At day 150 post-infection , all mice ( 7/7 ) inoculated with bacteria grown on 7H10 or 7 . 5% starch-enriched medium developed ulceration . On the other side , for glucose-cultured bacteria , only four mice displayed similar lesions , which is significantly different ( P = 0 . 07 in a Fisher's exact test ) . These results suggest that culture on 7 . 5% glucose-enriched medium seems to have an impact on lesions development and thus probably on host colonization . We are aware of the limitation of our data because of the small number of mice , nevertheless , these results are in accordance with the major role of mycolactone in host colonization like water bugs [30] , [31]or mammalian host [13] , [56] . Furthermore , our proteomic data indicated that bacteria cultured in 7 . 5% glucose-enriched medium overproduced some antigens like the secreted antigen 85-A FbpA ( Table S2 ) . This may induce a better recognition by the host immune system . The orange phenotype of bacteria grown on 7 . 5% glucose- or maltose-enriched medium was not due to variation of mycolactone production but to the presence of an orange compound extracted with the total lipids from M . ulcerans ( Figure 4B ) . This compound was purified by preparative TLC and identified upon LC/MS analysis as mycobactins ( Figure 7A–B ) [50] . From the quantitative proteomic analysis by nanoLC MS/MS , it appeared that three Mbt proteins encoding enzymes required for the assembly of mycobactin ( MbtB , MbtE and MbtG ) were overproduced in glucose or maltose ( Table S2 ) . MbtE was found to be overproduced by 80 . 16 and 28 . 38 in those media . For MbtG protein , no ratio could be calculated since the protein was not detected in 7H10 medium enriched or not with 7 . 5% maltopentaose or starch . Two other proteins , Mul_3635 and Mul_3636 , whose genes are located in the mbt locus , were overproduced . Finally , four proteins involved in mycobactin regulation or export ( IdeR , IrtA , EsxG and EsxH , MmpS5 and MmpL5 ) were found to be down- or up-regulated in glucose or maltose conditions ( Table S2 ) .
Based on previous studies demonstrating the stimulating effect of aquatic plant extracts on in vitro M . ulcerans growth [22] , we decided to identify the carbohydrates involved in this phenomenon . Our results showed that addition of glucose polymers in Middlebrook 7H12B medium stimulates M . ulcerans growth . The growth of M . ulcerans thus depends on carbohydrates found in plants . The growth analysis of various clinical M . ulcerans strains from three Buruli ulcer endemic regions revealed a common stimulation by starch . The delay for the detection of positive culture was indeed highly reduced for all tested strains . It is thus possible to consider starch , an inexpensive product , as a complement in culture medium to enhance M . ulcerans growth . It is noteworthy that Lowenstein-Jensen medium , used for diagnostic labs , contains potato starch ( 18 . 75 g/L ) whereas glucose ( 2 . 0 g/L ) is the carbon source in MGIT medium . The addition of starch ( 7 . 5% ) in MGIT medium would be of particular interest to facilitate the isolation of M . ulcerans from the environment . The improvement of M . ulcerans in vitro culture will be very useful for genetic manipulation of this slow-growing mycobacterium as it will reduce time to obtain transformants or recombinant clones . Our experiments on carbohydrates-dependent growth have led to the identification of growth conditions under which the mycobacterial toxin is down-regulated . Indeed , the mycolactone production was reduced on 7 . 5% maltopentaose- , maltose- and glucose-enriched medium from 40 to 85% . To our best knowledge , this is the first time that such a regulation is described . By electron microscopy , vesicles have been observed in all tested culture conditions . Smaller vesicles produced by M . ulcerans have been already observed by scanning electron microscopy and were shown to be the main reservoir of the mycobacterial toxin [13] . Our observations suggest that ECM and vesicles productions are not dependent on mycolactone biosynthesis by M . ulcerans . This is in agreement with previous isolation of ECM and vesicles from mup045 mutant which does not produce mycolactone [13] . It was also shown that ECM is undetectable in presence of algal extracts [13] . The regulation of toxin production by plant carbohydrates leads to the hypothesis that mycolactone production is probably regulated by environmental signals such as compounds produced by aquatic plants . The decrease in mycolactone production was not due to a defect in the production of the enzymes required for its biosynthesis . In fact the toxin enzymatic machinery was even overproduced in 7 . 5% glucose-enriched medium according to the quantitative proteomic study corroborated by the quantification of the transcriptional level of the corresponding genes . Different hypothesis could explain the toxin regulation mechanism . ( i ) One may hypothesize that a post-translational modification is involved in the regulation of the activity of enzymes involved in mycolactone biosynthesis . ( ii ) It is also possible that toxin degradation occurs . ( iii ) Another explanation could reside in the existence of a membrane-anchored enzymatic megacomplex required for mycolactone biosynthesis , as previously shown for other polyketide metabolites from different species [57] , [58] , [59] . In the case of mycolactone , the MlsA1 and MlsB , as well as Mup053c have been identified in membrane fraction or in vesicles [13] , [60] . It could be hypothesize that a mycolactone biosynthetic megacomplex exists and is disassembled due to a repression of some proteins involved in the complex scaffold . ( iv ) A limitation of substrate for the synthesis of mycolactone could also take place . The up-regulation of the toxin enzymatic machinery could take place to counteract the toxin decrease . From an interesting point of view , the decrease in mycolactone production is correlated with the production of the mycobacterial siderophore , the mycobactin . Mycobactins are high-affinity iron-binding molecules and the principle iron acquisition molecules . Iron is an indispensable nutrient for almost all organisms . It is required for the activity of enzymes that are involved in vital cellular functions ranging from respiration to DNA replication [61] . Our results demonstrated that M . ulcerans is able to produce this siderophore and confirmed a bioinformatical work showing conservation of mbt genes encoding enzymes involved in mycobactin biosynthesis in M . ulcerans [62] . We showed that some Mbt enzymes are overproduced in 7 . 5% glucose-enriched medium . The repression of IdeR , the iron-dependent negative regulator , fits this observation . In addition , other proteins involved in iron metabolism are overproduced . This is the case for IrtA ( Mul_3902 ) , an iron regulated transporters of siderophores , Esx-3 encoding EsxG ( Mul_1209 ) and EsxH ( Mul_1210 ) , a specialized secretion system required for mycobactin-mediated iron acquisition and for survival during infection , and MmpS5/MmpL5 required for siderophore export ( Table S2 ) [63] , [64] , [65] , [66] . The latest proteins were previously shown to be IdeR-independent and iron-repressed [67] . It is likely that the export machineries of mycolactone and mycobactins act as scaffolds for their respective biosynthetic machineries . Coupled elongation and export machineries are indeed not uncommon in the biogenesis of mycobacterial polyketide-derived lipids and abolition of their biosynthesis caused by defects in the expression of the export machinery or failure of the biosynthetic enzymes to interact with the transporters have been reported in the case of phthiocerol dimycocerosates and glycopeptidolipids [57] , [58] . If one hypothesizes that mycolactone and mycobactins and their respective biosynthetic complexes compete for the same translocation machinery ( like MmpL5/MmpS5 , recently shown to be required for mycobactin export in M . tuberculosis [66] ) , then this may explain the disappearance of mycolactone when mycobactin production is induced . It was shown that production of siderophores is a major point for the regulation of iron uptake . These molecules are produced exclusively under iron limitation when the bacterium needs them , and increasing the concentration of available iron turns off their synthesis [61] . Intriguingly , it appears from our experimental work that iron metabolism is activated in glucose or maltose-rich medium , without the influence of the iron concentration which has never been modified . This suggests that the iron regulon is activated by other signal that iron concentration . It may be part of another regulon . It could be noticed that in the proteomic analysis , no sigma factors were found to be regulated in glucose-rich medium . We have thus shown that siderophore overproduction is correlated to the toxin down-regulation . Therefore , one question emerges: are their production biochemically linked or are they oppositely regulated by the same environmental signals ? Indeed , we have shown that some carbohydrates like maltopentaose or starch , both derived from maltose , are able to stimulate M . ulcerans growth . On the opposite , some carbohydrates are able to reduce toxin production and to induce iron metabolism . These observations suggest a regulation of M . ulcerans metabolism by environmental signals . In some conditions , high multiplicity and toxin production could confer capacity to M . ulcerans for host colonization . In other conditions , M . ulcerans adapts its metabolism by reducing mycolactone production and increasing iron metabolism . Whereas not required for planktonic growth in M . smegmatis , iron is essential for biofilm development [68] . We could speculate that our in vitro culture condition mimics environmental conditions in which M . ulcerans does not need to produce toxin to survive and activate the iron pathway to colonize specific niches in their environment . One can also ask what the role of iron concentrations in toxin metabolism is . Indeed , as mycobactine production is stimulated in low iron concentration , its production should be limited in environments rich in iron and the production of mycolactone would be increased if the biosynthesis of these two metabolites is linked . In the infectious cycle of M . ulcerans , the high concentration of iron in human blood could induce reduction in siderophore production and increase of toxin production by bacteria from the early to the late stages of infection . This hypothesis could explain the fact that M . ulcerans infection occurs by environmental contact such as insect bite and that no human-to-human transmission had been observed . On the other hand , it is possible that the in vitro medium enrichment by starch mimics the bacterial environment in cutaneous ulcer . It has been described that M . ulcerans is able to colonize salivary glands of Naucoris cimicoides from Naucoridae family [30] and is present in saliva of Appasus sp . from Belostomatidae family [29] . It is well known that insect salivary glands contain α-glucosidases and α-amylases that initiate the digestion of carbohydrates . The activity of both type of enzymes in salivary glands of B . lutarium ( Belostomatidae family ) has demonstrated [69] . Their activity leads to high level of glucose . It is thus possible that such glucose concentration could inhibit the mycolactone biosynthesis when M . ulcerans colonize this habitat , explaining the absence of tissue damage mediated by the cytotoxic activity of the mycolactone whereas the insect cells are sensitive to the toxin [30] . The study of mycolactone regulation is essential to deepen our knowledge on the mechanisms of M . ulcerans virulence . Unfortunately , some tools to quantify this polyketide toxin sorely lacking . Nevertheless , this work is the first step in mycolactone regulation understanding . Indeed , for the first time , we have identified culture conditions influencing its production . Those media enriched with carbohydrates could reflect ideal conditions to study the mechanisms of mycolactone regulation and its role in the ecology of M . ulcerans . The understanding of such mechanisms is a key issue for a better comprehension of Buruli ulcer physiopathology and the identification of putative therapeutic targets .
|
Mycolactone , a polyketide cytotoxic toxin , is the key virulence factor responsible for large skin ulcers in Buruli ulcer . This disease , mainly occurring in humid tropical zones , especially in West African countries , is due to infection by Mycobacterium ulcerans , a slow-growing environmental mycobacterium . The toxin has destructive effects on the skin , soft tissues and bones . In this study , we brought out for the first time that addition of specific carbohydrate to culture medium induces the down-regulation of the toxin . Furthermore , this decrease in toxin production is correlated with the activation of the iron acquisition pathway , especially by siderophore production . These results show that M . ulcerans adapts its metabolism to culture conditions , which probably reflect its adaptation in its natural habitats . This work is the first step in toxin regulation understanding which is a key issue for a better comprehension of Buruli ulcer physiopathology and the identification of putative therapeutic targets .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Regulation of Mycolactone, the Mycobacterium ulcerans Toxin, Depends on Nutrient Source
|
Delivery of microbial products into the mammalian cell cytosol by bacterial secretion systems is a strong stimulus for triggering pro-inflammatory host responses . Here we show that Salmonella enterica serovar Typhi ( S . Typhi ) , the causative agent of typhoid fever , tightly regulates expression of the invasion-associated type III secretion system ( T3SS-1 ) and thus fails to activate these innate immune signaling pathways . The S . Typhi regulatory protein TviA rapidly repressed T3SS-1 expression , thereby preventing RAC1-dependent , RIP2-dependent activation of NF-κB in epithelial cells . Heterologous expression of TviA in S . enterica serovar Typhimurium ( S . Typhimurium ) suppressed T3SS-1-dependent inflammatory responses generated early after infection in animal models of gastroenteritis . These results suggest that S . Typhi reduces intestinal inflammation by limiting the induction of pathogen-induced processes through regulation of virulence gene expression .
One function of the innate immune system in the intestinal tract is to generate temporary inflammatory responses against invasive enteric pathogens while avoiding detrimental overreaction against harmless commensal bacteria under homeostatic conditions . In contrast to commensal microbes , pathogenic microbes express an array of virulence factors to manipulate host cell functions . Pathogen-induced processes , also known as patterns of pathogenesis [1] , activate specific pathways of the innate immune system , enabling the host to distinguish virulent microbes from ones with lower disease-causing potential . By detecting pathogen-induced processes the host can escalate innate immune responses to levels that are appropriate to the threat [2] . Salmonella enterica serovar Typhimurium ( S . Typhimurium ) , an invasive enteric pathogen associated with human gastroenteritis , triggers acute intestinal inflammation in the terminal ileum and colon , thereby producing symptoms of diarrhea and abdominal pain within less than one day after ingestion [3] . The inflammatory infiltrate in the affected intestinal tissue is dominated by neutrophils [4] , [5] . Similarly , neutrophils are the primary cell type in the stool during acute illness [6]–[8] . In contrast , individuals infected with serovar Typhi ( S . Typhi ) develop a febrile illness ( typhoid fever ) with systemic dissemination of the organism . In contrast to Salmonella-induced gastroenteritis , only a third of patients develop diarrhea that is characterized by a dominance of mononuclear cells in the stool [6] . The dominant cell type in intestinal infiltrates is mononuclear , while neutrophils are infrequent [9]–[11] . Unlike S . Typhi , interaction of S . Typhimurium with intestinal model epithelia induces hepoxilin A3-dependent transmigration of neutrophils [12] . Moreover , infection of human colonic tissue explants with S . Typhimurium results in the increased production of the neutrophil-attracting chemokine IL-8 , while S . Typhi does not elicit this response [13] . These observations suggest that invasion of the intestinal mucosa by S . Typhimurium is accompanied by a rapid escalation of host responses leading to acute , purulent inflammation , while S . Typhi elicits little intestinal inflammation during early stages of infection , however the molecular mechanisms underlying these apparent differences are poorly defined . One pathogen-induced processes that triggers pro-inflammatory immune responses is the transfer of bacterial molecules into the host cell cytosol by secretion systems . The invasion-associated type III secretion system ( T3SS-1 ) expressed by all Salmonella serovars and delivers effector proteins into the cytosol of epithelial cells [14] . A subset of these translocated effector proteins activate Rho-family GTPases [15]–[18] , thereby triggering alterations in the host cell cytoskeleton that result in bacterial invasion of epithelial cells [19] . Excessive stimulation of Rho-family GTPases activates the transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells ( NF-κB ) and promotes the subsequent release of proinflammatory cytokines and chemokines [15] , [20] , [21] . In a bovine model of S . Typhimurium-induced gastroenteritis , the rapid induction of intestinal inflammation and diarrhea requires the T3SS-1 apparatus as well as the effector proteins SipA , SopA , SopB , SopD , and SopE2 [22]–[24] . Similarly , in a murine model of Salmonella induced colitis , SipA , SopE and SopE2 can independently induce intestinal inflammation [25] and mutants lacking a functional T3SS-1 are unable to initiate neutrophil recruitment to the intestinal mucosa during early infection [25] , [26] . These findings indicate that T3SS-1-mediated effector translocation induces innate immune responses during S . Typhimurium-induced colitis . Similar to S . Typhimurium , invasion of cultured intestinal epithelial cells by S . Typhi is mediated by the T3SS-1 [27] . Replacement of S . Typhimurium T3SS-1 effector proteins with their S . Typhi orthologues does not attenuate inflammatory responses elicited by S . Typhimurium in the intestinal mucosa of calves [28] , demonstrating that S . Typhi T3SS-1 effector proteins can exhibit intrinsic pro-inflammatory properties in vivo . Thus , the molecular basis for the absence of T3SS-1-dependent innate immune responses early during S . Typhi infection remains unclear .
To study the induction of pro-inflammatory signaling pathways upon infection with S . Typhimurium and S . Typhi , we employed a human epithelial cell line permanently transfected with a NF-κB-dependent luciferase reporter ( HeLa 57A ) [29] . Infection with the S . Typhimurium wild-type strain SL1344 resulted in a significant increase ( 7-fold; P<0 . 01 ) in luciferase activity compared to mock-infected cells ( Fig . 1A ) , while a derivative of S . Typhimurium SL1344 carrying a mutation in the T3SS-1 apparatus gene invA ( SW767 ) did not elicit NF-κB signaling [20] , [30] . In contrast to the S . Typhimurium wild type , the S . Typhi wild-type strain Ty2 failed to trigger NF-κB activation ( Fig . 1A ) , suggesting that S . Typhi is a poor activator of T3SS-1-dependent inflammatory processes in human epithelial cells . The T3SS-1 mediates invasion of non-phagocytic cells . S . Typhi has been reported to differ from S . Typhimurium with regards to invasion of human epithelial cells [31]–[33] , thus raising the possibility that the observed differential activation of the NF-κB signaling pathway could be due to varying degrees of invasiveness . To test this hypothesis , HeLa cells were infected with S . Typhimurium and S . Typhi strains and a gentamicin protection assay was performed ( Fig . 1B ) . The S . Typhimurium wild type SL1344 and the S . Typhi wild type Ty2 were recovered in similar numbers , while the respective isogenic invA-deficient mutants displayed significantly reduced invasiveness . T3SS-1 activity has to two functional consequences: manipulation of host signaling pathways and subsequent bacterial uptake . To discern between effects mediated directly by the T3SS-1 or indirectly by increasing the intracellular bacterial load , we next sought to reinstate invasiveness of the S . Typhimurium invA mutant without restoring T3SS-1 function . Expression of the Yersinia pseudotuberculosis invasin , encoded by the plasmid pRI203 , raised invasiveness of the S . Typhimurium invA mutant comparable to the wild type strain ( Fig . 1B ) , but failed to restore the ability to induce NF-κB activation in epithelial cells ( Fig . 1C ) [34] . Taken together , these observations indicate that immune evasion by S . Typhi did not directly correlate with the intracellular bacterial load or invasiveness . Despite causing disparate disease entities , the genomes of S . Typhimurium and S . Typhi display remarkable similarity . Chromosomal DNA sequences of both serovars are highly syntenic , with mostly minor inversions , deletions and insertions [35] , [36] . One DNA region that is present in S . Typhi but absent from S . Typhimurium is the Salmonella pathogenicity island 7 ( SPI-7 ) . Situated within SPI-7 is the viaB locus , an operon encoding regulatory ( tviA ) , biosynthesis ( tviBCDE ) , and export ( vexABCDE ) genes involved in the production of the virulence ( Vi ) capsular polysaccharide of S . Typhi [37] ( Fig . S1A ) . The viaB locus has been shown to suppress Toll-like receptor ( TLR ) signaling pathways [13] , [38] , [39] . We therefore explored the contribution of the viaB locus on diminishing NF-κB activation in epithelial cells ( Fig . 1D and S1B ) . Deletion of the entire viaB locus in S . Typhi ( ΔviaB mutant; SW347 ) markedly increased the ability to activate NF-κB in epithelial HeLa cells ( P<0 . 001 ) . Akin to the findings with S . Typhimurium , NF-κB signaling induced by the S . Typhi viaB mutant was independent of invasiveness ( Fig . S1C ) but required a functional T3SS-1 since inactivation of invA in the viaB mutant background ( ΔviaB invA mutant , STY4 ) completely abolished luciferase activity ( P<0 . 001 ) ( Fig . S1D ) . These results supported the idea that the viaB locus attenuates T3SS-1-induced , pro-inflammatory signaling pathways in human epithelial cells . The viaB locus has been shown to alter interaction of S . Typhi with host cells through multiple distinct mechanisms ( reviewed in [40] ) . The Vi capsular polysaccharide prevents complement deposition , phagocytosis , and TLR4 activation , while the regulatory protein TviA is known to dampen TLR5 signaling . We therefore wanted to discern whether the absence of NF-κB signaling in human epithelial cells is due to the production of the Vi capsule or due to altered gene expression mediated by TviA . To this end , the tviA gene cloned into a low copy number plasmid ( pTVIA1 ) was introduced into a S . Typhi viaB mutant ( STY2 ) . Expression of tviA under control of the native promoter significantly lowered NF-κB activation ( P<0 . 01 ) in comparison to cells infected with the S . Typhi viaB mutant carrying the empty vector control ( pWSK29 ) . Remarkably , expression of tviA reduced inflammatory responses to levels comparable to the S . Typhi wild-type strain ( Fig . 1D and S1B ) , suggesting that the regulatory protein TviA is involved in dampening inflammatory responses in cultured human epithelial cells . We had recently demonstrated that a S . Typhimurium strain carrying the S . Typhi viaB locus on a plasmid elicits less mucosal inflammation in a bovine ligated ileal loop model than the isogenic S . Typhimurium wild type ATCC14028 [38] , raising the possibility that TviA might be involved in suppressing inflammatory responses in vivo . To delineate the relative contribution of the Vi capsule and the regulator TviA to reducing inflammatory responses in the bovine ligated ileal loop model [23] , we repeated these studies with derivatives of S . Typhimurium strain ATCC 14028 in which the phoN gene in the chromosome had been replaced with the entire S . Typhi viaB locus ( phoN::viaB mutant , TH170 ) or the tviA gene only ( phoN::tviA mutant , SW474 ) . In these strains , transcription of tviA and the downstream genes is solely controlled by the native S . Typhi promoter [41] , [42] . This strategy was chosen to ensure that attenuation of intestinal inflammation in this model was not caused by introduction of the viaB locus on a multi-copy plasmid [38] . We compared the phoN::viaB mutant and the phoN::tviA mutant to a strain carrying an antibiotic resistance gene inserted chromosomally in the phoN gene ( phoN mutant , AJB715 ) . The phoN::viaB mutant , the phoN::tviA mutant , and the isogenic phoN mutant were recovered in equal numbers from gentamycin-treated tissue samples five hours after inoculation ( Fig . 2A ) , suggesting that neither the tviA gene nor the entire viaB locus interfered with tissue invasion . Consistent with our previous observations [38] , the phoN::viaB mutant elicited less fluid accumulation ( Fig . 2B ) and less pathological changes in the mucosa ( Fig . 2C and D ) than the isogenic phoN mutant . Remarkably , expression of tviA alone ( phoN::tviA mutant ) significantly reduced fluid accumulation and inflammation compared to the phoN mutant ( P<0 . 01 ) . The responses elicited by the phoN::tviA mutant and the phoN::viaB mutant were indistinguishable , suggesting that the viaB-mediated attenuation of inflammatory responses five hours after inoculation of bovine ligated ileal loops with S . Typhimurium was mostly attributable to the action of the TviA regulatory protein . Taken together , these data suggested that gene regulation mediated by TviA could dampen inflammatory processes in vivo . A functional T3SS-1 is required for the induction of intestinal host responses in cattle [22] , [24] , [43] . A S . Typhimurium strain carrying a mutation in the T3SS-1 apparatus gene invA ( invA phoN mutant , SW737 ) was significantly less invasive than a phoN mutant ( Fig . 2A ) ( P<0 . 05 ) . Interestingly , inactivation of invA ( invA phoN mutant ) reduced fluid accumulation ( Fig . 2B ) and intestinal inflammation ( Fig . 2C and D ) by a magnitude that was similar to that observed for the phoN::tviA mutant . This finding was consistent with the idea that TviA reduces T3SS-1-dependent host responses in vivo , prompting us to further investigate the mechanism by which TviA inhibits T3SS-1 gene expression . TviA is a key activator of the tviBCDEvexABCDE operon but can also control transcription of genes outside its own operon ( Fig . S2A ) . Expression of TviA results in diminished motility and flagellin secretion due to downregulation of the flagellar regulon by repressing transcription of the flhDC genes [42] , [44] . FlhDC , the master regulator of flagellar gene expression , activates transcription of class II flagellar genes , such as fliA and fliZ [45] , [46] . FliA is a positive regulator of class III flagellar genes , including flagellin [45] , [47] . To determine whether reduced motility or diminished flagellin production could account for the TviA-dependent reduction in NF-κB activation , we inactivated the fliC gene encoding the sole flagellin of the monophasic serovar Typhi , thereby rendering strains carrying these mutations aflagellate and non-motile . Deletion of the entire viaB operon ( ΔviaB ΔfliC mutant , SW483 ) in the fliC background ( ΔfliC mutant , SW359 ) significantly increased NF-κB signaling in infected HeLa and HEK293 epithelial cells ( Fig . S2B and S2C ) . Expression of TviA from a plasmid ( pTVIA1 ) in a viaB fliC mutant reduced luciferase activity to levels comparable to the fliC mutant ( Fig . S2B and S2C ) , demonstrating that TviA-dependent repression of NF-κB activation was flagellin-independent . Gene expression profiling experiments suggest that TviA affects transcription of T3SS-1 genes through the following signaling cascade [42]: By repressing transcription of flhDC , TviA downregulates expression of FliZ . The regulatory protein FliZ is an activator of hilA [48]–[50] , the master regulator of T3SS-1 genes [51] , [52] , thus placing T3SS-1 gene expression under negative control of TviA ( Fig . S2A ) . We therefore analyzed the effect of TviA on the transcription of a subset of regulatory , structural , and effector proteins in S . Typhi ( Fig . S3 ) . Consistent with previous findings , deletion of the Vi capsule biosynthesis genes alone ( ΔtviB-vexE mutant , SW74 ) did not alter transcription of T3SS-1 genes [42] , [44] . In contrast , concomitant deletion of tviA and capsule biosynthesis genes ( ΔviaB mutant , SW347 ) significantly enhanced transcription of the regulatory genes flhD , hilA , and invF , the structural component gene prgH , as well as the effector genes sipA and sopE ( Fig . S3 ) . We next determined which T3SS-1 effector proteins contributed to pro-inflammatory responses elicited by S . Typhimurium and S . Typhi . Previous work has demonstrated that SopE , SopE2 , SopB , and SipA contribute to NF-κB activation in epithelial cells [15]–[17] , [53] . The bacteriophage-encoded sopE gene is present in S . Typhi Ty2 but absent from S . Typhimurium strain ATCC 14028 . To better model the contribution of TviA on attenuating T3SS-1-induced host responses , we chose to continue our studies using the S . Typhimurium strain SL1344 , an isolate that carries the sopE gene . Consistent with previous reports [15]–[17] , [53] , we found that simultaneous inactivation of sopE , sopE2 , sopB , and sipA ( sopE sopE2 sopB sipA mutant , SW868 ) reduced the ability of the S . Typhimurium strain SL1344 to induce NF-κB activation to levels observed in an isogenic S . Typhimurium strain unable to translocate effector proteins ( invA mutant; SW767 ) ( Fig . S4 ) . A S . Typhimurium strain only expressing SopE ( sopE2 sopB sipA mutant , SW867 ) elicited considerable NF-κB activation . A moderate NF-κB activation was also observed with S . Typhimurium strains only expressing SopB ( sopE sopE2 sipA mutant , SW972 ) or only expressing SipA ( sopE sopE2 sopB mutant , SW940 ) ( Fig . S4 ) . Essentially no response was observed in cells infected with a SL1344 derivative that only expressed SopE2 ( sopE sopA sopB mutant , SW973 ) . Collectively , these data suggested that SopE was the most potent inducer of pro-inflammatory responses in this tissue culture model , while the contributions of SopB and SipA were more modest . We next determined the potential contribution of the S . Typhi orthologues of these effectors to the induction of NF-κB signaling in the absence of the tviA gene ( S . Typhi ΔviaB mutant , SW347 ) ( Fig . 3 ) . The sopE2 gene is a pseudogene in S . Typhi Ty2 and was not further analyzed . Concomitant inactivation of sopE , sipA and sopB in the S . Typhi viaB mutant ( sopB sipA sopE ΔviaB mutant , SW1217 ) completely abolished NF-κB-driven luciferase activity ( Fig . 3 ) . This indicated that , akin to the findings with the S . Typhimurium strain SL1344 , SopE , SipA , and SopB are critical for the induction of inflammatory responses in epithelial cells upon infection with S . Typhi . A S . Typhi viaB sopB sipA mutant ( SW1211 ) elicited pronounced NF-κB activation , but a more modest NF-κB activation was also observed with the S . Typhi viaB sopE sipA mutant ( SW1214 ) and the viaB sopE sopB mutant ( SW1216 ) ( Fig . 3 ) . These data suggested that SopE was the most potent inducer of pro-inflammatory responses in S . Typhi strains lacking the tviA gene while SopB and SipA contributed moderately . In contrast , diminished NF-κB activation was observed with S . Typhi tviB-vexE mutant ( carrying the tviA gene ) and its derivatives ( Fig . 3 ) . This intricate comparison between derivatives of the viaB mutant and the tviB-vexE mutant allowed us to preclude any confounding effects expression of the Vi antigen might have on gene regulation: both the viaB mutant and the tviB-vexE mutant are non-encapsulated and only differ in their capability of expressing tviA . In contrast , a simple tviA mutant would exhibit a pleiotropic effect , i . e . it would lack the regulatory TviA protein but at the same time exhibit virtually no production of the Vi antigen [37] . Collectively , these data suggested that TviA-mediated gene regulation reduced T3SS-1 effector-triggered NF-κB activation . Since SopE triggered the most pronounced host responses in the absence of tviA , we focused our further analysis on this signaling pathway . Mechanistic studies in cultured epithelial cells have revealed that the bacterial guanine nucleotide exchange factor ( GEF ) SopE activates the Rho-family GTPase Ras-related C3 botulinum toxin substrate 1 ( RAC1 ) [15] . Excessive stimulation of RAC1 by bacterial effectors is sensed by the nucleotide-binding oligomerization domain-containing protein 1 ( NOD1 ) [30] . Activation of NOD1 leads to phosphorylation of the receptor-interacting serine/threonine-protein kinase 2 ( RIP2 ) and activation of NF-κB signaling in epithelial cells [15] , [20] , [30] , [54] . The NOD1/2 signaling pathway in HeLa cells can also be triggered by SipA [34] , although this pathway plays a lesser role in the SopE-encoding strain SL1344 ( Fig . S4 ) . Taken together , these findings raised the possibility that TviA-mediated downregulation of SopE allows S . Typhi to abate immune recognition by the RAC1-NOD1/2-RIP2 signaling pathway . To test this hypothesis , we abrogated RAC1 and RIP2 signaling by either ectopically expressing a dominant negative form of RAC1 ( RAC1-DN ) [30] , [55] or by treating cells with the RIP2 inhibitor ( SB203580 ) ( Fig . 4 ) . Consistent with previous reports , ectopic expression of a GFP-SopE fusion protein alone was sufficient to induce NF-κB activation while no upregulation of this signaling pathway was observed with a GFP-SopE construct lacking GEF activity ( GFP-SopE G168A ) [30] , [56] . Simultaneous expression of the GFP-SopE fusion protein and a RAC1-DN construct abrogated NF-κB signaling ( Fig . 4A ) . Infection of HeLa cells with the S . Typhi wild type or the T3SS-1-deficient viaB invA mutant did not result in a statistically significant increase in NF-κB activation and abrogation of RAC1 or RIP2 signaling did not further impact signaling ( Fig . 4A , B , and C ) . In marked contrast , infection with the S . Typhi viaB mutant led to a substantial upregulation of NF-κB-driven responses . Abrogation of RAC1 or RIP2 activity significantly blunted the induction of NF-κB responses in cells infected with the viaB mutant . Moreover , NF-κB activation in cells infected with a viaB sopB sipA mutant was inhibited when cells were transfected with a plasmid construct encoding RAC1-DN ( Fig . 4C ) , suggesting that SopE , translocated into host cells in the absence of TviA , could activate NF-κB signaling in a RAC1-dependent manner . Treatment with the RIP2 inhibitor did not impact T3SS-1-mediated invasion of S . Typhi strains towards epithelial cells ( Fig . S5 ) , excluding the possibility that the RIP2 inhibitor inadvertently interfered with the function of the T3SS-1 machinery . Collectively , these data supported the idea that TviA restricts activation of the RAC1-NOD1/2-RIP2 signaling pathway in S . Typhi-infected epithelial cells . In addition to repressing T3SS-1 genes , TviA also suppresses flagella expression ( Fig . S2A ) [57] . Flagellin is known to induce pro-inflammatory responses by activating TLR5 [58] and the NLRC4- ( nucleotide-binding oligomerization domain [NOD]-like receptor [NLR] family caspase-associated recruitment domain [CARD]-containing protein 4- ) inflammasome [59] , [60] . While our initial experiments in the bovine ligated ileal loop model suggest that TviA could mitigate mucosal inflammation ( Fig . 2 ) , it is conceivable that TviA-mediated gene regulation of flagellar biosynthesis could have affected flagellin-dependent innate immune pathways . To better study consequences of the expression of TviA on the RAC1-NOD1/2-RIP2 signaling pathway in an animal model , we therefore generated a phoN::tviA mutant in the S . Typhimurium SL1344 background ( SW760 ) . Akin to the findings with S . Typhi , expression of TviA in S . Typhimurium reduced transcription of T3SS-1 genes ( Fig . S3 ) and the phoN::tviA mutant elicited significantly less ( P<0 . 05 ) NF-κB activation than the phoN control strain ( Fig . 5A and S6 ) . We next introduced the tviA gene into SL1344 derivatives that only expressed the most potent inducers of the NF-κB pathway , SipA ( sopE sopE2 sopB phoN::tviA mutant; SW809 ) and SopE ( sopE2 sopB sipA phoN::tviA mutant; SW807 ) . Upon infection of HeLa cells ( Fig . 5B ) , strains carrying the phoN::tviA insertion elicited significantly less luciferase activity than the respective phoN mutants ( P<0 . 01 ) , indicating that TviA is able to reduce the NF-κB activation elicited by the S . Typhimurium orthologues of SopE and SipA . Inhibition of RIP2 significantly reduced NF-κB activation levels induced by the wild-type strain or the phoN mutant ( Fig . 5C ) . The modest response induced by the phoN::tviA mutant was further blunted by inhibition of RIP2 signaling ( P<0 . 05 ) ( Fig . 5C ) , suggesting that TviA-mediated regulation of T3SS-1 is partially able to avoid induction of the NOD1/2-RIP2 pathway in vitro . To exclude any effects of TviA on flagellin-dependent pathways , we introduced the phoN::tviA mutation into a non-motile S . Typhimurium strain lacking phase 1 and 2 flagellins , FliC and FljB ( fliC fljB mutant , SW762 ) ( Fig . 6A ) . Both the fliC fljB mutant and the fliC fljB phoN mutant ( SW793 ) elicited significant levels of NF-κB activation in cultured epithelial cells ( Fig . 6A ) , while this response was greatly reduced in cells infected with the fliC fljB phoN::tviA mutant ( SW764 ) . Inactivation of the essential T3SS-1 gene invA completely abolished the ability to induce NF-κB signaling ( Fig . 6A ) . To directly assess the ability of TviA to impede inflammatory processes in the intestinal mucosa , we used the Streptomycin pre-treated mouse model [61] . In this model , detection of cytosolic access by the S . Typhimurium T3SS-1 through the NOD1/2 signaling pathway contributes to intestinal inflammation early during infection [30] , [34] , [62] . Compared to mock infected mice , transcript levels of the pro-inflammatory genes Nos2 , encoding inducible nitric oxide synthase ( iNOS ) , and Tnfa , encoding tumor necrosis factor ( TNF ) -α , were significantly ( P<0 . 05 ) elevated in the cecal mucosa at 12 hours after infection with a non-flagellated S . Typhimurium phoN fliC fljB mutant ( SW793 ) ( Fig . 6B and C ) . Introduction of the S . Typhi tviA gene into a S . Typhimurium fliC fljB mutant ( phoN::tviA fliC fljB mutant , SW764 ) significantly ( P<0 . 05 ) reduced pro-inflammatory gene expression ( Fig . 6B and 6C ) , but not bacterial numbers recovered from intestinal contents or Peyer's patches ( Fig . S7 ) . Inflammatory responses observed in the cecal mucosa at this early time point were T3SS-1-dependent , because introduction of a mutation in invA abrogated the ability of S . Typhimurium to elicit pro-inflammatory gene expression . Collectively , these data suggested that TviA represses T3SS-1-dependent , early inflammatory responses in vivo through a flagellin-independent mechanism .
S . Typhi invades the intestinal mucosa without triggering the massive neutrophil influx observed during gastroenteritis caused by non-typhoidal serovars . Here we show that one mechanism for attenuating host responses is a TviA-mediated repression of T3SS-1 , a virulence factor known to induce potent inflammatory host responses . Effector molecules translocated by the T3SS-1 into the host cell cytosol activate Rho-family GTPases [15]–[17] . The activation of Rho-family GTPases is a pathogen-induced process that is sensed by NOD1 [21] , [30] , which ultimately results in the activation of pro-inflammatory responses in vitro [15] , [20] , [54] and in vivo [30] , [62] . However , S . Typhi requires a functional T3SS-1 to invade the intestinal epithelium during infection [27] . Our data suggest that S . Typhi might have evolved to invade the intestinal epithelium without inducing a potent antibacterial inflammatory response by regulating T3SS-1 expression in a TviA-dependent manner . Osmoregulation prevents expression of TviA in the intestinal lumen , which renders S . Typhi invasive [42] ( Fig . 2A ) . However , TviA expression is rapidly upregulated upon entry into tissue [63] , resulting in repression of T3SS-1 and flagella expression while biosynthesis of the Vi capsule is induced [42] , [44] . Here we show that TviA prevented NF-κB activation in epithelial cells by reducing T3SS-1-dependent activation of RAC1 . Furthermore , the TviA-mediated reduction of T3SS-1-dependent inflammatory responses elicited at early time points in animal models was independent of flagella and the Vi capsule . These data support the hypothesis that TviA attenuates inflammation because it rapidly turns off T3SS-1 expression upon entry into tissue , thereby concealing a pathogen-induced process from the host . Bovine ligated ileal loops are suited to model the initial 12 hours of host pathogen interaction , a time period during which inflammatory responses are largely T3SS-1-dependent [22] , [23] , [64] . Similarly , in the mouse colitis model , inflammatory responses elicited in the cecum at early time points ( i . e . during the first 2 days ) after infection are largely T3SS-1-dependent [61] , [65] , [66] . However , mechanisms independent of T3SS-1 are responsible for cecal inflammation observed at later time points ( i . e . at days 4 and 5 after infection ) in the mouse colitis model [65] . Expression of the S . Typhi Vi capsular polysaccharide in S . Typhimurium leads to an attenuation of these T3SS-1-independent inflammatory responses in the mouse colitis model [41] , by reducing complement activation and TLR4 signaling [67] , [68] . Thus the viaB locus reduces intestinal inflammation by multiple different mechanisms ( Fig . S2A ) . A TviA-mediated repression of T3SS-1 reduces early inflammatory responses while the Vi capsular polysaccharide attenuates responses generated through T3SS-1-independent mechanisms at later time points . It is tempting to speculate that the result of these immune evasion mechanisms is a reduction in the intestinal inflammatory response that could contribute to differences in disease symptoms caused by typhoidal and non-typhoidal serotypes .
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 on mouse experiments was approved by the Institutional Animal Care and Use Committee of the University of California , Davis ( Permit Number: 16179 ) . The protocol on calf experiments was approved by the Institutional Committee at the Universidade Federal de Minas Gerais , Brazil ( Permit Number: CETEA 197/2008 ) . The bacterial strains , including relevant properties , are listed in table 1 . Unless noted otherwise , bacteria were aerobically grown at 37°C in Luria-Bertani ( LB ) broth ( 10 g/l tryptone , 5 g/l yeast extract , 10 g/l NaCl ) or LB agar ( 15 g/l agar ) . To induce expression of tviA and Vi capsule biosynthesis genes , an overnight culture in LB broth was diluted 1∶50 in tryptone yeast extract ( TYE ) broth ( 10 g/l tryptone , 5 g/l yeast extract ) or Dulbecco's modified Eagle's medium ( DMEM ) as indicated and incubated aerobically at 37°C for 3 h . When appropriate , antibiotics were added to LB broth cultures or LB agar plates at the following concentrations: carbenicillin ( 0 . 1 mg/ml ) , chloramphenicol ( 0 . 03 mg/ml ) , kanamycin ( 0 . 05 mg/ml ) , nalidixic acid ( 0 . 05 mg/ml ) , and tetracycline ( 0 . 01 mg/ml ) . Standard cloning techniques were performed to generate the plasmids listed in table 2 . Cloning vectors and ori ( R6K ) -based suicide plasmids were routinely maintained in E . coli TOP10 and DH5α λpir , respectively . An internal fragment of the phoN coding sequence was PCR amplified from the S . Typhimurium IR715 chromosome using the primers listed in table 3 , subcloned into pCR2 . 1 ( TOPO TA cloning kit , Life Technologies ) , and cloned into pEP185 . 2 utilizing the unique XbaI and SacI restriction sites to give rise to pSW208 . To generate pSW233 , pSW28 was digested with EcoRI and the DNA fragment comprising the joint upstream- and downstream regions of the tviB and vexE genes , respectively , was cloned into the EcoRI site of pRDH10 . Plasmids were introduced into S17-1 λpir and conjugation performed as described previously [57] . The unmarked S . Typhi ΔtviB-vexE mutant SW904 was constructed by inserting the plasmid pSW233 into the STY2 mutant chromosome , selecting for single crossover events ( creating merodiploids ) on LB agar plates containing Cm and Kan . Sucrose selection was performed as described previously [69] to select for a second crossover event , thus effectively deleting the tviBCDEvexABCDE genes , yielding SW904 . The deletion was confirmed by PCR . To facilitate transduction of the unmarked ΔsopE mutation , pSW245 was introduced in this locus in the SW976 chromosome by conjugation with S17-1 λpir as the donor strain , creating SW977 as an intermediate . Phage P22 HT int-105 was utilized for generalized phage transduction in S . Typhimurium as described previously [70] . For S . Typhi recipients , a similar protocol was followed except the multiplicity of infection ( MOI ) was increased to 100 . A phage lysate of SW399 was used to transduce the invA::pSW127 mutation into SW483 and SW74 , thus generating the S . Typhi ΔviaB ΔfliC invA mutant ( SW398 ) and the ΔtviB-vexE invA mutant ( SW611 ) . SW1207 and SW1208 were created by transducing the sopB::MudJ mutation from SW798 into the ΔviaB mutant ( SW347 ) and the ΔtviB-vexE mutant ( SW904 ) , respectively . The S . Typhi ΔviaB ΔsopE ( SW1209 ) , ΔtviB-vexE ΔsopE ( SW1210 ) , ΔviaB sopB::MudJ ΔsopE ( SW1216 ) , and ΔtviB-vexE sopB::MudJ ΔsopE ( SW1213 ) mutants were constructed by transducing the ΔsopE::pSW245 mutation from SW977 into SW347 , SW904 , SW1207 , and SW1208 , respectively . Subsequent sucrose selection allowed selecting for mutants that had lost the plasmid by allelic exchange and generated a clean ΔsopE mutation , thus creating SW1209 , SW1211 , SW1216 , and SW1213 , respectively . Similarly , a P22 lysate of SW839 was used to transduce the ΔsipA::pSW244 mutation ( SW839 ) into SW1207 , SW1208 , SW1209 , and SW1210 . The intermediates were subjected to sucrose selection , thus creating the clean ΔsipA mutation of strains SW1211 , SW1212 , SW1214 , and SW1215 , respectively . The ΔviaB sopB::MudJ ΔsipA ΔsopE mutant ( SW1217 ) was generated through transduction of the ΔsopE::pSW245 mutation from SW977 into the SW1211 chromosome and sucrose selection . The S . Typhimurium SL1344 derivatives SW759 and SW760 were established by transducing the phoN::CmR and phoN::tviA-CmR mutations from SW284 and SW474 into the SL1344 wild type . Transduction of the ΔfliC::pSPN29 from SPN305 into the SL1344 wild type and subsequent sucrose selection gave rise to the SL1344 fliC deletion mutant SW761 . Subsequent introduction of the fljB5001::MudJ into this strain led to the SL1344 ΔfliC fljB5001::MudJ mutant ( SW762 ) . To construct SW764 and SW793 , the phoN::tviA-CmR ( SW474 ) and phoN::pSW208 ( SW751 ) mutations were transduced separately into SW762 . Invasion-deficient derivatives of these strains were generated by transducing the invA::TetR mutation from SW562 into SW764 , SW793 , and SL1344 , thus creating strains SW766 , SW794 , and SW767 , respectively . SW806 , SW807 , SW808 , and SW809 were generated by transducing the phoN::Tn10dCm ( CS019 ) or phoN::tviA-CmR ( SW474 ) into SW867 or SW940 . The ΔsipA::pSW244 mutation ( SW974 ) was moved into the SL1344 wild type to create SW839 . SW940 was established by transduction of the sopB::MudJ mutation ( SW798 ) into SW976 and subsequent introduction of the sopE2::pSB1039 mutation ( SW800 ) . A P22 phage lysate of SW800 was used to create SW972 using SW1009 as the recipient strain . The phoN::KanR mutation from AJB715 was transduced into SW562 to give rise to the phoN::KanR invA::TetR mutant ( SW737 ) . HeLa 57A cells [29] , [34] were generously provided by R . T . Hay ( the Wellcome Trust Centre for Gene Regulation and Expression , College of Life Sciences , University of Dundee , United Kingdom ) . HEK-293 cells were obtained from ATCC ( ATCC CRL-1573 ) . Both cells lines were routinely cultured at 37°C in a 5% CO2 atmosphere in DMEM containing 10% fetal bovine serum ( FBS ) ( Life Technologies ) . For NF-κB activation and invasion experiments , cells were seeded in 24-well plates and 48-well plates ( Corning ) at densities of 1×105 cells/well and 2×105 cells/well , respectively , and incubated for 24 h prior to subsequent experiments . S . Typhi and S . Typhimurium strains were pre-cultured in TYE broth as described above . HeLa 57A cells or HEK-293 cells transfected with a NF-κB -luciferase reporter construct were infected with the indicated strains at a final concentration of approximately 106 colony forming units ( CFU ) /ml . To synchronize the infection , plates were centrifuged for 5 min at 500 g at room temperature . After 3 h , cells were washed with DPBS and incubated at 37°C for an additional 2 h in the presence of DMEM containing 10% FBS . Cells were washed in DPBS , lysed in 0 . 1 ml of reporter lysis buffer ( Promega ) , and firefly luciferase activity was measured using the luciferase assay system ( Promega ) in a FilterMax3 microplate reader ( Molecular Devices ) . Results are expressed as percentage of maximum signal elicited in each individual assay . In some experiments , cells were treated 30 min prior to infection until the end of the experiment with either DMSO ( vehicle control ) or the RIP2-inhibitor SB203580 at a final concentration of 10 µM dissolved DMSO . The NOD1 agonist C12-iE-DAP ( Invivogen ) was added a final concentration of 100 ng/ml . For transfection assays [34] , HeLa 57A cells were grown to a confluency of about 60% and transiently transfected with a total of 250 ng of plasmid DNA , consisting of 50 ng of the β-galactosidase-encoding vector pTK-LacZ , and either 200 ng of pCMV-myc ( control vector ) or 100 ng pRAC1-DN and 100 ng of control vector . For co-transfection with pGFP-SopE constructs , 50 ng of pTK-LacZ , 10 ng of the pGFP-SopE plasmid , 90 ng of pEGFP ( empty vector ) , and 100 ng of either pCMV-myc or pRAC1-DN was added . HEK-293 cells were transfected with 25 ng of pTK-LacZ and 25 ng of pNFkB-luc . 48 h after transfection , cells were infected with the indicated Salmonella strains or mock-treated ( LB broth ) as described above . Efficiency of transfection was normalized by adjusting luciferase values to β-galactosidase values . Invasiveness of the indicated Salmonella strains was determined using a Gentamicin protection assay as described previously [71] . Briefly , HeLa 57A cells were infected at a MOI of 5 with Salmonella strains pre-cultured in TYE broth . After 1 h , cells were washed and media containing 0 . 1 mg/ml Gentamicin was added for 90 min . Diluted cell lysates ( 0 . 5% Triton-X-100 ) were spread on LB agar plates to determine the number of CFU per well . Invasiveness was calculated as percentage of recovered bacteria compared to the inoculum . Overnight cultures of the indicated S . Typhi and S . Typhimurium strains were diluted 1∶50 in TYE broth and incubated at 37°C for 3 h . Total RNA was extracted from approximately 2×109 CFU using the Aurum Total RNA Mini Kit ( Biorad ) . 1 µg of total RNA was subjected to an additional DNase treatment ( DNA-free kit , Life Technologies ) and converted to cDNA using MuLV reverse transcriptase ( Life Technologies ) in a 25 µl volume as described previously [71] . 4 µl of this cDNA was used as the template for real time PCR analysis with the primers listed in table 3 . Data was acquired on a ViiA 7 real-time PCR instrument ( Life Technologies ) . Relative target gene expression was normalized to mRNA levels of the house keeping gene gmk , encoding guanylate kinase ( ΔΔCt method ) . DNA contamination was less than 1% for all amplicons as determined by a separate RT-PCR mock reaction lacking reverse transcriptase . Salmonella Typhimurium was cultured in LB broth at 37°C under agitation , followed by subculture in fresh LB ( without antibiotics ) for 3 hours , at 37°C under agitation . Four 3–4 week-old male healthy Salmonella-free Holstein calves were used in this study . Ligated ileal loops were surgically prepared as previously described [23] . Ligated loops were mock treated with intraluminal injection of sterile LB broth or inoculated with 3 ml of suspensions containing 1×108 CFU of the S . Typhimurium ATCC14028 phoN mutant ( AJB715 ) , a phoN::viaB mutant ( TH170 ) , a phoN::tviA mutant ( SW474 ) , or a phoN invA mutant ( SW737 ) . Ligated loops were surgically removed at 5 h after infection for tissue sampling and measurement of intraluminal fluid accumulation . Samples containing the intestinal mucosa and the associated lymphoid tissue were collected with a 6 mm biopsy punch . Each intestinal biopsy was kept in sterile PBS with 50 µg/ml of gentamicin for 1 h , homogenized in 2 ml of PBS , serially diluted , and plated on LB agar plates containing nalidixic acid . Additional biopsies were fixed by immersion in 10% buffered formalin , processed for paraffin embedding , cut and stained with hematoxylin and eosin . Histopathologic changes including hemorrhage , neutrophilic infiltration , edema , and necrosis and/or apoptosis were scored from 0 to 3 ( 0 for absence of lesions , and 1 , 2 , or 3 for mild , moderate , or severe lesion , respectively ) for a combined total score ranging from 0 to 12 . Animals were obtained from The Jackson Laboratory ( Bar Harbor ) , housed under specific-pathogen-free conditions and provided with water and food ad libitum . Groups of female , 9–12 week old C57BL/6 mice were orally treated with 20 mg Streptomycin . After 24 h , these mice were inoculated as described previously [61] with either 0 . 1 ml LB broth ( mock treatment ) or 1×109 CFU of the S . Typhimurium SL1344 fliC fljB phoN mutant ( SW793 ) , the fliC fljB phoN::tviA mutant ( SW764 ) , the fliC fljB phoN invA mutant ( SW794 ) , or the fliC fljB phoN::tviA invA mutant ( SW766 ) suspended in 0 . 1 ml LB broth . 12 h after infection , animals were euthanized and tissues were collected . The bacterial load was determined by spreading serial 10-fold dilutions of homogenates on LB agar plates containing the appropriate antibiotics . Flash-frozen cecal tissue was homogenized in a Mini-beadbeater ( Biospec Products ) and RNA was extracted by the TRI reagent method ( Molecular Research Center ) . cDNA was generated using MuLV reverse transcriptase and reverse transcription reagents ( Life Technologies ) . SYBR Green ( Life Technologies ) -based real-time PCR was performed as described previously [72] using the primers listed in table 3 . Data was acquired by a ViiA 7 real-time PCR system ( Life Technologies ) and analyzed using the comparative Ct method ( ΔΔCt method ) . Murine target gene transcription within each sample was normalized to the respective levels of Gapdh mRNA . Data obtained from tissue culture experiments , bacterial gene transcription experiments , and the bovine ligated ileal loop model was log-transformed prior to analysis with a paired Student's t-test . To determine statistical significance for relative mucosal mRNA transcription and tissue bacterial load between treatment groups , an unpaired Student's t-test was employed .
|
Bacterial pathogens translocate effector proteins into the cytoplasm of host cells to manipulate the mammalian host . These processes , e . g . the stimulation of small regulatory GTPases , activate the innate immune system and induce pro-inflammatory responses aimed at clearing invading microbes from the infected tissue . Here we show that strict regulation of virulence gene expression can be used as a strategy to limit the induction of inflammatory responses while retaining the ability to manipulate the host . Upon entry into host tissue , Salmonella enterica serovar Typhi , the causative agent of typhoid fever , rapidly represses expression of a virulence factor required for entering tissue to avoid detection by the host innate immune surveillance . This tight control of virulence gene expression enables the pathogen to deploy a virulence factor for epithelial invasion , while preventing the subsequent generation of pro-inflammatory responses in host cells . We conclude that regulation of virulence gene expression contributes to innate immune evasion during typhoid fever by concealing a pattern of pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacterial",
"diseases",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"host-pathogen",
"interactions",
"medical",
"microbiology",
"gastroenterology",
"and",
"hepatology",
"microbial",
"pathogens",
"biology",
"and",
"life",
"sciences",
"microbiology",
"salmonella",
"gastrointestinal",
"infections",
"bacterial",
"pathogens",
"pathogenesis"
] |
2014
|
Salmonella enterica Serovar Typhi Conceals the Invasion-Associated Type Three Secretion System from the Innate Immune System by Gene Regulation
|
Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus . In addition to studying the network interactions , there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making . Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities . These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing , and hence the utility of single cell resolution data . Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances . We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling . Our analysis reveals ( 1 ) the dynamic features of signaling that significantly alter transcriptional regulatory patterns ( encoding ) , and ( 2 ) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity ( decoding ) . Immediate early gene expression patterns were informative of signaling peak retention kinetics , whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling . Moreover , the information processing aspects varied across the network , with each component encoding a selective subset of the dynamic signaling features . We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components . Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions . Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses .
Cells continuously sense a variety of physical and chemical signals and respond suitably to changes in their environment [1–3] . Depending on the nature of the environmental change , a subset of membrane receptors gets stimulated , eliciting specific signaling pathways and enabling cells to make informed decision downstream [4] . Recent studies indicate that the functional information to trigger a specific downstream response is typically carried through activity change in one or more signaling molecules , transcription factor activities or protein substrates [5–7] . Considering the multifunctionality of signaling pathways [8] ) , it has been argued that this mode of information transfer is more rich and complex than encoding it through merely binary on/off steady states [9–12] . For example , depending on the cell type , transient activation pattern of stimulated extracellular signal-regulated kinases ( ERK ) leads to cell proliferation , whereas sustained pattern of ERK may lead to cell differentiation [13] . However , the mechanism through which the functional information encoded within the temporal activity change is processed and decoded to very fine alterations in an upstream signaling pattern still remains unclear . Instantaneous activity change between upstream and downstream events has been typically considered to be the natural mechanism of information processing , thus , limiting our understanding of dynamical patterns [14–17] . Therefore , a challenging but relevant task is to elucidate the mechanism through which features or properties of signaling dynamics ( time dependent activity-change in signals ) encode information , and how cells decode this information via complex signaling pathways with high specificity , when variability is the norm . Mounting evidence from single cell microscopy [18] , flow cytometry [19] , single cell PCR [20] , single cell RNA-seq [21 , 22] , and single cell mass cytometry [23] studies reveals that variability in post-transcriptional or post-translational events , even within homogeneous condition , is ubiquitous [24–26] . Such results has generated considerable interest in employing single cell resolution data to elucidate the role of variability and characterize underlying mechanism of intracellular information processing [3 , 27] . Since comprehensive network motif identification studies have unveiled a strong interdependence between dynamics , motif structure , and specific function [28] , role of network topologies during information processing also needs to be considered [29] . For instance , feedforward loops have an ability to generate a transient activity or protect against brief fluctuations depending on the nature of their interactions [28] , whereas positive feedback loops induces a response delay and hysteresis in the network dynamics [30] . Negative feedback mechanisms protect the information transfer by saturating the signaling pathways from upstream fluctuations [31] . In sum , precise orchestration of cellular events is a consequence of an efficient signaling modulation code involving upstream signaling dynamics and network motifs [1 , 32 , 33] . Hence , we hypothesize that functional information to direct cells towards specific responses is encoded in the temporal features of the signaling dynamics , and that the regulatory network motifs facilitate decoding this information . We test our hypothesis through analysis of an integrated model of signaling dynamics and gene regulatory networks . Both gene regulatory networks and communication networks have a similar underlying operating principle [34 , 35] . As shown in Fig 1 the goal is to encode relevant information in the characteristic features of the input signals and transmit them over a channel so they can be decoded at the receiving end [17 , 36 , 37] . Molecular signaling pathways elicited by receptor activation in cells which are involved in the regulation of physiological responses such as growth , differentiation and programmed apoptosis , among others [11 , 38] , have been shown to exhibit such properties [3 , 9] . Unlike communication networks , there are limitations to the predictability of downstream responses in a biochemical network for any given input signal , and in particular at single cell levels [39] . This is because it is difficult to infer the strength of an input stimulus received by a single cell based on the instantaneous downstream responses alone . The goal of this manuscript is to assess how variation in dynamic signaling pattern influences downstream regulation at the single cell level . Identifying the mechanism of how a specific downstream phenotype could be triggered by different combinations of kinetic modulation in an upstream signaling pattern is critical to our understanding of the cellular decision making process . Here we employ a mechanistic ODE based model [40] , that is comprised of multiple receptor stimulated signaling kinase cascades activating immediate early response genes ( IEGs ) ( c-Fos and c-Jun ) , and transcription factors ( TF ) ( AP-1 ) , which turn on late response target genes ( right side of Fig 1 ) [41] . This two-tier regulatory network is comprised of a feedback as well as a feedforward transcriptional network motif , making it a suitable candidate to test our hypothesis . We characterized a set of independent dynamical features for each input signal ( ERK , FRK and JNK in Fig 1 ) through a novel phenomenological model ( see Fig 2A for graphical illustration ) . Variance based global sensitivity analysis was used to study how uncertainty in signaling features affects uncertainty in downstream IEG and TF responses , and to also quantify the relative importance of each feature at every timepoint [40 , 42] . Information theory was used to measure mutual information between signaling feature and the downstream gene regulatory responses . Mutual information quantifies the amount of information that is carried by a signaling feature , and allow us to evaluate its capacity to make a binary transcriptional decision downstream . We further use decision tree analysis [43] , a predictive modeling technique , to identify decision rules for signaling features that significantly shape the IEG and TF response patterns . As single cell studies become more advanced , it has become more evident that cells may use dynamics of signaling species to encode important information that can be decoded to generate distinct cellular responses . We use our earlier published single cell gene expression data [20] for specific transcriptional target outputs to identify the upstream signaling features that are most likely to be transmitted into specific phenotypes via generation of distinct target gene expression levels . Our results revealed that while signaling dynamics have many potential functional information encoding features , transcriptional regulatory network motifs play a significant role in limiting which features are significant at different phases of the stimulation . Moreover , estimating the information transferred by each signaling feature showed that in the majority of cases a single feature is insufficient to lead to a binary transcriptional decision ( i . e . individual features carried less than one bit of information ) . In this light , decision tree analysis demonstrated that a combination of signaling features constitutes a feature based modulation code involving crosstalk in order to drive the cellular decision making . Given extensive single cell variability , our methodology is of practical utility to experimentalists who use single cell resolution data and are typically confronted with the variable aspects of the cellular activities [44 , 45] . By focusing on dynamical features of the inputs to a signaling system , we have provided a novel framework which is broadly applicable to many signaling systems .
A phenomenological model for dynamics of a transient signaling kinase was developed to characterize independent time-invariant signaling features based on dynamical phases of delay , activation , peak retention and deactivation kinetics ( Fig 2A; Methods section ) . An exponential ascent or decay model for temporal phases was derived to establish duration and time-constant features ( see Table 1 for details ) . In order to capture observed variability in signaling dynamics , we introduced simultaneous variation in all of the identified upstream signaling features using a differential variation schema ( Table 1 and S1B Fig ) . Our results revealed two distinct aspects of signaling variability . First , we observed that simultaneous variation in signaling features captured the variation of the entire signaling profile ( shown as gray region in the Fig 2B , 2C and 2D ) . Second , these variations were found to be within experimental range measured from bulk tissue samples , shown as red error bars in the plots . Our results suggest that the variability in signaling features spans the combined effects of intrinsic and extrinsic fluctuations . A combination of three distinct dynamical profiles for each signaling kinase ERK , FRK and JNK , respectively , was selected at random and simulations were performed for each of these combinations . A total of 100 , 000 simulations were conducted . The response of immediate early genes ( IEGs ) and transcription factors ( TFs ) resulting from these simulations revealed that variability in dynamical features propagates downstream as signal progresses in the network ( gray regions in Fig 2E , 2F , 2G , 2H and 2I ) . Specifically , variability in downstream AP-1 TF , the aggregate of the two dimers , matched well with the observations from cell population measures [46] , shown in red error bars , implying that variations in signaling features captures the biological variability at different levels in the network . Each dynamic signaling feature impacted the overall downstream response both positively and negatively , depending on the nature of the variations and dynamical interactions involved . Downstream response to receptor stimulation was divided into three phases: ( 1 ) Early phase where cells predominantly utilized the steady state TF proteins as well as triggered new IEG expression , ( 2 ) Intermediate phase where cells utilized new proteins transferred from IEG expression to modulate AP-1 TF composition and levels , and when the heterodimer ppc-FOS:ppc-JUN was the dominant contributor to AP-1 aggregate [47 , 48] , and ( 3 ) Late phase where ppc-JUN:ppc-JUN homodimer was the primary contributor to AP-1 TF [49] . It is important to note that transcription of c-Jun gene is slower in comparison to that of c-Fos gene [40] , however , the translation process of c-Jun mRNA is much faster in comparison to that of c-Fos mRNA [41] . Variability in downstream TF dynamics ( shown as density plots in Fig 3A ) were decomposed to quantify the contribution of each signaling feature at different phases of the stimulation . Variance based global sensitivity analysis was performed to measure these contributions as first order and total sensitivities ( see Methods ) . These evaluations revealed that AP-1 dynamics ( both aggregate and individual dimers ) were sensitive to only a select subset of signaling features at any given time ( Fig 3B ) . Transcription regulatory pathways contain a small set of recurring regulation patterns , called network motifs that are critical in processing of signaling information and in shaping of the response pattern . Our network contains two such recurring network motifs ( S2 Fig ) : ( 1 ) FRK/ERK module showing a coherent feed forward interactions between receptor stimulated ERK pathway to activate ELK1 and subsequently transcribe IEG c-Fos , which is further activated ( phosphorylated ) by FRK to produce the heterodimer . ( 2 ) JNK module represents a positive feedback loop by expressing IEG c-Jun buffered via pp-ATF2:ppc-JUN dimer complex , and activation ( phosphorylation ) of the homodimer . Estimating the lower bound of the Mutual Information ( MI ) between a signaling feature and downstream TF response revealed that majority of the information carried by individual features to TFs were less than one bit ( See Methods and Fig 4 ) . In this context , MI corresponds to the decoding of the TF level which can discriminate between the two levels of a dynamic signaling feature . Analysis of the information transfer at 20 minutes ( early phase ) revealed that α2F and τ1F transferred nearly equal amount of information ( ∼ 0 . 4 bits ) to both heterodimer as well as to AP-1 . In contrast , at 40 minutes ( intermediate phase ) and 60 minutes ( late phase ) the amount of information transferred by activation time-constant of JNK ( τ1J ) to AP-1 was the highest ( ∼ 0 . 4 and ∼ 0 . 45 bits , respectively ) . However , τ1J carried ∼ 0 . 7 and ∼ 0 . 5 bits of information to homodimer at 40 and 60 minutes , respectively , a complete reversal to the pattern observed in the Total AP-1 . Importantly , the time-constant feature of FRK ( τ3F ) carried ∼ 0 . 4 bits at 40 minutes and ∼ 1 . 2 bits at 60 minutes , suggesting that the network has the capacity to decode this feature to make binary decisions at the later phase of the dynamic response . In contrast , the FRK time-constant τ3F carried less than 0 . 1 bit of information to Total AP-1 . The signaling features of ERK had minimal information transfer to the TFs , in consistent with the network topology , as the effect of changes in ERK on the TFs is indirect and conditional on the dynamic features of FRK and JNK signaling . We extended our results on global sensitivity analysis and information transfer to study whether the variable responses at the single cell level could be analyzed for inferring the dynamic features of upstream signaling . We considered a typical case involving snapshot measures from single cells , e . g . , gene expression levels , or immunocytochemistry data on activated TF protein levels [51] . Decision tree analysis was performed ( see Methods ) on downstream TF activity phenotypes at three different timepoints: 20 minutes ( S5 Fig ) , 40 minutes ( S6 Fig ) and 60 minutes ( Fig 5 ) . We observed that the TF activity response at any given time point was a distribution , and a function of varying upstream signaling features . Three distinct phenotypes at each time point were defined as High , Mid , and Low TF levels ( Fig 5 ) . The High and Low phenotypes represent the upper and lower margins of the distribution , respectively , and the Mid phenotype corresponds to the median of the distribution . We considered the TF phenotypes of Total AP-1 ( Fig 5A ) , heterodimer ppc-FOS:ppc-JUN ( Fig 5E ) , and homodimer ppc-JUN:ppc-JUN ( Fig 5I ) . Mapping these TF phenotypes to corresponding kinase profiles resulted in distinguishable signaling features that would yield distinct TF response dynamics ( Fig 5B , 5F and 5J ) . The combination of key features varied depending on the specific TF phenotype analyzed: JNK features appear to be distinct for the Total AP-1 and homodimer phenotypes ( Fig 5B and 5J ) , whereas FRK features were specific to the stratification of the heterodimer phenotypes ( Fig 5F ) . We developed decision trees to organize these signaling features into a hierarchy of rules defining the ranges of variation that stratify activation profiles of downstream TF phenotypes . For the Total AP-1 TF snapshot response measure at late phase , the decision tree analysis revealed a hierarchy of rules based on three signaling features of JNK ( τ1J , τ2J and τ3J ) as primarily yielding downstream phenotypes ( Fig 5C ) . Our results revealed that fast activation of JNK ( τ1J < 5 . 3 ) and very slow peak deactivation of JNK ( τ2J > = 25 ) were highly likely to drive cells to a High AP-1 phenotype , even in presence of variations in other kinase signaling features . In contrast , slow activation of JNK ( τ1J >= 5 . 3 ) , fast peak-deactivation of JNK ( τ2J < 16 ) , and fast deactivation of JNK ( τ3J < 22 ) were very likely to drive cells to Low AP-1 phenotype . We assessed the decision tree analysis results in an alternative visualization layout considering the top two dominant features from the decision trees in a scatter plot , and mapped the rules as well as the phenotype categories to the plot to observe how these features separated cells with a specific phenotype ( Fig 5D ) . The extremes , High and Low AP-1 , were well separated by specific intervals of τ1J and τ2J , with faster activation and slower inactivation leading to a High AP-1 phenotype . In the case of heterodimer phenotypes , τ3F was found to be the only dominant feature separating all of the three phenotypes ( Fig 5G and 5H ) . On the contrary , the homodimer phenotypes were segregated by a combination of τ1J , τ2J , τ3J and α1J ( Fig 5K and 5L ) . The results from decision trees were in agreement with those from analysis of MI . Features which carried one or more than one bit of information to downstream TF responses were sufficient to individually distinguish High , Mid , and Low phenotypes . For example , α1J separated homodimer phenotypes at 20 minutes ( S5K and S5L Fig ) , and τ3F separated heterodimer phenotypes at 60 minutes ( Fig 5G and 5H ) . α1J carried ∼ 0 . 85 bits of information to homodimer at 20 minutes , and τ3F carried ∼ 1 . 2 bits of information to heterodimer at 60 minutes ( Fig 4 ) . In summary , decision tree analysis revealed that an individual feature which transferred ≥ 1 bit of information corresponds to the distinct downstream TF phenotypes , with potential consequences for fine tuning cellular decision making in response to external stimuli . We further analyzed bivariate gene expression patterns of immediate early genes ( c-Fos and c-Jun ) as observed in single cell experiments ( Fig 7A , data is from [20] ) . Despite the cells being picked from a pool of neurons of same type under homogeneous conditions , experiments show that the variability in the gene expression levels is extensive [53] ( almost 4-8 fold variation in the abundance ) . In order to identify the key upstream signaling features that lead to distinct phenotypes of the observed response distributions ( Fig 7A ) , we employed a model based approach to analyze statistically similar distribution of the single cells from our simulation results . Four groups were identified based on the high and low 25th percentile of the marginal distributions: ( 1 ) high c-Fos , high c-Jun , ( 2 ) high c-Fos , low c-Jun , ( 3 ) low c-Fos , high c-Jun , and ( 4 ) low c-Fos , low c-Jun . We found that the key controlling features established from our earlier analysis ( Fig 6 ) , separated the four IEG groups distinctively in the signaling feature space ( Fig 7B ) . Decision tree analysis ( Fig 7C ) revealed that: ( 1 ) slow rate of ERK peak deactivation ( high τ2E ) and slow rate of JNK peak deactivation ( high τ2J ) produced high c-Fos and high c-Jun gene expression pattern , ( 2 ) slow rate of ERK peak deactivation ( high τ2E ) but fast rate of JNK peak deactivation ( low τ2J ) produced high c-Fos and low c-Jun gene expression pattern , ( 3 ) fast rate of ERK peak deactivation ( low τ2E ) but slow rate of JNK peak deactivation ( high τ2J ) produced low c-Fos and high c-Jun gene expression pattern , and ( 4 ) fast rate of ERK peak deactivation ( low τ2E ) and fast rate of JNK peak deactivation ( low τ2J ) produced low c-Fos and low c-Jun gene expression pattern . Our results indicate that the variability is the gene expression patterns in single cells is likely to contain information on features of the upstream signaling dynamics . Our analysis thus far revealed that specific features of signaling dynamics correspond to the patterns of downstream gene regulation and TF activity . The results also indicated that distinct signaling features can be decoded depending on the downstream location in the regulatory network . This motivated us to consider whether there was a higher order organization of which features being decoded and where in the network . We performed exhaustive global sensitivity analysis and information transfer assessment to consider each node in the network as the output of interest . For each node , we computed the sensitivity indices corresponding to the dynamic signaling features as well as the information transfer between the signaling feature at the corresponding node levels . We represented the results as a Sensitivity Map and Information Transfer Map . The Sensitivity Map lays out all of the controlling features ( Si > 0 . 1 ) for different nodes in the regulatory network and at different phases of the stimulation ( early , intermediate and late ) ( Fig 8A ) . Similarly , the Information Transfer Map lays out the extent of information transferred by each feature to different network decoders ( mutual information between signaling features and the downstream responses ) and at different phases of the stimulation ( Fig 8B ) . Unsupervised clustering of these maps yielded two major groups corresponding to the two regulatory motifs that drove distinct downstream activity patterns: transcriptional feedforward and transcriptional positive feedback interactions ( Fig 8C ) . Both of these maps independently separated regulatory motifs along the network decoders thereby revealing the interdependence of information processing by signaling features and by network topology .
We present a new paradigm in which dynamic signaling features and not molecular abundances are introduced as the carriers of functional information . We developed a novel empirical model for signaling profiles based on transient phases of activation , peak retention and deactivation kinetics to characterize the dynamical aspects of the signal . We then performed dense Sobol sampling over an uniform high-dimensional space of signaling features to map the functional space of signaling dynamics which spans all possible essential dynamical profiles for three upstream signaling kinases , namely ERK , FRK and JNK respectively . We employed variance based global sensitivity analysis , estimated mutual information , and developed decision trees to identify the key controlling features . The amount of the information carried to various network components were also analyzed , specifically to the IEG and TF responses . Our results showed that the key controlling feature must carry at least one bit of information in order to distinctively separate downstream IEG and TF dynamical response patterns . Our analysis revealed that in the majority of cases an individual signaling feature carried insufficient amount of information ( less than one bit ) to directly lead a binary transcriptional regulation , and therefore a combination of features influenced downstream regulatory process . Cellular decision , hence , is a consequence of decoding of multiple dynamic signaling features , rather than a direct function of the abundances of functionally active signaling effectors . With recent advancements in single cell technologies such as fluorescent labeling techniques , optogenetics , gene-expression profiling , and time-lapse microscopy , it is evident that cells transduce functional information through time-dependent activity changes in one or more signaling molecules [9–11 , 54–56] . For example in a recent single cell quantitative live-cell imaging study , it was found that individual macrophages exhibited analog responses to LPS-induced NF-κB dynamics [57] , in contrast to the digital responses previously reported in cytokine treated non-immune cells [58 , 59] . Cells , therefore , are capable of interpreting a stimulus in a continuous manner for which the time-dependent aspect of the signal represents an embedded functionality . We explored a scheme where the cell’s signaling code involves dynamic signaling features . These features represent different kinetic modulation of the signal as the functional aspect of a molecular activity change . Simultaneous variation in these features yielded signaling dynamics as variable as observed experimentally , and also bounded within the limits of the population measure . As the signal progressed in the network , the variability through dynamical features also progressed resulting in a non-uniform response distribution downstream , in consistent with other studies [24 , 26 , 60 , 61] . Our results showed that this downstream response distribution were also within the biological variability reported in the population response . Using global sensitivity analysis , decomposition of the variance of the downstream IEG and TF responses revealed that the response distributions were essentially a result of multiplexing of various signaling features . Our results implied that pleiotropic regulators such as signaling pathways are likely to employ a feature-based modulation code to transduce intracellular information and elicit context-specific responses . This code includes combinations of time-invariant signaling features including duration , time constant , amplitude and/or frequency of dynamics to ensure specificity in the downstream responses . Further , our approach employs decision tree analysis to identify the rules involving upstream signaling features to understand the nature of decision making , enabling us to unravel the complexity of cellular information processing . Our approach points to a novel modulation code involving dynamic features of signaling , through which the gene regulatory phenotypes may be shaped . In such a formulation , the manipulation of signaling towards desired gene regulatory responses involve alterations in the signaling onset , duration , etc [62] . This is in stark contrast to an abundance based code that would use over/under-expression or knock in/out based interventions . Analyzing the implications of transcriptional variability at the single cell level must include the interplay between the properties or features of the upstream signaling dynamics [41 , 60] . Decision tree analysis allow us to establish the functional implications of variability in signaling dynamics in cellular decision and elucidate how this variability may result in cellular ( or phenotypic ) heterogeneity . Our results revealed that correlations between activity levels of signaling molecules and downstream responses are insufficient to establish a physiologically relevant code . For instance AP-1 activity levels were found to be sensitive to a different subsets of signaling features at different phases of the stimulation . Our analysis found that the regulatory network is as essential an element in cellular decision making . Intracellular information decoding is equally as critical as information encoding . Similar observations have been reported in a single cell study where dual reporters were used to characterize live-cell response through LPS induced NF-κB dynamics , and to learn how they correlated with inflammatory cytokine gene-expression output [57] . When the instantaneous ratio of abundance levels was calculated , the transcriptional regulator ( NF-κB ) and downstream transcription ( TNF-α gene reporter ) were poorly correlated . An unbiased regression analysis of NF-κB dynamics over entire stimulation time course was found to be the key determinant of the transcriptional output , suggesting the limitation of comparing molecular abundances . In the same study , it was observed that the macrophages showed a robust discrimination to different LPS doses . This was because the information decoding involved a feedback dominance switching which stratified the immune response despite the limited information encoding capacity of the signaling network [17] . Hence , our dynamic feature based information processing framework can be readily employed to study cellular decision making in a broad range of biological contexts . Variability in signaling dynamics [63] and in transcriptional regulation [25 , 41] have been typically studied in isolation of each other . For instance , studies on the dynamics of ERK2 translocation in single human cells reveal a large variation in basal ERK2 nuclear levels [63 , 64] . Besides peak fold change , other features of the dynamic response pattern such as final fold change , the peak delay and timing of activation also exhibit high variability [65] . Similar variability is also seen among the single cell time courses of NF-κB dynamics treated across the range of LPS concentrations [57] . On the other hand , the seminal studies of transcriptional variability in bacteria and yeast have demonstrated that inherent stochastic noise in gene expression ( intrinsic variability ) , as well as variability in cellular transcription factor activity levels ( extrinsic variability ) , can result in cellular heterogeneity [26] . We have also reported similar observations in mammalian cells where cell-to-cell variability of transcriptional change was shaped by the inputs to the cells , suggesting the patterns of response distribution are based on a systematic processing of inputs instead of being a noise around the mean [20] . Our framework bridges the variability between the signaling dynamics and transcriptional regulatory responses through a model based approach . Most interestingly , our results demonstrate that even a single snapshot response measure of single cell transcriptional activity can be informative of upstream signaling dynamics . Further analysis revealed that different network components ( IEG vs TF ) decode different aspects of the signaling dynamics , indicating that a network component can only decode a portion of the signaling history . For instance , AP-1 response dynamics were informative of a combination of activation and peak retention kinetics of the JNK signal , whereas c-Fos and c-Jun IEGs were informative of peak deactivation kinetics of ERK and JNK signals , respectively . This distinction in dynamic decoders is a result of multiplexing of signaling features via regulatory network motifs . Feedforward interactions imposed a bottleneck to information processing , where early phase features such as delay and activation kinetics of FRK did not transmit information to downstream TFs . In contrast , positive feedback loop introduced a time lag in information processing where the late phase of the downstream AP-1 TF was informative of activation kinetics ( early phase ) of the JNK signal . Other studies on signaling dynamics , particularly those considering networks with feedback and feedforward loops , use much longer term analysis than just one hour [66 , 67] . Opportunities exist for longer-term dynamics to unravel additional complexities of these regulatory network motifs which may not be captured by shorter-term dynamics . Studies to date have analyzed the mechanism of cellular decision making as related to dynamics of signaling activities by evaluating the correlation between the upstream and downstream molecular abundances . For instance , high correlation between EGF binding to EGFR and subsequent cell proliferation [64] , or high correlation between increase in IL2 and IL4 receptor trafficking and consequent T cell proliferation [68 , 69] have been interpreted as information transfer from the activated receptor levels to the downstream effector activities . However , inferences based on instantaneous correlations do not consider how cells process the time-dependent activity changes . The implicit assumption to these studies is that instantaneous change in molecular abundances between the upstream and downstream events is the natural mechanism of cellular information encoding [31 , 70] . Such an approach may establish a causality between the two domains , but limits our understanding of any functional capabilities that could be gained by processing dynamical pattern . Our approach of reformulating the problem overcomes these limitations for a novel analysis of cellular information processing that takes dynamics into account . Emerging single cell studies are illustrating the use of high temporal resolution data sets to unravel the mechanism underlying cellular decision making process . For instance in a recent study , a microfluidic setup was used to dynamically administer inhibitor treatment to alter the amplitude , frequency , and duration of nuclear localization of transcription factor Msn2 , while a fluorescent reporter of Msn2 transcriptional activity was used to detect the downstream response [7 , 33] . The study revealed that different expression patterns correlated with distinct dynamical features of administrators . As another example , a short duration of LPS stimulus does not elicit an immune response , whereas persistent LPS stimulus yields an innate immune response [71] . Such results are consistent with our findings that the information transfer underlying cellular decision making involves dynamic aspects of signaling . We found that the dynamic decoding is in close alignment with the network topology , as revealed by unsupervised clustering of the newly developed Sensitivity and Information Transfer Maps . Our findings suggest a novel dynamic modulation code , where signaling features in coordination with the network motifs shape the downstream regulatory patterns . Our approach provides a novel framework to study the structural features , i . e . , static aspects of a network , in integration with signaling and transcriptional regulatory kinetics , i . e . , dynamic aspects of the function , to understand information transfer underlying cellular decision making process . Understanding these dynamic encoding-decoding principles would enable sophisticated strategies to manipulate signaling dynamics to better control cellular responses in a broad range of biological contexts .
We defined gene regulatory model as a combination of regulatory network ℱ and external input signal fs as y ˙ = F ( t , y 0 , k , f s ( X , t ) ) ( 1 ) where species y ∈ ℝ s , parameters k ∈ ℝ m and signaling features X . Activation of receptor due to ligand binding on the cell surface elicits and localizes multiple signaling kinases [72] . In our model , extracellular-signal-regulated kinase ( ERK ) , c-FOS regulating kinase ( FRK ) and c-Jun N-terminal kinase ( JNK ) were activated in cells through receptor stimulation , to transcriptionally and post-translationally regulate the activator protein-1 ( AP-1 ) family of TFs ( See [40 , 73] for details on model development ) . Family of AP-1 TFs considered here are: the heterodimer ppc-FOS:ppc-JUN and the homodimer ppc-JUN:ppc-JUN . These activated AP-1 TFs subsequently bind to promoters of key target genes resulting in gene expression changes [20 , 74] . Activation of target genes is involved in regulation of blood pressure and the development of hypertension in brainstem [74–76] . Hence , AT1R stimulated multiple signaling kinases in brainstem neurons are tightly coupled with the specific physiological function of controlling blood pressure . This mechanistic model was implemented as a set of ordinary differential equations that integrated three signaling kinases viz . FRK , ERK and JNK with activation of AP-1 TFs ( Fig 1 ) . Experimentally measured time course activity change in three kinases were interpreted as input signals [40 , 46] , and subsequent changes in the activity of IEGs and TFs as the downstream responses [40 , 77] . Details of the model equations are reported in S1 and S2 Tables . The model code in MATLAB format is available through accession No . 185122 on the ModelDB resource [78] at https://senselab . med . yale . edu/ModelDB/showModel . cshtml ? model=185122 . In our model , signaling kinase profiles were assumed to be an outcome of enacting activation , peak retention , and deactivation processes , initiated in parallel by receptor stimulation through ligand binding ( input stimulus ) [79 , 80] . A simplified phenomenological interpretation of the phosphorylated kinase signaling profile can then be developed as the difference between first order activation and peak processes , and both of them opposed by a first order deactivation processes , represented as: f s ( t ) = ( z 1 ( t ) - z 2 ( t ) ) - z 3 ( t ) ( 2 ) where fs is the dynamics of the signaling kinase , z1 are the activation processes , z2 are the peak processes , and z3 are the deactivation processes initiated by a receptor stimulus as shown in control diagram ( S1A Fig ) . z 1 ˙ ( t ) = k a z 1 u ( t ) - k d z 1 z 1 ( t ) ( 3 ) z 2 ˙ ( t ) = k a z 2 u ( t ) - k d z 2 z 2 ( t ) ( 4 ) z 3 ˙ ( t ) = k a z 3 u ( t ) - k d z 3 z 3 ( t ) ( 5 ) where u ( t ) is a function representing the upstream stimulus , k a z i is the activation parameter of the dynamical process zi and k d z i is the deactivation parameter of zi . To characterize the dynamical signaling features of the activated kinase signal , a transfer function model can be derived by taking the Laplace transformation on each of these differential equations . Considering all three processes as analogous to each other in their behavior yields the following form . L { z ˙ ( t ) } = L { k a z u ( t ) } - L { k d z z ( t ) } ( 6 ) s z ^ ( s ) = k a z u ^ ( s ) - k d z z ^ ( s ) ( 7 ) F ( s ) = z ^ ( s ) = ( k a z / k d z ( 1 / k d z ) s + 1 ) u ^ ( s ) = ( λ τ s + 1 ) u ^ ( s ) ( 8 ) For u ( t ) as a heavyside unit step function , the inverse laplace transformation ( ℒ − 1 { F ( s ) } ) results in an exponential ascent or decay model depending on the process [81] . Note that each of the activation , peak , and deactivation dynamical processes are defined with specified delays , αi , with a following constraint; α1 + α2 + α3 ≤ ( tf − ti ) , where α1 is the delay in activation processes , α1 + α2 is the delay in peak processes , α1 + α2 + α3 is the delay in deactivation processes , ti is the initial time point and tf is the final time point . Resulting dynamics of the signaling kinase typically follow a transient signaling pattern over minute timescales [79 , 82] . This transient signaling profile , represented by fs in Eq ( 9 ) , was observed to have four phases of dynamics: delay , activation , peak retention and deactivation phases . The mathematical form for last three phases are represented as a function of two independent features: ( 1 ) a duration feature , α and ( 2 ) a time constant feature , τ , as shown in Eq ( 9 ) . The resulting function of signaling dynamics is continuous and differentiable within a given boundary condition ( from ti = 0 to tf = 60 minutes in our case ) ( See Fig 2A for a graphical representation ) . f s ( X , t ) = λ { 0 , t ≤ α 1 Delay 1 - exp ( - ( t - α 1 ) τ 1 ) , α 1 < t ≤ ( α 1 + α 2 ) Activation exp ( - ( t - α 1 - α 2 ) τ 2 ) , ( α 1 + α 2 ) < t ≤ ( α 1 + α 2 + α 3 ) Peak Retention exp ( - ( t - α 1 - α 2 - α 3 ) τ 3 ) , ( α 1 + α 2 + α 3 ) < t ≤ 60 Deactivation ( 9 ) where X ∈ {α1 , τ1 , α2 , τ2 , α3 , τ3} represents the collective set of all signaling features of a kinase signal . The signaling dynamics of each activated kinase is represented by six independent features , making it a total of eighteen for three kinases ERK , FRK and JNK , respectively . List of all independent features , their least square fitted values with their appropriate experimental observations , and the range of induced variation to each signaling feature are reported in Table 1 . To computationally depict the variability of single cells , duration features which are in time domain were varied up to a multiplicative factor of 2 to 4 , whereas time constant features were varied up to two-fold . In this study , a biochemical regulatory network is considered as the channel which processes various dynamical aspects of the signaling pattern and generates a downstream response pattern ( Fig 1 ) . Because multiple interactions occur simultaneously in a signaling pathway , a true mathematical relationship between the dynamical signaling features and a downstream response is difficult to construct . Hence , to evaluate the influence of signaling features on a downstream response , we turn to global sensitivity analysis . Global sensitivity analysis investigates how uncertainty in key signaling features of a biochemical model affects uncertainty in its response and quantify the relative importance of these features . We here use Sobol’s high dimensional model representation ( HDMR ) of biochemical regulatory networks to perform variance based global sensitivity analysis and to quantify the influence of signaling features [42 , 83] . The gene regulatory model is considered as a multivariate function of signaling features X ∈ {α1 , τ1 , … , αn , τn} , where these features acts as inputs , influencing the output IEG or TF responses , ℱ , either in an independent and/or cooperative way as shown below . F ( X ) = f 0 + ∑ i ≤ 2 n f i ( X i ) + ∑ 1 ≤ i < j ≤ 2 n f i j ( X i , X j ) + ⋯ + f 1 , 2 , ⋯ , 2 n ( X i , X 2 , ⋯ , X 2 n ) ( 10 ) where ℱ ( X ) ≡ ℱ ( t , y 0 , k , f s ( X , t ) ) as shown in Eq ( 1 ) , f0 denotes the mean value of the ℱ ( X ) over the entire variation range of X . The first order function fi ( Xi ) describes the independent behavior of Xi on output ℱ ( X ) . In addition , the functions also preserve a nonlinearity which is essential for establishing a general case . The second order function fij ( Xi , Xj ) represents the cooperative behavior between two input features Xi and Xj on output ℱ ( X ) . Similarly , 2nth order functions represents the cooperative effects of 2n signaling features acting together to influence the output ℱ ( X ) . We began this analysis by considering variability in signaling dynamics as a function of variability in signaling features , αi and τi in Eq ( 9 ) , as a result of continuous intrinsic and extrinsic fluctuations in cells [25] . The simultaneous Monte-Carlo sampling for each signaling feature was employed to induce variability , in consistent with experimental observation , using function ‘sobolset’ and ‘scramble’ in the Matlab computational environment [84] ( Table 1 for details on range of variation ) . HDMR decomposition measures the individual as well as higher order cooperative measurements of the variations in output ( downstream response ) in terms of variations in input ( s ) ( or signaling feature ( s ) ) . F ( X ) ≈ ∑ i ≤ 2 n f i ( X i ) ︸ first order + ( ∑ 1 ≤ i < j ≤ 2 n f i j ( X i , X j ) + ⋯ + f 1 , 2 , ⋯ , 2 n ( X i , X 2 , ⋯ , X 2 n ) ) ︸ higher order ( 11 ) Total variance V[R] for a response output R ≡ ℱ ( X ) can be decomposed into individual variances of each HDMR component function using mutual orthogonality [83 , 85] . The response R is the activity level of the downstream IEG or TF under observation . This is a very useful property of HDMR in determining how the uncertainty in downstream response ( R ) is influenced by the uncertainties in signaling features ( X ) . V [ R ] = ∑ i ≤ 2 n V ( E [ R | X i ] ) + ∑ 1 ≤ i < j ≤ 2 n V ( E [ R | X i , X j ] ) + ⋯ + V ( E [ R | X 1 , X 2 , ⋯ , X 2 n ] ) ( 12 ) When variation in each feature is assumed to be unbiased ( i . e . Xi’s having a uniform distribution ) S1 Fig , the Sobol sensitivity indices are defined as the ratio of decomposed partial variances to the total variance as shown below , where Si represents first order sensitivity indices , and S i T represents total sensitivity indices . S i = V ( E [ R | X i ] ) V [ R ] ( 13 ) S i T = V ( E [ R | X i ] ) + ∑ i j V ( E [ R | X i , X j ] ) + ⋯ + V ( E [ R | X 1 , X 2 , ⋯ , X 2 n ] ) V [ R ] ( 14 ) The difference between the two S i T − S i is considered as the higher order sensitivity index . The conditional variance in the sensitivity indices were estimated using Saltelli’s approach [86] . While estimating the indices , we assume that the biochemical system responded sufficiently and quickly to reach steady state before there was any change in the signaling feature ( otherwise the system would still be responding to a previous fluctuation in the features when the next change happened ) . The objective of the global sensitivity analysis is to identify the most important signaling feature affecting a downstream response at a given time . This is determined through identification of a feature which , when varied simultaneously along with all the features , leads to the greatest reduction in the variance of the downstream response . Similarly , second most important feature can be determined which leads to second greatest reduction in the variance of the downstream response , and so on until all features are ranked in the order of importance . Estimation of sensitivity indices captures this reduction in the variance through a continuous metric , bounded between 0 and 1 [86] . A sensitivity index of zero means that the associated feature is non-influential to a downstream response , while the higher the index the more influential the feature . If the sensitivity indices were lower than a preestablished threshold value ( in our case Si < 0 . 1 ) , we assumed that the signaling feature was not sensitive enough to lead to significant reduction in the response variation and was not important . This criteria was used throughout the study and in generation of the heatmap in Fig 8 . We applied information theory to quantify the amount of information transduced by each upstream signaling feature to downstream responses [87] . Feature-based mutual information is a quantity that measures the amount of information which can be predicted about a response , R , when the measurement of a signaling feature , Xi , is known . The quantity is calculated for each feature to any downstream response at a given time point . Feature based mutual information is a universal quantity by definition , and can be applied to any network topology regardless of its underlying physical basis or complexity . In biological networks , the network topology can be defined as a channel ( Fig 1A ) or a black box that maps the input functions of signaling dynamics to a downstream response . Because the distribution of the input signals are typically not known a priori , they can be considered as a function of random variables ( signaling features ) given as X in Eq ( 9 ) . For a input signal S = fs ( X , t ) , and downstream response R , mutual information I is defined as: I ( R ; X i ) ≡ H ( R ) - H ( R | X i ) = H ( X i ) - H ( X i | R ) = I ( X i ; R ) ( 15 ) where entropy H is H ( R ) ≡ E p { - log ( p ( R ) ) } = - ∑ R p R ( r ) log ( p R ( r ) ) and conditional entropy H ( R | X i ) = E p { - log ( p ( R | X i ) ) } ( 16 ) = - ∑ X i p X i ( x i ) ∑ R p R | X i ( r | x i ) log ( p R | X i ( r | x i ) ) ( 17 ) = - ∑ X i p X i ( x i ) H ( R | X i = x i ) ( 18 ) Since mutual information is always positive ( I ( Xi;R ) ≥ 0 ) , the conditional entropy is always less than individual entropy , H ( R∣Xi ) ≤ H ( R ) [87] . The resulting mutual information I ( Xi;R ) is then a concave function of pR ( r ) for fixed pR∣Xi ( r∣xi ) and a convex function of pR∣Xi ( r∣xi ) for fixed pR ( r ) . The conditional distribution characterizes these measurements . I ( X i ; R ) = E { log ( p X i R ( X i , R ) p X i ( X i ) p R ( R ) ) } ( 19 ) = ∑ R ∑ X i P R | X i ( r | x i ) P X i ( x i ) log ( P R | X i ( r | x i ) ∑ P R | X i ( r | x i ) p X i ( x i ) ) ( 20 ) = ∑ X i P X i ( x i ) ( H ( p X i ( x i ) ) - H ( P ( R | X i ) ) ) ( 21 ) The uniform distribution of signaling features Xi ensures the maximum possible entropy of H ( P X i ( x i ) ) = log ( 1 m a x ( X i ) − m i n ( X i ) ) . The intuition that the conditional distribution P ( R∣Xi ) is narrower or more concentrated than PXi ( xi ) is quantified by the fact that the entropy H ( P ( R∣Xi ) ) is smaller than H ( PXi ( xi ) ) , and this reduction in entropy is exactly the information when observing R provides about Xi , measured here in bits [88] . However , it should be noted that this equation allows us to measure the information carried by the response to recover estimates of the signaling features in each cells . We then assumed that the total as well as conditional distribution of downstream responses emerging from signaling activation followed a Gaussian distribution , whose variance was not dependent on Xi . In mathematical terms , this means V[R∣Xi] = EXi ( V[R∣Xi] ) . From the law of total variance V[R] = V ( E[R∣Xi] ) + E ( V[R∣Xi] ) , mutual information is estimated as: I ( R ; X i ) = H ( R ) - H ( R | X i ) = 1 2 log ( 2 π e V [ R ] ) - 1 2 log ( 2 π e V [ R | X i ] ) ( 22 ) = - 1 2 log E ( V [ R | X i ] ) V [ R ] = - 1 2 log V [ R ] - V ( E [ R | X i ] ) V [ R ] ( 23 ) = - 1 2 log ( 1 - V ( E [ R | X i ] ) V [ R ] ) ( 24 ) where entropy ( H ) of any Gaussian distribution pZ ( z ) , is H ( Z ) ≡ 1 2 log ( 2 π e V [ Z ] ) [60 , 87] . The information content between R and Xi in Eq ( 24 ) was estimated using Eq ( 13 ) . Since both the signaling feature and and the output response measure are assumed to be a Gaussian , the estimation of the mutual information gives us a lower bound on the information carried by a signaling feature to the downstream response [89] . We later make a generalization that the information is not conveyed by just one single signaling feature , but by a combination of features , as it was the case revealed during the analysis . It is also important to emphasize that the number of bits of information carried by a single feature has a meaning irrespective of the dynamic multiplexing resulting from the network topology . We employed decision tree analysis by generating 105 sets of simultaneous variation in the signaling features through application of Sobol sequence ‘MatousekAffineOwen’ algorithm to scramble a total of 18-dimensional pseudo-random numbers generated using Sobol’s method in MATLAB computational environment [84] . The responses to these variations were statistically grouped into three categories: High , Mid and Low phenotypes . High phenotype represented most positive deviation from the mean of the downstream response at the time point under consideration , Low phenotype meant the most negative deviation from the mean of the downstream response , and Mid represented the median of the response distribution . We then selected first 250 profiles from these groups to identify the rules of the binary tree , using the classification and regression trees ( CART ) algorithm [43] . The CART algorithm uncovers the predictive structure of how cells might be classified in various phenotypic groups based on variations in dynamical features of the upstream signals . The objective is to progressively split the cells ( or predictor dataset ) into smaller and smaller subsets until each subset corresponds to only one phenotype ( or a leaf of the tree ) . Each subset represent a hierarchy of binary rules defining the ranges of variation in the signaling features determined through a splitting criteria . In our study , the Gini index was used as the splitting criteria to construct the trees . The analysis was performed in the R statistical language [90] using the ‘rpart’ and the‘rpart . plot’ packages [91 , 92] .
|
Single cell studies have shown that differential patterns in the dynamics of signaling proteins , transcription factor activity , gene expression , etc . produce distinct downstream outcomes . The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules . Signaling pathways , therefore , serve as signal processing units to inform specific downstream regulation . However , the functional capabilities of the dynamic aspects of signaling are not well understood . To address this issue , we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity . Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations . Moreover , our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses . Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features
|
Tsetse flies use olfactory and gustatory responses , through odorant and gustatory receptors ( ORs and GRs ) , to interact with their environment . Glossina morsitans morsitans genome ORs and GRs were annotated using homologs of these genes in Drosophila melanogaster and an ab initio approach based on OR and GR specific motifs in G . m . morsitans gene models coupled to gene ontology ( GO ) . Phylogenetic relationships among the ORs or GRs and the homologs were determined using Maximum Likelihood estimates . Relative expression levels among the G . m . morsitans ORs or GRs were established using RNA-seq data derived from adult female fly . Overall , 46 and 14 putative G . m . morsitans ORs and GRs respectively were recovered . These were reduced by 12 and 59 ORs and GRs respectively compared to D . melanogaster . Six of the ORs were homologous to a single D . melanogaster OR ( DmOr67d ) associated with mating deterrence in females . Sweet taste GRs , present in all the other Diptera , were not recovered in G . m . morsitans . The GRs associated with detection of CO2 were conserved in G . m . morsitans relative to D . melanogaster . RNA-sequence data analysis revealed expression of GmmOR15 locus represented over 90% of expression profiles for the ORs . The G . m . morsitans ORs or GRs were phylogenetically closer to those in D . melanogaster than to other insects assessed . We found the chemoreceptor repertoire in G . m . morsitans smaller than other Diptera , and we postulate that this may be related to the restricted diet of blood-meal for both sexes of tsetse flies . However , the clade of some specific receptors has been expanded , indicative of their potential importance in chemoreception in the tsetse .
Trypanosomiasis management has been a longstanding development preoccupation in sub-Saharan Africa , with tsetse fly control constituting the cornerstone in this effort [1] . Since all tsetse species are able to transmit trypanosomes , the critical determinant of transmission is their obligate blood feeding . Tsetse flies select their hosts through visual and olfactory signals , a process that is mediated by olfactory and gustatory receptors . Tsetse flies navigate their environment by detecting and responding to volatiles and non-volatile cues ( odors and tastants ) . Artificial bait technologies , based on tsetse olfactory responses to natural cues and blends of synthetic versions that mimic those of their natural hosts in the field , have successfully been applied in tsetse control because of their relatively high specificity , low cost , community acceptability , and ability to slow down tsetse re-invasion from adjacent areas [2] , [3] . These technologies are environment friendly [4] , and applicable for riverine and savanna species of tsetse flies [5] , [6] . The attractants include various phenolic derivatives [7]–[9] , carbon dioxide , acetone , 1-octen-3-ol , and vertebrate host breath , skin and urine extracts [10]–[12] . Interestingly , 1-octen-3-ol is a constituent of the chemical profile from Lantana camara , an invasive plant to which tsetse flies are attracted [13] . The response to olfactory cues has also been exploited in design of tsetse repellents [14] , [15] . The repellents include guaiacol ( methylphenols ) , δ-octalactone and methylketones [16]–[18] and 2-methoxy-4-methylphenol [14] . Natural differential responses among tsetse species and even between sexes and allopatric populations of the same species have been observed [18]–[22] , which have stimulated research and design to enhance the efficiencies of the existing attractant-based bait technologies , to develop new ones based on repellent blends ( ‘push’ tactics ) from refractory animals , and to integrate these into ‘push-pull’ strategies . Different Glossina species exhibit different olfactory uniqueness' and this may partly account for the observed graduation of preferences for particular hosts . For instance , riverine tsetse species ( such as G . fuscipes fuscipes , G . palpalis and G . tachinoides ) prefer feeding on reptilian hosts compared to their savanna relatives ( G . morsitans morsitans , G . pallidipes ) that feed largely on ungulates and other large mammals [6] . Larvipostion pheromones ( n-pentadecane and n-dodecane ) from exudates of mature larvae are also known to attract and induce gravid G . m . morsitans and G . m . centralis females to aggregate and deposit larvae [23] . Research on response to tastants in tsetse flies are limited , but point to their potential application in tsetse control [10] , [24] . In all , responses to odors and tastants in tsetse have established utility in tsetse control that can be augmented with better understanding of the molecular factors that underpin these responses . Molecular factors mediating the olfactory and gustatory responses in the tsetse flies are poorly understood . However , research on other insects indicates that the odors and tastants in the environment are generally detected in peripheral sensory neurons by distinct odorant and gustatory receptors ( ORs and GRs ) [25]–[28] . These receptors are divergent members of a superfamily characterized by seven transmembrane domains , and share low sequence conservation among them except at the C-terminus region that coincides with the seventh trans-membrane domain [29] . The ORs and GRs are thought to have evolved as parallel chemoreceptors across diverse organisms [26] . Each OR is expressed in olfactory receptor neurons ( ORNs ) within maxillary palpi and antennae [25] , [30]–[32] . The ORs generally have multiple introns and are very divergent with poor structural conservation within and across insect orders and species [33] , [34] , which potentially reflect diverse olfaction related preferences within the orders and species . However , a canonical co-receptor commonly referred to as Orco remains highly conserved across insect orders [35]–[38] , a phenomenon that may be associated with its role in proper tuning of odor specificity and activation necessary for appropriate signal transduction in the neurons [39] . The GRs are generally expressed in gustatory receptor neurons ( GRNs ) within gustatory organs [40] in response to soluble taste and contact pheromones [41] , [42] . However , some GRs are expressed in antennal dendrites and respond to carbon dioxide , potentially implicating them in olfaction [40] , [43] . The GRs are more conserved in sequence and structure than the ORs [44] , [45] probably due to comparatively smaller search space among cues associated with GRs than ORs . The diversity among the ORs and GRs in tsetse can potentially shed light on the natural differential responses observed among them [12] , [17] , [18] , [20]–[29] , with potential application in tsetse control . To improve or develop new approaches of vector management , an understanding of the molecular attributes of GRs and ORs and their potential roles in tsetse ecology is essential . This study was initiated to ( 1 ) comparatively annotate and catalogue ORs and GRs in G . m . morsitans ( GMOY1 . 1 ) , ( 2 ) establish evolutionary distance between G . m . morsitans ORs or GRs and those in especially D . melanogaster , and ( 3 ) examine relative expression of the ORs and GRs in the G . m . morsitans . The assembly has been estimated to be over 99% complete based on the software Core Eukaryotic Genes Mapping Approach ( CEGMA ) [46] and manually sequenced BACs data . The assembly is currently undergoing genome-wide manual curation and annotation by the International Glossina Genome Initiative ( IGGI ) consortium .
Coding sequences ( CDS ) of ORs and GRs in Drosophila melanogaster were obtained from FlyBase5 . 13 [47] and used to isolate their respective homologs in the G . m . morsitans genome ( GMOY1 . 1 ) at VectorBase [48] using tBLASTx algorithm [49] . Scaffolds encoding the homologs were searched for and retrieved at a cut-off e-value <1 . 0e-05 . Whole transcriptome illumina 84 million RNA sequence reads generated from female G . m . morsitans [50] were mapped onto the scaffolds using default settings in CLC Genomics workbench suite Version 4 . 8 ( CLC Bio , Aarhus , Denmark ) . Gene loci of putative Glossina homologs were curated in the scaffold sequences flanking the tBLASTx hits , and intron/exons modeled using the RNA-seq mappings . The predicted gene models were viewed and edited using Artemis v13 . 2 . 12 [51] where , intron/exon boundaries were edited using motifs GT for 5′ donor site , and AG for 3′ acceptor site . The start codon ( ATG ) for each gene model was fixed at the 5′ end and the reading frame terminated at the first of any of the stop codons ( TAA , TGA , or TAG ) . Sequences shorter than average size of known insect ORs ( 370 aa ) were marked as incomplete if they lacked start or stop codons . Sequences with poorly conserved functional domains were considered as pseudogenes . The homologs were validated through sequence-based searches for presence of ORs or GRs specific 7tm-6-olf-recpt or 7tm-7-olf-recpt [29] , [52] domains respectively . The homologs were probed for the domains using DELTA BLAST algorithm [53] against the conserved domains databases ( CDD ) [54] , and presence of alpha helix trans-membrane domains validated using TMHMM server v2 . 0 [55] . Additionally , all the putative ORs or GRs were validated , using BLAST2GO analyses [56] against the non-redundant Swiss-Prot database [57] . The curated gene models were assigned annotation identifiers by comparing them with automated transcript feature models obtained from the Glossina community annotation portal at VectorBase [48] and edited using Artemis genome viewer tool [51] . The models without automated prediction matches and identifiers were manually built using the Artemis gene build tool window [51] and given unique temporary annotation identifiers . In this respect , features for gene , exons , mRNA , and CDS were created for such gene models . The Glossina gene models were assigned putative gene names where GmmOR* and GmmGR* were adopted for G . m . morsitans odorant receptors and gustatory receptors respectively ( the asterisk ( * ) being an identifier number ) . The annotated gene model features were submitted to the VectorBase community annotation portal for G . m . morsitans [48] for integration into genome database; nevertheless , a list of annotated amino acid coding sequences is presented in supplementary Dataset S1 , and a list of associated gene identities in Table S2 . The G . m . morsitans receptor repertoires were evaluated against those documented for D . melanogaster , Anopheles gambiae , Aedes aegypti , Apis mellifera , Nasonia vitripennis , Camponotus floridanus , Harpegnathos saltator and Tribolium casteneum ( references in Table 1 ) . MUltiple Sequence Comparison by Log-Expectation ( MUSCLE ) tool [58] was used to align GmmORs and GmmGRs with homologs in D . melanogaster , and the alignments edited using Jalview web-server [59] . The secondary structures in the alignments were predicted using JPred program [60] . Phylogenetic cluster inference was done using Maximum Likelihood approach with best fitting Wheelan and Goldman+Freq ( WAG+F ) model [61] , which was chosen as the best ranked from a panel of all amino acid model tests run in MEGA5 [62] . The initial tree was automatically generated and bootstrapped with 500 iterations . The evolutionary rate difference among sites was modeled using a discrete Gamma distribution ( 5 categories ( +G , parameter = 4 . 2651 ) ) . The rate variation model allowed for some sites to be evolutionarily invariable ( [+I] , 0 . 8705% sites ) . All positions with less than 95% site coverage were eliminated and branch nodes determination set at very strong . Evolutionary analyses were conducted using the MEGA5 suite [62] . The expression profiles of G . m . morsitans ORs and GRs gene loci were determined using whole transcriptome 84 million illumina RNA-sequence reads [50] . The RNA-seq reads were mapped onto the G . m . morsitans ORs or GRs nucleotide coding sequences ( CDS ) in CLC Genomics Workbench ( CLC Bio , Aarhus , Denmark ) via RNA-seq analysis pipeline with default settings . The expression profiles were presented as reads per kilobase of exon model per million mapped reads ( RPKM ) for each receptor sequence [63] .
Numbers of OR and GR gene loci recovered in G . m . morsitans , relative to those published in other insects are summarized in Table 1 . Similar to most insects , the G . m . morsitans has more ORs loci than GRs loci , with the exception of D . melanogaster where the numbers are equal . However , the G . m . morsitans ORs are fewer than those documented in all the insects evaluated , including D . melanogaster . A similar trend was exhibited in G . m . morsitans GRs , except in relation to A . mellifera . Annotation of G . m . morsitans ORs and GRs are summarized in Table 2 . The lengths of G . m . morsitans ORs varied between 260 and 541 amino acids , while those of G . m . morsitans GRs ranged from 309 to 514 amino acids . The number of exons ranged between two and eight or 12 in GRs and ORs respectively . The predicted genome structures are given in Figure S1 . The frequency of detectable trans-membrane domains was also variable , with proteins having six trans–membrane domains representing about one half of all genes . The G . m . morsitans ORs ( 57% , 26 out of 46 ) were homologous to nine D . melanogaster ORs . Similarly , most of the G . m morsitans GRs ( 57% , 8 out of 14 ) were homologous to three D . melanogaster GRs genes . The remainder of the G . m . morsitans GRs had one-to-one homology with a single D . melanogaster specific homolog . Reciprocal blasts onto non-redundant protein databases for both G . m . morsitans ORs and GRs are summarized in Supplementary material – Table S1 ) . GmmGR3 and GmmGR4 were also homologous to An . gambiae orthologs , while GmmGR5 , GmmGR8 and GmmGR13 had homologs to genes in other Drosophila species . The G . m . morsitans ORs pseudogenes were scanty , representing 7% of the ORs genes recovered . Only GmmOR5 had alternative splice variants . The 7tm-6-olfct-rcpt domain was detected in all G . m . morsitans ORs , and the 7tm-7-chem-rcpt domain was detected in five ORs ( GmmOR17 , GmmOR21 , GmmOR24 , GmmOR38 and GmmOR39 ) . The 7tm-7-chem-rcpt domain was also detected in all the G . m . morsitans GRs . Phylogenetic relationships between G . m . morsitans ORs and GRs and their counterparts in D . melanogaster are summarized in Figure 1 . Most of the G . m . morsitans ORs and GRs clustered with their respective ORs and GRs orthologs with a bootstrap support of over 80% . The G . m . morsitans OR14 , OR15 and OR16 were homologous to a drosophila larvae receptor , Or45a . The G . m . morsitans co-receptor ( Orco ) ( GmmOR1 ) had 100% bootstrap support homology to D . melanogaster homolog , Or63b , and was a single copy in the genome , similar to other insects investigated ( data not shown ) . There was an expanded cluster of ORs in G . m . morsitans ( GmmOR41-46 ) , relative to a single D . melanogaster homolog , Or67d ( Figure 1A ) , which also had multiple copies in An . gambiae , Cu . quinquefasciatus , Ae . aegypti , Tribolium casteneum ( Data not shown ) . The G . m . morsitans and D . melanogaster GRs clustered into four groups ( Figure 1B ) . Four G . m . morsitans GRs ( GmmGR1-4 ) clustered with homologs of CO2 receptors , Gr21a and Gr63a in D . melanogaster; GmmGR6-7 and GmmGR14 , though distantly , clustered with an unusual splice variant DmelGr28a/28b; GmmGR5 , 8–12 were homologous to bitter taste-related sensors in D . melanogaster; and GmmGR13 clustered distantly to DmelGr58a/58b homologs , whose functions are unknown . Relative expression profiles of the G . m . morsitans ORs and GRs gene loci are summarized in Figure 2 . Among the G . m . morsitans ORs , expression of GmmOR15 was surprisingly most predominant , accounting for more than 90% of the total RNA-sequence data supporting expression of the ORs . GmmOR15 is homologous to Or45a gene in D . melanogaster . About 5% of RNA-sequence data provided supporting evidence for expression of GmmOR2 , GmmOR1 ( Orco homolog ) , GmmOR43 and GmmOR9 . Expressions of GmmOR8 , GmmOR11 , GmmOR25 , GmmOR31 , and GmmOR39 were not detected in the available RNA-sequence dataset ( Figure 2A ) . Amongst the GRs , GmmGR1-4 had the best RNA-sequence data expression support ( Figure 2B ) .
Specific groups of the G . m . morsitans ORs and GRs were clustered within selected scaffolds . Similar clusters of genes performing common and related functions have been observed among chemosensory genes in D . melanogaster [41] , [42] , [44] , and more recently among twelve G . m . morsitans major milk proteins associated with lactation [50] . Since genes within clusters are generally co-regulated and can lead to joint gene expression [29] , [34] , [64] , the individual clusters of ORs and GRs might be under common regulatory mechanisms and in response to common or related stimuli . The ORs and GRs in G . m . morsitans were fewer than those documented in most insects evaluated ( Table 1 ) [65] , [66] . Additionally , specific ORs and GRs in D . melanogaster ( nine and three ORs and GRs respectively ) appear to have been expanded in G . m . morsitans , representing more than half of the chemoreceptors . The factors underlying the apparent reductions and expansions of these receptors in the tsetse are unknown . However , it can be postulated that the obligate blood feeding of the tsetse fly ( restricted to vertebrate hosts ) relative to D . melanogaster ( with expansive fruit species hosts ) might have necessitated evolutionary selection for specific chemoreceptor loci relevant to discriminate among limited host choices . We know also that environmental factors can determine host choice , as tsetse have been shown to have an acquired preference to specific hosts encountered early in life [67] . Notably , other blood-feeders , such as mosquitoes also seek a variety of plant sources for sugar as energy source , while tsetse flies derive their energy from the amino acids proline and alanine [68] . The G . m . morsitans OR15 ( GmmOR15 ) accounted for more than 90% of the OR expression data . This OR is homologous to DmelOr45a , whose product has been , associated with an escape response in D . melanogaster larvae [69] . The function of this OR in tsetse was not determined; nonetheless it is notable that the source of RNA sequence data was a reproductively active adult female . Hence , it is possible that the GmmOR15 is in some way associated with larval activity . Similarly , the GmmGR1-4 cluster was most prominent among the GRs homologous to CO2 receptors in D . melanogaster . These GRs may be associated with host seeking and may have a duplicate role in olfaction . These receptors may putatively be associated with attractive responses elicited by the savanna tsetse species , including G . m . morsitans [10] . From the foregoing , it is evident that tsetse seems to prioritize and invest on a select few chemoreceptor genes towards their adaptive behaviors . Indeed , a heavy investment in specific genes is not uncommon in insects [70]–[73] . The G . m . morsitans OR1 ( homologous to Orco ) was the most conserved amongst the G . m . morsitans ORs , not surprising since such conservation has been observed in other insects [74] probably due to its critical role in modulating responses of the other receptors . In conclusion , when examined against other blood feeders , which also take sugar sources from plants ( e . g . An . gambiae and Ae . aegypti ) , the G . m . morsitans has a reduced repertoire of ORs and GRs genes . There is a complete loss of receptors for sugar , and a heavy investment in some chemoreceptors , such as those associated with detection of CO2 . These observations offer opportunities to develop control tools exploiting these unique adaptations .
|
Tsetse flies navigate their environments using chemosensory receptors , which permit them to locate hosts , mating partners , and resting and larviposition sites . The genome of G . m . morsitans was interrogated for coding genes of odorant receptors ( ORs ) and gustatory receptors ( GRs ) that express in antennae and maxillary palp , and detect the volatile and soluble chemical signals . The signals are then transmitted to the central nervous system and translated to phenotypes . Majority of these genes in G . m . morsitans were spread across different scaffolds , but a few were found to occur in clusters , which suggested possible co-regulation of their expression . The number of ORs and GRs were much reduced in the G . m . morsitans genome , including the apparent loss of receptors for sugar when compared to selected Diptera . There was also an apparent numerical expansion of some receptors , presumably to maximize on their restricted blood-meal diet . The annotation of the chemoreceptor package of G . m . morsitans provides a resource for investigating key activities of tsetse flies that could be exploited for their control .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"genomics",
"genome",
"evolution",
"genetics",
"biology",
"and",
"life",
"sciences",
"comparative",
"genomics",
"computational",
"biology",
"evolutionary",
"biology"
] |
2014
|
Odorant and Gustatory Receptors in the Tsetse Fly Glossina morsitans morsitans
|
Malaria burden in Brazil has reached its lowest levels in 35 years and Plasmodium vivax now accounts for 84% of cases countrywide . Targeting residual malaria transmission entrenched in the Amazon is the next major challenge for ongoing elimination efforts . Better strategies are urgently needed to address the vast reservoir of asymptomatic P . vivax carriers in this and other areas approaching malaria elimination . We evaluated a reactive case detection ( RCD ) strategy tailored for P . vivax transmission in farming settlements in the Amazon Basin of Brazil . Over six months , 41 cases detected by passive surveillance triggered four rounds of RCD ( 0 , 30 , 60 , and 180 days after index case enrollment ) , using microscopy- and quantitative real-time polymerase chain reaction ( qPCR ) -based diagnosis , comprising subjects sharing the household ( HH ) with the index case ( n = 163 ) , those living in the 5 nearest HHs within 3 km ( n = 878 ) , and individuals from 5 randomly chosen control HHs located > 5 km away from index cases ( n = 841 ) . Correlates of infection were identified with mixed-effects logistic regression models . Molecular genotyping was used to infer local parasite transmission networks . Subjects in index and neighbor HHs were significantly more likely to be parasitemic than control HH members , after adjusting for potential confounders , and together harbored > 90% of the P . vivax biomass in study subjects . Clustering patterns were temporally stable . Four rounds of microscopy-based RCD would identify only 49 . 5% of the infections diagnosed by qPCR , but 76 . 8% of the total parasite biomass circulating in the proximity of index HHs . However , control HHs accounted for 27 . 6% of qPCR-positive samples , 92 . 6% of them from asymptomatic carriers beyond the reach of RCD . Molecular genotyping revealed high P . vivax diversity , consistent with complex transmission networks and multiple sources of infection within clusters , potentially complicating malaria elimination efforts .
Over one-third of the world's population is currently at risk of infection with Plasmodium vivax , the human malaria parasite with the widest global distribution [1] . Where both species coexist , P . vivax typically causes less severe cases and deaths than P . falciparum , but its distinctive biological features pose major challenges for malaria elimination strategies focused on early diagnosis and prompt treatment of blood-stage infections . First , low-density P . vivax infections , which are often subpatent ( i . e . , missed by conventional microscopy ) and asymptomatic , predominate in areas approaching malaria elimination , rendering accurate clinical and laboratory diagnosis more difficult [2] . Accordingly , 54% of the P . vivax infections detected by polymerase chain reaction ( PCR ) in a rural Amazonian cohort were missed by microscopy; 57% of them caused no clinical signs or symptoms suggestive of malaria and 33% were both subpatent and asymptomatic [3] . Second , P . vivax remains silent within human hosts for several months as hypnozoites , the dormant liver stages that may eventually cause relapses [4] . Finally , compared to other human malaria parasites , P . vivax transmission is favored by the earlier production within human hosts of blood stages that are infective to mosquito vectors ( mature gametocytes ) as well as by the faster development within vectors of stages that are infective to humans ( sporozoites ) [5] . Brazil is one of the 14 ( out of 21 ) malaria-endemic countries in the region of the Americas that achieved a ≥ 75% reduction in their case incidence rates between 2000 and 2015 , as called for by the United Nations' Millennium Development Goals [6] . This country currently contributes 37% of the regional malaria burden , with 142 , 314 microscopically confirmed cases in 2015 ( http://portalsaude . saude . gov . br/index . php/o-ministerio/principal/leia-mais-o-ministerio/662-secretaria-svs/vigilancia-de-a-a-z/malaria/11346-situacao-epidemiologica-dados ) . Most transmission hotspots are located in remote sites across the Amazon Basin . This vast region , with 60% of the country's territory but only 13% of its total population , accounts for 99 . 5% of all malaria cases diagnosed in Brazil; 84% of them are due to P . vivax [7] . Eliminating residual foci when malaria is steadily declining and reaching pre-elimination stages is the next major challenge in Brazil [7] . Routine surveillance targets subjects presenting with fever or with a history of recent fever or other malaria-related symptoms and signs , who are screened for malaria parasites by microscopy or rapid diagnostic tests ( RDT ) followed by antimalarial treatment if found to be infected [8] . Accordingly , nearly 60% of all infections in Brazil are laboratory-confirmed within 48 hours after the onset of symptoms , preventing severe morbidity [7] . However , this strategy overlooks asymptomatic infections that might be otherwise detected by periodic cross-sectional surveys of the entire population at risk [9] . As malaria transmission declines , large populations must be screened to diagnose relatively few asymptomatic carriers , and diagnostic techniques available for large-scale use , such as microscopy and RDT , are not sensitive enough to detect low-grade infections that are typical of residual malaria settings [10] . Here , we evaluate a reactive case detection ( RCD ) strategy [11] that has been tailored for rural Amazonian communities with low P . vivax endemicity and substantial spatial clustering of malaria episodes [12 , 13] . We sought to test three hypotheses: ( a ) individuals living in close proximity to malaria cases diagnosed by routine surveillance ( index cases ) are more likely to carry malaria parasites ( including subpatent and asymptomatic infections ) than randomly selected inhabitants living in the same area; ( b ) the increased risk of infection in the proximity of index cases persists over the next few months , due to the relapsing nature of P . vivax; and ( c ) molecular genotyping of parasites may help to determine the epidemiological connections among cases found by RCD .
Acrelândia is a municipality situated in the easternmost corner of Acre State , Amazon Basin of Brazil , bordering with the States of Amazonas and Rondônia ( S1 Fig ) . The municipality covers a territory of 1 , 808 km2 between the Abunã and Iquiri Rivers , in the Acre River Valley , with a population of 14 , 120 inhabitants . Nearly half of them live in the town of Acrelândia ( 9°49'40''S , 66°52'58''W ) , located 35 km northeast of Plácido de Castro ( the nearest urban center , on the border with Bolivia ) and 112 km east of Rio Branco ( capital city of Acre ) . With its equatorial humid climate , this area receives the most rainfall ( annual average , 2 , 198 . 5 mm ) between December and March . The mean annual temperature is 24 . 5°C . Subsistence agriculture and cattle raising are the main economic activities , with coffee , bananas , and rice being the predominant cash crops . Acrelândia has experienced a major decline in malaria incidence in recent years [14] , but year-round residual malaria transmission persists in farming settlements surrounding the town , with 163 slide-confirmed cases in 2009 , 248 in 2010 , 104 in 2011 and 79 in 2012 ( http://portalsaude . saude . gov . br/index . php/o-ministerio/principal/leia-mais-o-ministerio/662-secretaria-svs/vigilancia-de-a-a-z/malaria/11346-situacao-epidemiologica-dados ) . Although both P . falciparum and P . vivax were transmitted in the early 2000s , with the latter accounting for over 80% of all infections [14] , only P . vivax has been observed in slide-confirmed indigenous cases since 2010 . Selective indoor spraying with residual insecticides , implemented in early 2008 , and the widespread distribution of long-lasting insecticide-treated bed nets , since 2010 , are likely to have contributed to the dramatic reduction of malaria transmission ( with the virtual elimination of P . falciparum ) in Acrelândia in recent years . The main local vector is the highly anthropophilic and exophilic Anopheles darlingi [15] , but An . benarrochi s . l . , An . oswaldoi s . l . , and An . konderi s . l . are also often found in larval habitats [16] . The study design is summarized in Fig 1 . Briefly , the RCD strategy was triggered whenever an eligible malaria case was detected by routine passive surveillance ( “index case” ) ; subjects who shared the household ( HH ) with the index case ( “index HH” members ) and their neighbors within a defined radius ( “neighbor HH” members ) were screened for malaria parasites in search of secondary infections . We also enrolled randomly chosen subjects in “control HHs” located beyond a 5-km radius from index HHs , but in the same or nearby locality , to serve as controls putatively unexposed to secondary infections originating from the vicinity of index cases . The vast majority of the 1 , 923 study participants were enrolled between January and July 2013 , except for those who were later recruited to replace individuals leaving the site before study completion ( see below ) . Inclusion criteria were: ( a ) all subjects aged ≥ 3 months living in the selected HHs who provided written informed consent , or with parental written informed consent and assent when age-appropriate; and ( b ) subjects with plans to remain in the study site for the next six months . Exclusion criteria included mental disorders and any chronic or acute condition that , in the opinion of the field nurse , might affect the results of the study or the subjects' ability of understanding the study objectives and of providing informed consent . Participants were classified into the following categories: ( a ) 41 index cases—had a P . vivax infection diagnosed by routine surveillance during the enrollment period and were recruited in one of the two only government-run malaria clinics currently operating in Acrelândia ( one in the urban area and one on the BR 364 highway , next to the border with the State of Rondônia; no other public or private facilities provide malaria diagnosis and treatment in this area ) ; ( b ) 163 index HH members—subjects who shared the HH with the index case; ( c ) 878 neighbor HH members—subjects living in the five nearest houses , within a radius of up to 3 km ( average , 731 m ) away from the index HH ( neighbor HH members ) ; ( d ) 841 control HH members—subjects living in five randomly chosen houses located in the same or nearby locality but > 5 km ( average , 12 km ) away from the index HH . The 5-km radius was chosen based on the maximal flight range reported for An . darlingi in the Amazon [17] to ensure that control subjects are unlikely to share common sources of infection with index cases and their neighbors . Home visits were made to eligible subjects on day 0 ( at enrollment , within 48 hrs of the index case diagnosis ) and 30 ( ±5 ) , 60 ( ±5 ) , and 180 ( ±10 ) days after the first visit , with a total of 6 , 172 observations . Visits were scheduled to maximize the probability of finding infections that had been acquired at the same time as the index case ( day 0 ) , those that were secondary to the index case or other infections diagnosed at baseline ( day 30 ) , new infections originating from these secondary infections ( day 60 ) , as well as most relapses , which are usually recorded in local strains of P . vivax up to 180 days after the primary infection [18] . In every visit , subjects were interviewed by our field team , consisting of two biologists and a nurse , and invited to provide a 5-mL venous blood sample ( index cases ) or finger-prick blood sample ( all other study participants ) for malaria diagnosis by both thick-smear microscopy and quantitative real-time PCR ( qPCR ) , regardless of any clinical symptoms ( S1 Fig ) . During home visits , 6 , 028 blood samples were collected , with only 144 ( 2 . 4% ) refusals . Neighbor or control HH members who left the study site over the six-month study period ( n = 151 ) were replaced with their nearest neighbors available . Laboratory diagnosis of malaria was based on microscopic examination of thick smears and qPCR . A total of 6 , 028 thick blood smears were stained with Giemsa and examined for malaria parasites under 1000 × magnification in our laboratory in the town of Acrelândia . At least 100 fields ( corresponding to approximately 0 . 3 μL of blood ) were examined before a slide was declared negative . We additionally used qPCR to detect and quantify P . vivax parasitemia on 5 , 969 clinical samples . To this end , we isolated DNA templates using Qiagen ( Hilden , Germany ) kits . We used QIAamp DNA blood kits for 200-μL venous blood samples and QIAmp DNA micro kits for 5-μL capillary blood samples eluted from three 3-mm bloodspots excised from FTA Micro Cards ( Whatman , Clifton , NJ ) . Final DNA elution volumes were 20 μL for bloodspots and 200 μL for whole blood . Each 20 μL qPCR mixture contained 2 μL of template DNA ( corresponding to approximately 2 μL of venous blood or 0 . 5 μL of finger-prick blood ) , 10 μL of 2× Maxima SYBR Green qPCR master mixture ( Fermentas , Burlington , Canada ) and 0 . 5 μM of each of the primers P1 ( ACG ATC AGA TAC CGT CGT AAT CTT ) and V1 ( CAA TCT AAG AAT AAA CTC CGA AGA GAA A ) [19] , which allow the amplification of a 100-base pair ( bp ) fragment of the P . vivax 18S rRNA gene . Standard curves were prepared with serial tenfold dilutions of the target sequence , cloned into pGEM-T Easy vectors ( Promega , Madison , WI ) , to allow for parasite load quantitation ( number of parasites/μL of blood ) . We used a Step One Plus Real-Time PCR System ( Applied Biosystems , Foster City , CA ) for PCR amplification with a template denaturation step at 95°C for 10 min , followed by 40 cycles of 15 sec at 95°C and 1 minute at 60°C , with fluorescence acquisition at the end of each extension step . Amplification was followed by a melting program consisting of 15 sec at 95°C , 15 sec at 60°C , and a stepwise temperature increase of 0 . 2°C/sec until 95°C , with fluorescence acquisition at each temperature transition . The detection threshold of this diagnostic qPCR is approximately 3 . 3 parasites/μL of blood . No-template controls ( containing all reagents for amplification except for the DNA template ) were run for every qPCR microplate . To estimate the relative contribution of particular subgroups of infected individuals to the total P . vivax biomass found in the study population , we summed up all individual qPCR-derived P . vivax densities and calculated the fraction corresponding to each subgroup . Because parasite densities are expressed as parasites per blood volume , we assumed that average whole-body blood volumes ( volemias ) do not differ across subgroups . Parasite genotyping was carried out on microscopy- and qPCR-positive samples to classify infections as either ( a ) recrudescences , homologous relapses or persisting untreated infections ( identical multilocus genotype reappearing in repeated infections in the same subject ) , ( b ) related infections in different subjects ( identical or similar genotypes appearing in different subjects either in the same or consecutive RCD rounds , suggestive of local—within cluster—transmission ) or ( c ) unrelated , sporadic infections or heterologous relapses ( different genotypes ) , as described in Fig 2 . Note that relapses may originate from reactivation of either the same parasite clone found in the primary bloodstream infection ( homologous hypnozoites ) or another , genetically different clone ( heterologous hypnozoites ) [20 , 21] . We used nested PCR [22] to genotype three highly polymorphic single-copy markers: one variable domain of the merozoite surface protein-1 gene ( msp1F3 [23] ) and two microsatellite DNA markers , Pv3 . 27 [24] and MS16 [25] . PCR products were analyzed by capillary electrophoresis on an automated DNA sequencer ABI 3500 ( Applied Biosystems ) , and their lengths ( in bp ) and relative abundance ( peak heights in electropherograms ) were determined using the commercially available GeneMapper 4 . 1 ( Applied Biosystems ) software . DNA samples from 361 laboratory-confirmed P . vivax infections diagnosed during the study ( 66 from index cases and 295 from other study participants ) were tested; 343 ( 95 . 0% ) of them were successfully genotyped at all 3 loci . The minimal detectable peak height was set to 200 arbitrary fluorescence units . Because stutter bands may be occasionally observed in microsatellite genotyping , we scored two alleles at a locus only when the minor peak was > 33% the height of the predominant peak . Only two samples , out of 343 that were fully genotyped , had minor alleles with peaks of > 200 fluorescence units that were discarded ( one in each sample ) because they failed to reach one-third of the height of the major peak in the same sample . Samples were considered to contain multiple clones if at least one locus showed more than one allele . Multilocus genotypes were defined as unique combinations of alleles at each locus analyzed . Although our descriptive analysis considered the most abundant allele ( as judged by peak heights ) at each locus for assigning predominant genotypes to multiple-clone infections , we also considered all possible genotypes in pairwise comparisons of samples from the same subjects ( recurring infections ) or within the same cluster ( comprising the index and neighbor HHs ) . For this purpose , we listed all combinations of alleles , considering major and minor peaks , in each pair . Samples were considered to harbor identical parasites when they had exactly the same predominant ( or only ) genotype; they were considered to harbor similar parasites when the same alleles ( considering major and minor peaks ) were found at all loci , but their predominant genotypes were different . A laboratory-confirmed P . vivax infection was defined as any episode of parasitemia detected by thick-smear microscopy , qPCR , or both . Subpatent infections were defined as infections confirmed by qPCR but missed by microscopy . We defined symptomatic malaria as a laboratory-confirmed infection , irrespective of the parasite density , diagnosed in a subject reporting one or more signs and symptoms investigated ( fever , headache , arthralgia , myalgia , and lower back pain ) , up to seven days before the interview . Subjects with laboratory-confirmed infections , irrespective of the parasite density , but no malaria-related signs or symptoms up to seven days prior to the interview , were classified as asymptomatic parasite carriers . All study participants with vivax malaria infection confirmed by onsite microscopy , either symptomatic or not , received free-of-charge treatment with chloroquine ( total dose , 25 mg of base/kg over 3 days ) and primaquine ( 0 . 5 mg of base/kg/day for 7 days ) following the latest antimalarial therapy guidelines of the Ministry of Health of Brazil [26] . According to these guidelines , infections in microscopy-negative subjects that were later detected by qPCR were not routinely treated . Data were entered and cleaned using EpiInfo version 3 . 5 . 2 software ( Center for Diseases Control and Prevention , Atlanta , GA ) and analyzed with STATA version 14 . 1 software ( Stata , College Station , TX ) . Proportions were compared by applying standard χ2 of Fisher exact tests tests to contingency tables . Statistical significance was defined at the 5% level ( two-tailed tests ) and 95% confidence intervals ( CI ) were estimated whenever appropriate . Regression models were run to identify correlates of P . vivax infection in one or more RCD rounds , regardless of any symptoms , stratified by diagnostic method: microscopy ( n = 5 , 866 complete observations ) and qPCR ( n = 5 , 807 complete observations ) in one or more RCD rounds , regardless of any symptoms . Index cases were excluded from this analysis . Individual-level variables included in the model were: age , gender , occupation , past migratory history , travel history during the last month , overnight stays in the forest in the last month ( for hunting or fishing ) , use of vector control measures such as bed nets , history of slide-confirmed P . vivax malaria within the past six months , history of malaria-related symptoms and signs within the previous week , and use of antimalarials within the past month . HH-level variables were: HH location , determined with the use of global positioning system ( GPS ) receivers , and HH size . S1 Database , provided as supplementary material online , has the variables included in the logistic regression models . Due to the nested structure of the data , which introduces dependency among observations that can affect model parameter estimates , we used the “melogit” STATA command to build mixed-effects logistic regression models that included the grouping variables as random factors . We had repeated observations ( on days 0 , 30 , 60 , and 180; grouping variable , “round” ) nested within subjects ( grouping variable , “individual” ) who were clustered within HHs ( grouping variable , “household” ) . Finally , HHs were clustered within index cases ( grouping variable , “index case” ) , since the inclusion of each index case triggered the recruitment of matched study participants ( other members of the index HHs , neighbor HHs , and control HHs; Fig 1 ) . Covariates were introduced in the models in a stepwise forward approach , and only those that were associated with the outcome at a significance level of at least 20% were retained in the final model . Cases with missing information were excluded . In addition , we fitted mixed-effects Poisson regression models to the data , but the random-effects variances associated with the estimates were substantially higher than those obtained with the logistic models described above . Thus , here we only present results derived from the logistic regression analysis . As required for all observational studies published by PLoS Neglected Tropical Diseases , this article includes the STROBE ( STrengthening the Reporting of OBservational studies in Epidemiology ) checklist to document its compliance with STROBE guidelines ( S1 Checklist ) . Study protocols were approved by the Institutional Review Board of the Institute of Biomedical Sciences , University of São Paulo ( approval number , 228 . 720 ) . Written informed consent was obtained from all study participants or their parents/guardians .
The study population comprised 1 , 923 subjects ( 13 . 6% of the total population of Acrelândia ) , who were aged between 3 months and 89 years ( mean , 26 . 5 years ) and had been living in the study site for 1 month to 64 years ( mean , 9 years ) . Most ( 1 , 037 or 53 . 9% ) were males and 1 , 613 ( 83 . 9% ) were born in the Amazon . Nearly half of them reported previous slide-confirmed malaria episodes , but <5% had a P . vivax infection within the past six months . Eighteen percent reported overnight stays in the forest in the last month . Study participants were distributed into seven localities of the municipality of Acrelândia: Gleba Porto Luiz ( PTL ) , Projeto Santo Antônio Peixoto ( SAP ) , Projeto São João do Balanceio ( SJB ) , Projeto Redenção ( RED ) , Projeto Orion ( ORI ) , Reserva Porto Dias ( PTD ) , and Projeto Pedro Peixoto ( PEP ) . Individual HH locations are shown in Fig 3 . The 41 index cases corresponded to 21 . 9% of all P . vivax infections diagnosed in Acrelândia between January and July 2013; between 19 ( March ) and 98 ( July ) cases were diagnosed monthly during the study period . Index cases were tentatively classified as indigenous , relapsing , or imported according to classical eradication-era criteria [27]; 6 were considered imported ( cases that could be traced to a locality outside the study site ) , 11 were putative relapses ( cases diagnosed in subjects with a history of P . vivax infection within the past six months ) , and 24 were considered indigenous ( cases that were locally acquired or autochthonous ) . Index cases , other index HH members and neighbor HH members were recruited in all localities listed above , except for PEP; 22 ( 53 . 6% ) index cases lived in PTL . A total of 108 infections were diagnosed by microscopy by our field team over the entire study period . Plasmodium vivax positivity rates differed significantly across HH types ( Table 1 ) . They were relatively high ( range , 4 . 1–8 . 2% ) in index HHs ( 35 positive slides , 6 . 1% , among 577 examined , 12 of them from asymptomatic subjects; index cases excluded ) and much lower ( 0 . 0–0 . 3% ) in control HHs ( 3 positive slides , 0 . 1% , among 2 , 586 examined , one of them from an asymptomatic carrier ) . No slide was positive for P . falciparum or P . malariae . These results indicate that index HH members are much more likely to carry patent P . vivax parasitemias than randomly selected inhabitants living in the same area ( control HH members ) ; interestingly , the highest prevalence of infection was observed in index HHs not only at the first visit but also over the following RCD rounds . Whether RCD strategies should target only index HHs or be extended to neighbor HHs remains a matter of debate . We found widely variable slide positivity rates in neighbor HHs across RCD rounds in our population , ranging from 0 . 7% ( day 0 ) to 4 . 4% ( day 60 ) ( Table 1 ) . In the first RCD round , slide-confirmed infections were as rare in neighbor HHs as they were in control HHs ( Fisher exact test , P = 0 . 220 ) , suggesting that targeting neighbor HH members on day 0 would not be more effective for diagnosing additional patent infections than randomly screening subjects living in the same locality . In contrast , positivity rates in all other RCD rounds were similar between index HHs and neighbor HHs ( Fisher exact test , P > 0 . 05 in all comparisons ) and significantly higher than those in control HHs ( Fisher exact test , P < 0 . 001 in all comparisons ) , indicating that screening index and neighbor HH members would be similarly effective for diagnosing new infections between days 30 and 180 ( Table 1 ) . Overall , 70 slides ( 2 . 6% ) from neighbor HHs were positive among 2 , 703 examined . Thirty-two ( 45 . 7% ) positive slides were from asymptomatic carriers who were likely to be missed by routine malaria surveillance . We next compared P . vivax prevalence diagnosed by qPCR in index HHs ( excluding the index case ) , neighbor HHs and control HHs during the four RCD rounds ( Table 2 ) . Not surprisingly , qPCR detected more than double the number of infections than microscopy ( 293 positive samples among 5 , 807 examined ) . Overall qPCR positivity rates were 10 . 6% in index HHs ( 1 . 7-fold higher than microscopy ) , 5 . 7% in neighbor HHs ( 2 . 2-fold higher than microscopy ) , and 3 . 2% in control HHs ( > 30-fold higher than microscopy ) . The vast majority ( 75 or 92 . 6% ) of infections diagnosed by qPCR in control HHs were asymptomatic , consistent with a silent circulation of P . vivax in the community that would be entirely missed by RCD . Significantly different qPCR positivity rates were found across HH types , ranging from 6 . 8% to 15 . 0% in index HHs , 5 . 0% to 7 . 0% in neighbor HHs , and 0 . 3 to 5 . 6% in control HHs ( Table 2 ) . On days 0 and 30 , positivity rates were significantly higher in index HHs ( excluding the index cases ) than in neighbor and control HHs ( Fisher exact test , P < 0 . 001 in both comparisons ) but they were similar when comparing neighbor to control HHs ( Fisher exact test , P > 0 . 05 in both comparisons ) . Therefore , targeting neighbor HHs in search of new infections would not be more effective than randomly sampling community members on days 0 and 30 . However , similar infection rates were found in index and neighbor HHs on day 60 ( Fisher exact test , P = 0 . 231 ) and day 180 ( Fisher exact test , P = 0 . 413 ) . Furthermore , significantly higher infection rates were detected by qPCR in neighbor than control HHs in the last two RCD rounds ( Fisher exact test , P < 0 . 001 in both comparisons ) . Microscopy diagnosed 1 . 9-fold and 1 . 2-fold more symptomatic than asymptomatic infections in index and neighbor HHs , respectively ( Table 1 ) , suggesting that many of these additional slide-positive subjects would be eventually detected by routine surveillance of febrile illnesses . Conversely , qPCR revealed 1 . 8-fold and 2 . 4-fold more asymptomatic than symptomatic infections in index HHs and neighbor HHs , respectively ( Table 2 ) . Moreover , 12 . 5-fold more asymptomatic than symptomatic infections were diagnosed by qPCR in control HHs ( Table 2 ) . We thus sought to estimate the relative contribution of index and neighbor HH members ( who would be screened during RCD ) and control HH members ( who would be missed by RCD ) to the total P . vivax biomass circulating in the study population . To this end , we used data from 276 qPCR-positive samples with available qPCR-based parasite-density measurements ( Fig 4A ) . Overall , geometric mean parasitemias were twice as low in control HHs ( 5 . 6 parasites/μL [95% CI: 4 . 3–7 . 5] ) as in index HHs ( 14 . 9 parasites/μL [95% CI: 8 . 9–24 . 7] ) and neighbor HHs ( 11 . 8 parasites/μL [95% CI: 8 . 9–16 . 4] ) . Only 7 . 7% of the parasite biomass circulating in study subjects were contributed by control HH members ( represented as black bar segments in Fig 4A ) , who had 27 . 4% of all qPCR-positive samples . The corresponding fractions harbored by index and neighbor HH members were 29 . 4% ( 20 . 7% of all qPCR-positive samples ) and 62 . 8% ( 51 . 9% of all qPCR-positive samples ) of the total parasite biomass , respectively . Individuals with patent infections harbored 80 . 7% of the P . vivax biomass ( geometric mean parasitemia , 34 . 6 parasites/μL [95% CI: 22 . 2–54 . 0] ) , while those that were missed by microscopy but detected by qPCR had much lower parasite densities ( geometric mean parasitemia , 5 . 5 parasites/μL [95% CI: 4 . 6–6 . 7] ) . If conventional microscopy were the only diagnostic method available to identify infections and guide malaria treatment , a large proportion of the parasite biomass carried by index HH members ( 92 . 4% ) and neighbor HH members ( 79 . 2% ) , but only 47 . 9% of that harbored by control HH members , would be detected and potentially removed by an effective antimalarial chemotherapy . Parasite densities had a unimodal right-skewed frequency distribution in asymptomatic carriers ( blue bar segments in Fig 4B ) ; 65 . 6% of them were below 10 parasites/μL , with a geometric mean of 8 . 6 parasites/μL ( 95% CI: 6 . 8–10 . 8 ) . Asymptomatic carriers contributed 75 . 4% of all qPCR-positive samples and 68 . 3% of the parasite biomass circulating in study subjects . Conversely , symptomatic subjects had a wide range of parasite densities ( black bar segments in Fig 4B ) , with a nearly flat frequency distribution ( geometric mean of 17 . 4 parasites/μL [95% CI: 10 . 6–28 . 4] ) ; they contributed 26 . 1% of all qPCR-positive results and 31 . 7% of the parasite biomass . We next estimated the parasite biomass fraction that was harbored by control HH members who were asymptomatic , which would be missed by RCD-based strategies . We found that these subjects contributed 24 . 1% of all qPCR-positive results , but only 4 . 1% of the total P . vivax biomass measured in study subjects . We used mixed-effects logistic regression analysis to test whether the association between residence in the proximity to an index case and prevalence of P . vivax infection remained significant after controlling for potential individual- and HH-level confounders . The fully adjusted model revealed 56 . 7- and 18 . 5-times higher odds of slide-confirmed P . vivax infection among index and neighbor HH members , respectively , compared to control HH members ( Table 3 ) . Similarly , although with smaller magnitude than microscopy , the odds of qPCR-detected infection were significantly higher in index ( OR = 3 . 40 ) and neighbor HH members ( OR = 1 . 61 ) , compared to control HH members ( Table 3 ) . Each additional year of residence in the area was associated with a 4% reduction ( 95% CI , 1–8% ) in the risk of positivity by microscopy ( P = 0 . 030 ) in the fully adjusted logistic model , thus regardless of the HH type . Interestingly , a history of slide-confirmed P . vivax infection within the past six months , in the fully adjusted model , was the only other variable significantly associated with increased risk of current infection detected by either microscopy ( OR = 5 . 42; 95% CI: 3 . 36–8 . 75 , P < 0 . 001 ) or qPCR ( OR = 5 . 02; 95% CI: 2 . 77–9 . 09 , P < 0 . 001 ) . These findings suggest that selectively targeting individuals reporting recent P . vivax infections , regardless of their proximity to an index case , might be a practical way of finding current infections . We used molecular genotyping data ( S2 Database ) to explore the epidemiological connections among infections diagnosed during the study . We found a high genetic diversity in local P . vivax populations , with 146 unique ( predominant or only ) genotypes found in 343 completely typed samples ( 267 from index and neighbor HHs and 76 from control HHs ) . Most ( 272 or 79 . 3% ) subjects harbored mixtures of genotypes . Overall , six common genotypes accounted for 30 . 1% of all infections and were widely distributed across study localities ( Table 4 ) . The most common genotype ( #81 ) was recovered from 54 ( 15 . 7% ) study subjects living in all seven localities , being particularly prevalent ( 26 . 1% of all samples ) in PTL . Moreover , we found no clear temporal clustering of the six most common genotypes; genotype #81 was recovered from samples collected year-round ( S3 Fig ) . We next examined parasite genotypes within the 41 clusters surrounding index cases , each comprising one index HH and five neighbor HHs ( see Fig 1 ) . Between 1 and 21 ( mean , 6 . 5 ) P . vivax infections were successfully genotyped per cluster during the four RCD rounds ( n = 267 genotypes analyzed ) . Overall , 96 ( 35 . 9% ) samples had genotypes that were either identical or similar to those found in other parasites within the same cluster , while 171 ( 64 . 0% ) genotypes were unique ( i . e . , not shared by parasites within the same cluster ) . Therefore , nearly two thirds of all infections were genetically unrelated to each other within a putative malaria cluster . However , most ( 23 or 56 . 1% ) clusters had at least one identical or similar genotype shared by two or more parasite samples . Fig 5 shows several examples of shared and unique genotypes within cluster #3 , located in PTL . Note that two index HH members shared the same genotype ( #81 ) on day 0; one of them ( who was not treated on day 0 ) still harbored this genotype 30 days later , consistent with a persisting blood-stage infection or an early homologous relapse , but acquired a different genotype ( #88 ) detected on day 60 . Haplotype #81 reappeared in two unrelated neighbor HH members on days 60 and 180 , consistent with onward transmission originating from the index HH . Two instances of similar genotypes were also found in pairwise comparisons including mixed-clone infections; these cases are indicated with similar superscript letters in Fig 5 . Finally , unique haplotypes ( #3 , 10 , 77 , 82 , 88 , 120 , and 141 ) were found in seven samples , consistent with sporadic P . vivax infections that resulted in no further onward transmission within the cluster . Examples of identical parasite strains within cluster #4 ( also from PTL ) , suggesting a common source of infection , are shown in Fig 6: genotypes #64 ( shared by two members of the same neighbor HH on day 30 ) and #4 ( shared by the index case and another member of the index HH on day 180 ) . The source of infection could not be determined in these cases . Interestingly , genotype #81 ( which accounts for over one-fourth of all genotypes from PTL ) was recovered from the index case ( on day 60 ) and one control HH member ( on day 30 ) . The probability of finding genotype #81 in a pair of randomly chosen parasite samples from PTL , given by the squared frequency of this genotype in the local parasite population , is 6 . 8% . Similar genotypes were observed within the cluster as well as between members of index and control HHs ( Fig 6 ) . We next examined all instances of recurrent infections within clusters . A total of 130 genotypes were recovered from 54 individuals who had parasite recurrences ( range , 2 to 4 ) during the study . Genotypes recovered from most ( 41 or 75 . 9% ) recurrent infections were different from those found in the primary infection , even when considering minor alleles in pairwise comparisons involving multiple-clone infections . These results are consistent with unrelated , sporadic infections or heterologous relapses . However , 11 ( 20 . 4% ) subjects had one or more episodes of recurrent parasitemia with identical or similar genotypes and two ( 3 . 7% ) had consecutive infections involving both identical and different genotypes ( S1 Table ) . Overall , we found 17 instances of identical or similar genotype recurrence in these 13 subjects , 30–180 days after the primary infection , which may represent parasite recrudescences ( ≤ 30 days after the primary infection ) , homologous relapses after successful clearance of blood-stage parasites , or untreated , chronic blood-stage infections ( see Fig 2 for definitions ) . Five subjects with genotype recurrences had subpatent primary infections that were left untreated , consistent with long-lasting infections being detected over the following RCD rounds ( S1 Table ) . Fig 7 shows an example of recurrent infection with the same genotype within cluster #9 , from PTD . Genotype #37 was first recovered from the index case and one neighbor HH member on day 0 ( consistent with a common source of infection; Fig 2 ) . This same neighbor HH member , whose primary infection treated , had recurrent subpatent parasitemia with genotype #37 on days 60 and 180 , consistent with homologous parasite relapses . Only six control HH members presented qPCR-detected parasite recurrences during the study . In all cases , different genotypes were found in the primary and subsequent infection . Although all infections were subpatent and left untreated , we found no example of chronic infection with the same strain reappearing in consecutive visits to control HH members .
Malaria burden in Brazil has reached its lowest levels in more than three decades . This success led the Ministry of Health to launch , in November 2015 , a Plan for Elimination of Malaria in Brazil , in alignment with the new Sustainable Development Agenda of the United Nations aimed to reduce the global number of malaria cases by 90% until 2030 , and to eventually eliminate malaria in 35 countries [7] . To this end , operationally feasible strategies that can address the vast parasite reservoir found in residual malaria pockets are urgently needed . In this study , we evaluated a RCD strategy tailored for rural Amazonian communities with low P . vivax endemicity and spatial clustering of malaria episodes . Our results provided further evidence that P . vivax infections are strongly clustered at the HH level in rural Amazonia [12 , 13] . In fact , over 53% of all index HHs are located in a single locality ( PTL ) in the study site ( Fig 3 ) . Such a clustering indicates that RCD programs targeting index HHs may be effective in detecting a large proportion of parasite carriers , many of them asymptomatic , in high-risk HHs . Also , index HH members persisted at increased risk of infection over up to 180 days after the index case diagnosis , suggesting that several rounds of RCD may be required to detect a large proportion of new infections within these clusters . These data suggest that malaria transmission hotspots in the area are stable over time , indicating that their identification may be used to target high-priority HHs for control and elimination . The finding that a history of slide-confirmed malaria within the past six months is a strong predictor of P . vivax positivity in all RCD rounds suggests a simple way of identifying high-risk subjects that can be further explored in the design of narrowly focused and intensive active surveillance strategies . In addition , although qPCR detected nearly twice more infections than conventional microscopy in index HHs , subpatent infections contributed only 7 . 6% of the total P . vivax biomass circulating in index HH members . Combined , these findings suggest that a highly sensitive molecular method is not an essential component of RCD programs targeting index HH members . Interestingly , the number of slide-confirmed P . vivax infections diagnosed in Acrelândia declined substantially after our study was completed; 117 and 54 P . vivax infections ( 99 and 40 of them classified as autochthonous ) were recorded in this municipality in 2014 and 2015 , respectively ( http://portalsaude . saude . gov . br/index . php/o-ministerio/principal/leia-mais-o-ministerio/662-secretaria-svs/vigilancia-de-a-a-z/malaria/11346-situacao-epidemiologica-dados ) . Large-scale RCD programs have been either limited to index HHs or extended to other HHs in the vicinity of the index HHs [28 , 29] . It has been hypothesized that limiting RCD to index HHs is cost-effective in areas where routine malaria surveillance already detects a large proportion of positive HHs; however , actively screening the surrounding HHs for new infections may be required in areas with a low coverage of passive surveillance [30] . Comparisons across studies are limited by the use of different criteria to define and recruit “neighbors” ( e . g . , predefined radius around index cases vs . predefined number of nearest HHs; all vs . febrile neighbors only ) and by the varying biological features ( e . g . , flight range ) of local vectors [27 , 28] . Here we show that malaria positivity rates among neighbor HH members on day 0 , when the index case diagnosis triggered the initial investigation , were not significantly different from those found in randomly selected inhabitants in the same communities . Therefore , targeting neighbor HH members at the time of index case diagnosis did not appear to be more cost-effective than screening randomly chosen individuals for identifying new infections in our rural Amazonian setting . Nevertheless , much higher microscopy positivity rates were found in neighbor HHs , compared to control HHs , over the next months , with a similar trend observed for qPCR-based diagnosis in the last two RCD rounds . Furthermore , nearly two-thirds of the P . vivax biomass harbored by study participants was found in neighbor HH members , highlighting their potential contribution to ongoing malaria transmission in the community . One possible explanation for the increased prevalence of infections in neighbor HH members diagnosed on days 60 and 180 is that at least some of them may be secondary to those diagnosed in index HH members on days 0 and 30 . We thus conclude that neighbor HHs must be screened in RCD programs in endemic settings similar to our study site . Defining the appropriate timing for microscopy- or qPCR-based screening is crucial for finding these additional cases in a cost-effective way . A major limitation of RCD-based strategies is that asymptomatic parasite carriers will be missed if no single symptomatic infection is found in their vicinity . A recent survey in coastal Kenya , for example , has revealed different features of clusters of asymptomatic vs . symptomatic infections , such as the greater temporal stability of the former over > 10 years; these clusters with purely asymptomatic or some symptomatic infections did not necessarily overlap spatially in all localities [31] . qPCR-detected asymptomatic infections in our control HHs , which would be entirely missed by RCD , contributed a small fraction ( 4 . 1% ) of the total parasite biomass found in study participants . Their infectiousness to local vectors remains unknown; one study described an infection rate of 1 . 2% after feeding An . darlingi with blood from asymptomatic P . vivax carriers vs . 22% for the mosquitoes fed with blood from symptomatic carriers [32] . Under the assumption that numbers of circulating P . vivax asexual blood-stages and mature gametocytes are positively correlated to each other [33] , asymptomatic carriers of low-level parasitemias are expected to have relatively few circulating mature gametocytes and thus contribute relatively little to ongoing malaria transmission at a particular time point . However , they may represent a large number of infective man-days if they remain parasitemic for weeks or months and gametocytemias reach a critical ( yet undetermined ) density threshold for successful mosquito infection [34 , 35] . Reassuringly , in our study population we found very few asymptomatic infections persisting over consecutive visits to control HHs , none of them with the same genotype , suggesting that most episodes of asymptomatic carriage were short-lived . Whether periodic mass blood screenings with highly sensitive diagnostic methods would be a cost-effective way of addressing this occult parasite reservoir in malaria elimination programs remains to be determined . Alternatively , molecular diagnostic methods in elimination programs may be carefully targeted at subjects who are found to be at high risk of persistent asymptomatic and subpatent parasitemia ( e . g . , young adults , forest workers etc . ) in sequential surveys of a given population . Malaria clusters in our study localities were not synonymous with isolated , common-source outbreaks involving one or a few closely related parasite genotypes , as in recently described P . falciparum outbreaks in Peru [36] and Panama [37] . To the contrary , we characterized a highly diverse P . vivax population in our study site as well as in endemic settings nearby [38–40] , with a wide variety of strains co-circulating not only in the proximity of index cases but also in the surrounding HHs outside the clusters . Accordingly , nearly two thirds of the genotypes circulating within each cluster were unique , consistent with a continuous introduction of new parasite strains and little onward transmission of these strains to neighboring individuals over the following months . These results suggest that a substantial proportion of infections are acquired outside the operationally defined malaria clusters; alternatively , human movement may continuously spread parasite genotypes ( wherever infections were acquired ) beyond the limits of the clusters . In other words , clusters would not represent isolated islands but archipelagos with considerable parasite migration between islands . Moreover , different strains were characterized in three-fourths of parasite recurrences observed in 54 study subjects , suggesting that re-infections or heterologous relapses , rather than recrudescences , homologous relapses , or chronic untreated blood-stage infections , are responsible for most repeated episodes of parasite carriage diagnosed within malaria clusters during the four RCD rounds . Results shown here may help decision-makers to design malaria elimination strategies in low-endemicity settings where P . vivax largely predominates , such as most Latin American countries [41] . We conclude that RCD-based strategies targeting index HH members and their neighbors may be suggested as a major component of malaria elimination efforts in Brazil . Whether the subpatent and asymptomatic carriers who remain outside the identified clusters of symptomatic infections , beyond the reach of RCD programs , may represent a significant parasite reservoir remains to be determined . These silent infections could only be identified by mass blood surveys using highly sensitive diagnostic techniques , whose feasibility and cost-effectivity are currently unknown . Finally , genotyping data have revealed rather complex networks of residual malaria transmission in rural Amazonian communities , consistent with multiple sources of infection , further complicating current elimination efforts .
|
Addressing the vast reservoir of asymptomatic Plasmodium vivax carriers clustered in hard-to-reach rural communities is a major challenge faced by countries approaching malaria elimination across Latin America and Asia . Routine surveillance targets subjects presenting with fever or reporting recent fever , but overlooks asymptomatic infections that might be otherwise detected by periodic mass blood surveys of the entire population at risk . Here we show that subjects living in close proximity to malaria cases detected by routine passive surveillance are much more likely to carry both symptomatic and asymptomatic infections than randomly selected inhabitants in the same farming settlements in the Amazon Basin of Brazil . Four rounds of microscopy-based screening for malaria parasites targeted at these high-risk subjects would identify 49 . 5% of the parasite carriers ( who together harbored 76 . 8% of the total P . vivax biomass circulating in the proximity of index cases ) detected by a more sensitive molecular method . Whether subpatent and asymptomatic carriers outside the identified clusters of symptomatic infections , beyond the reach of our screening , represent a significant parasite reservoir remains undetermined . The extensive genetic diversity found in local P . vivax populations suggests that multiple sources of infection fuel ongoing residual transmission within malaria clusters , further complicating current elimination efforts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"body",
"fluids",
"plasmodium",
"tropical",
"diseases",
"geographical",
"locations",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"apicomplexa",
"plasmodium",
"vivax",
"protozoans",
"infectious",
"disease",
"control",
"infectious",
"diseases",
"malarial",
"parasites",
"south",
"america",
"brazil",
"hematology",
"people",
"and",
"places",
"blood",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"malaria",
"organisms"
] |
2016
|
Reactive Case Detection for Plasmodium vivax Malaria Elimination in Rural Amazonia
|
Most empirical studies support a decline in speciation rates through time , although evidence for constant speciation rates also exists . Declining rates have been explained by invoking pre-existing niches , whereas constant rates have been attributed to non-adaptive processes such as sexual selection and mutation . Trends in speciation rate and the processes underlying it remain unclear , representing a critical information gap in understanding patterns of global diversity . Here we show that the temporal trend in the speciation rate can also be explained by frequency-dependent selection . We construct a frequency-dependent and DNA sequence-based model of speciation . We compare our model to empirical diversity patterns observed for cichlid fish and Darwin's finches , two classic systems for which speciation rates and richness data exist . Negative frequency-dependent selection predicts well both the declining speciation rate found in cichlid fish and explains their species richness . For groups like the Darwin's finches , in which speciation rates are constant and diversity is lower , speciation rate is better explained by a model without frequency-dependent selection . Our analysis shows that differences in diversity may be driven by incipient species abundance with frequency-dependent selection . Our results demonstrate that genetic-distance-based speciation and frequency-dependent selection are sufficient to explain the high diversity observed in natural systems and , importantly , predict decay through time in speciation rate in the absence of pre-existing niches .
Speciation is one of the most complex phenomena in nature , yet the effects of its tempo and mode for biodiversity patterns are still controversial [1] , [2] . Pre-existing niches is considered the dominant mechanism explaining the initial explosion of diversity observed in radiations [3]–[7] . In contrast , non-adaptive radiations [8] , [9] driven by niche-independent mechanisms such as sexual selection , rapid range expansion across multiple barriers or the simultaneous formation of multiple geographical barriers , dispersal limitation or isolation by distance without physical barriers due to genetic incompatibilities do not predict such a temporal trend of declining speciation rates during a radiation [10]–[13] . Although ecological opportunity ( the availability of an unoccupied adaptive zone ) or rapid range expansion across multiple barriers can explain rates of diversification in some radiating lineages , this is not sufficient for a radiation to occur [14]–[17] . Instead of attributing the propensity to have a radiation with decaying through time speciation rates to external influences like niche availability or rapid range expansion an alternative hypothesis can be based in the genome properties evolved during the evolutionary history of organisms . We explore this hypothesis using two models , one with frequency-dependent selection and one without it . Both models involve DNA sequence-based evolution of populations via a process of sexual reproduction , assortative mating , mutation , and genetic-distance-based speciation . The models we have analyzed in the present study are similar in spirit to previous speciation models [12] , [13] , [18] , [19] but different in two key details: ( 1 ) no approximations of the tempo and mode of speciation incorporating sexual reproduction and frequency-dependent selection have previously been shown to explain observed patterns of decay through time of the speciation rate during a radiation without invoking pre-existing niches . Furthermore , we show that the decay through time of the speciation rate during a radiation without invoking pre-existing niches has dramatic consequences to species richness and diversity , and ( 2 ) we contrast the models with two small radiations for a broad range of parameter values: the Tilapia cichlid genus [11] and the Darwin's finches [20] , two groups where assortative mating has been previously documented [21]–[24] . We note that larger radiations cannot be handled computationally . This represents a current limitation to explore broad patterns of speciation and diversity that requires further research . We simulated the evolution of a population whose members , at the beginning , have identical genomes . The population evolves under the combined influences of sexual reproduction and mutation ( Text S1 ) . During reproduction , potential mates are identified from those whose genomes are sufficiently similar to that of the reproducing individual ( ) . This parameter implicitly captures the effects of the accumulation of genetic incompatibilities by prezygotic or postzygotic reproductive isolation [18] , [25]–[27] . A mate is chosen from this set at random . An offspring is then dispersed in the environment . This minimal form of mating called assortative mating [13] , [28] , [29] is sufficient for speciation at least when there is no genetic linkage [18] , [19] . Genomic similarity between two individuals is defined as the proportion of identical nucleotides along the genome . The genomic similarity among individuals can be represented by an evolutionary graph in which nodes are individuals and edges connect reproductively compatible individuals [30] ( Figs . 1 and 2 ) . We identify a species as a group of organisms reproductively separated from all the others by genetic restriction on mating , but connected among themselves by the same condition . Thus , two individuals connected at least by one pathway through the evolutionary graph are considered conspecific , even if the two individuals themselves are reproductively incompatible . We consider three main assumptions that allow us to approximate the tempo of speciation and also to identify the conditions for each of two alternative modes of speciation in the evolutionary graph: ( 1 ) Our density of individuals is one per site , and these numbers are kept constant by assuming zero-sum dynamics . Birth-death zero-sum stochastic models are equivalent to their non zero-sum counterparts at stationarity [31]; ( 2 ) Factors influencing speciation may differ between regions of the genome , and regions of the genome involved in reproductive isolation may differ between taxa and the temporal stages of the speciation process [32] . In our model , the genome of each individual is considered effectively infinite ( i . e . , a very large string of nucleotides , Text S1 ) , and ( 3 ) The mate choice function explaining the viability of the offspring is given by a step-shaped function . This is the simplest representation of Dobzhansky-Muller reproductive incompatibility [33]–[35] . Functions with equal viability in a range [ , ] ( see Material and Methods ) , declining linearly and exponentially [12] give qualitatively the same results as the results presented here using the step-shaped function . At the beginning of the simulation , all individuals are reproductively compatible , corresponding to a completely connected graph . Because of mutations that can eventually reduce genetic similarity below the threshold required for mating , the graph will lose connections as generations pass ( Fig . 1 ) . The rate at which connections are lost in the evolutionary graph , and thus the tempo of speciation , depends on the mechanisms driving genome diversification . To explore the tempo of speciation and its implications for biodiversity patterns , we generated a second model with negative frequency-dependent selection . In this model there are not external factors creating pre-existing niches , which can be populated only by individuals of a specific genotype and can be filled up to a carrying capacity . In contrast , any rare genotype has higher fitness than common types . The reason may be natural selection driven by the ecology in which the organism is embedded ( e . g . , bacteria or pathogens attacking reproductive proteins of common types without altering the probability to die among individuals ) [36] , [37] or some form of sexual selection that lead to rare-type advantage ( e . g . , sexual conflict , molecular incompatibility or heterozygote advantage in sexually selected genes ) [38]–[40] and have more success at mating , whereas common types are likely–but not guaranteed–to become rare . Despite potential costs of the rare types ( i . e . , Allee effects , mating costs , etc ) , experimental and theoretical studies have shown that the selective value of a given genotype is often a function of its frequency in the population [41]–[46] . In summary , frequency-dependent selection in this context is a type of sexual selection with niches not imposed from outside the system but created by rare types with greater mating success that can spread their alleles more quickly through the population . Apart from the asymmetry introduced by the different reproductive probabilities at the individual level , these two models are identical ( Text S1 ) .
With appropriate parameter values satisfying the mathematical condition required for speciation ( where is the genetic similarity matrix at equilibrium ) both models can produce speciation events ( i . e . , sexual isolation of subpopulations in the genome space , Equation A-30 and Box 1 in Text S1 ) . We identified two distinct modes of speciation that can , under the right conditions , occur in the evolving graph: mutation-induced speciation and fission ( Fig . 1 ) . Mutation-induced speciation happens when a newly produced offspring is disconnected from its parents . This form of speciation requires the mutation rate to exceed some minimum value ( ) necessary to satisfy the inequality , where is the offspring and are the parents of ( Fig . 2 ) . Because the minimum number of steps equals 1 , the minimum mutation rate to have mutation-induced speciation is given by: ( 1 ) For example , if offspring become inviable once genetic divergence exceeds ( i . e . , ) , then the minimum mutation rate needed to achieve mutation-induced speciation is . There is a second mode of speciation which is also a consequence of mutations in the evolutionary graph . We call this mode “fission” because it takes place when the death of an individual breaks a link in what was the sole genetic pathway connecting some members of a species; this gives rise to one or more new species . Because of the strict condition for mutation-induced speciation to happen , fission is the only mode of speciation in the biologically relevant portion of model parameter space ( Section A3 in Text S1 ) . The speciation rate ( ) in the genetic similarity matrix ( ) has two different dynamics according to the initial minimum genetic similarity value to have fertile offspring ( ) : ( 2 ) where is the expected mean genetic similarity in the matrix at equilibrium [18] , with the population size . If , then is the rate of dropping links in the evolutionary graph that is proportional to the speciation rate for the model without frequency-dependent selection ( Fig . 2 and Text S1 ) . Fitting to the speciation rates obtained via simulation yielded least-squares regression coefficient estimates of and the slope ( ) : ( 3 ) This approximation suggests that the long term rate of speciation is independent of population size ( Section A3 and Figs . 1 and 3 in Text S1 ) . The models generate changes over time in the tempo of speciation , the distribution of incipient species abundance , and both the number and diversity of contemporary species . In Figs . 3 and 4 , we summarize the following two key predictions for the species number through time and species richness consistent with Darwin's finches and cichlid fish . First , we predict that whether the rate of speciation will remain constant or decline over time depends on the addition of frequency-dependent selection . Fig . 3a shows how the number of extinct and extant species varies over time . After a transient period , during which mutation introduces genetic variability into the initially identical population , the number of species increases rapidly . The two models then diverge dramatically . In the model without frequency-dependent selection , speciation rate remains constant . This pattern is consistent with the literature on whole-tree cladistic analysis [47] , the record of marine invertebrate fossils from the Phanerozoic eon [48] , and ( over shorter time frames ) observed genetic differences among North American songbirds [49] . The number of contemporary species ( Fig . 3b ) , diversity ( Inset Fig . 3b ) , and the abundance of the new species ( Fig . 3c ) are lower than in the frequency-dependent model . In the frequency-dependent case , rapid speciation is followed by a plateau with few speciation events , consistent with molecular data for several groups showing declining speciation rates through time [16] , [50]–[53] . This model predicts a greater number of contemporary species , higher diversity , and a more symmetric abundance distribution of incipient species; these are all attributes of rapid radiations . Second , frequency-dependent selection reproduces cichlid radiations in absence of pre-existing niches and the absence of frequency-dependent selection generates the Darwin's finches radiation . Fig . 4a and 4b show the best fit to the data for the number of species and speciation events through time . We predict decline over time and constant speciation rate in the cichlids and Darwin's finches with and without frequency-dependent selection , respectively ( data not shown ) . The expected distributions of species abundance derived from those predictions depart dramatically . For the genus , the model predicts high diversity , with most species having similar abundances ( inset Fig . 4a ) ; for the Darwin's finches , the model predicts much lower species diversity , with most species being rare ( insets Fig . 4b ) .
Most speciation studies have concluded that sympatric speciation only occurs if a stringent set of conditions is met [4] , [7] . Likewise , for the models we have explored , sympatric speciation can be highly unlikely or even impossible in biologically relevant areas of parameter space ( i . e . , , where , Text S1 ) . Note , however , that even though geographical barriers and dispersal limitation , and/or range expansion have played an important role in radiations , those factors do not generate decay through time in speciation rate in the absence of niche filling [10]–[13] ( see also Fig . 5 in Text S1 ) . Interestingly , the absence of frequency-dependent selection does not capture the exponential growth in number of species in the last stage of the Darwin's finches radiation . Time lag for extinctions [50] , taxonomic splitting but also the increase in heterogeneity with time in the Galápagos archipelago ( i . e . , more islands , habitat diversity and food types ) [20] are some of the factors that may hamper model predictions in this case . Nevertheless , the balance of results for both the cichlids and the Darwin's finches suggest that neutral and frequency-dependent selection mechanisms have played a role in radiating lineages . Current biodiversity theory , from population genetics [13] to island biogeography and its extensions [54] , explain species abundance patterns for many groups , but cannot predict different trends in the tempo of speciation nor their implications for radiations and diversity patterns . The models we have explored generate alternative tempo of speciation and these models can be compared with the patterns of diversity underlying classic radiations . In the context of these models , we have also determined the conditions necessary for the mutation-induced mode of speciation; if these are not met , then fission must be the only speciation mode . Finally , we have shown that frequency-dependent selection generates more symmetric and larger incipient species abundances , resulting in lower extinction rates . These results reinforce the notion that the incipient species abundance can have a dramatic impact on contemporary diversity patterns [54] , and suggest that both the tempo and mode of speciation themselves have a large effect on current community dynamics . Alternative models of speciation that incorporate additional molecular or ecological components exist ( i . e . , spatial heterogeneity and dispersal limitation [19] , [55] , recombination rate , insertions and deletions [56] and the explicit mechanisms that cause genetic incompatibilities [57] , [58] ) ; however , it is not yet possible to evaluate those models with speciation rates and diversity data . Fitting models with a large number of parameters remains a challenge for the future - we have shown that a quasi-likelihood method offer a powerful approach . In summary , the particular mechanisms underlying the dynamics of the evolutionary graph affect the tempo of speciation and diversity , but we nevertheless find theoretical distributions in agreement with the observed patterns of radiations and biodiversity for diverse taxa . Underlying the result are two simple models of a sexually reproducing population with and without frequency-dependent selection and with mating restrictions that depend on genetic distance . By examining these models under different parameter combinations and confronting them with data , we conclude that the properties of genomes during lineage diversification may influence patterns of radiations and biodiversity and the pre-existing environmental niches are not necessary for radiations to occur .
Our simulation is a stochastic , individual-based , zero-sum birth and death model of a sexual population with overlapping generations and age-independent birth and death rates . For the simulations reported in the paper , we considered haploid and hermaphroditic individuals where only one individual can exist in each site . Genomes consist of an infinite string of nucleotides and the genomic similarity between two individuals is defined as the proportion of identical nucleotides along the genome . Reproduction starts with a randomly selected individual looking for a mate among all the sufficiently similar individuals . To qualify , an individual must have a genetic similarity greater than the minimum value required for fertile offspring . From all such potential mates , we select the second parent at random . In the frequency-dependent selection model , individuals with few connections , and therefore with more rare alleles , have more success at mating and their alleles spread quickly through the population . Mating produces a haploid offspring that differs from both parents following free recombination and mutation ( Text S1 ) . Each nucleotide is inherited from one of the parents with the same probability . The results reported here are for asynchronous mating . Synchronous mating gave similar results , although speciation times were typically longer . According to tests of multiple model variants in the model without frequency-dependent selection , including parameter variation , self-incompatibility ( i . e . , by adding a to limit the reproduction of excessively similar individuals , Fig . 5a in Text S1 ) , and mating and dispersal limited to adjacent patches ( i . e . , 8-patch Moore neighborhood ) with and without a wrapped torus ( Fig . 5b in Text S1 ) , our results apply quite generally , with the key required properties to generate declining through time speciation rates being the limitations on genetic distance associated with mating and the frequency-dependent selection mechanism . Results for Fig . 3 are obtained by time-averaging over replicates lasting generations each . Given individuals in the initial population , a generation is an update of time steps . Parameter variation does not affect the overall behavior . Results for Fig . 4 are obtained after replicates for each parameter combination lasting generations each . We sampled the transients ( each generation ) and the steady state at the end of each replicate for the species through time and species abundance . We have explored parameter combinations in the range , , and community size , that satisfy the mathematical condition required for speciation ( , equation A-30 and Box 1 in Text S1 ) . Our results apply quite generally in a broad range of community size ( Fig . 3 in Text S1 ) and speciation rates ( Fig . 4 in Text S1 ) . The fit to the number of species and speciation events through time was done following these steps: 1 ) Normalize time for observed data and each simulation from the first speciation event to present time within the range [0 , 1] , 2 ) From each possible interval , starting with the size of the data until the size of the output in each simulation ( generations with increments of 1 generation at each time ) , we generated the sequence of speciation times that minimizes the difference with the observed speciation times , and 3 ) Identify the best fit as the one that minimizes the sum of the absolute values of the misfits: ( 4 ) where is defined as , i . e . , in terms of the misfit between observed and simulated species richness , , and the misfit in the timing of speciation events . and are our model parameters . Our search is performed for a broad range of plausible empirical values for and constant and satisfying ( Text S1 ) . If our errors per data point are a random variable following the exponential distribution , , and , assuming error independence , our measure of misfit is the model negative log-likelihood [59] . Confidence intervals have been calculated by taking the percentiles and from the distributions of values of different model replicates . Model replicates were generated with the best parameter estimates for and along with a family of pairs within 2 log-likelihood units away from the minimum [60] ( Fig . 4 in Text S1 ) .
|
Ecological opportunity , or filling a pre-existing unoccupied adaptive zone , is considered the dominant mechanism explaining the initial explosion of diversity . Although this type of niche filling can explain rates of diversification in some lineages , it is not sufficient for a radiation to occur . Instead of attributing the propensity to have an explosion of new species to external influences like niche availability , an alternative hypothesis can be based in frequency-dependent selection driven by the ecology in which organisms are embedded or endogenous sources mediated by gametes during fertilization . We show that genome diversification driven by higher reproductive probability of rare genotypes generates rapid initial speciation followed by a plateau with very low speciation rates , as shown by most empirical data . The absence of advantage of rare genotypes generates speciation events at constant rates . We predict decline over time and constant speciation rate in the cichlids and Darwin's finches , respectively , thus providing an alternative hypothesis for the origin of radiations and biodiversity in the absence of pre-existing niche filling . In addition to predicting observed temporal trends in diversification , our analysis also highlights new mechanistic models of evolutionary biodiversity dynamics that may become suitable to generate neutral models for testing observed patterns in speciation rates and species diversity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/evolutionary",
"modeling",
"ecology/community",
"ecology",
"and",
"biodiversity",
"evolutionary",
"biology",
"ecology/theoretical",
"ecology"
] |
2010
|
Frequency-Dependent Selection Predicts Patterns of Radiations and Biodiversity
|
Masculinization of the external genitalia in humans is dependent on formation of 5α-dihydrotestosterone ( DHT ) through both the canonical androgenic pathway and an alternative ( backdoor ) pathway . The fetal testes are essential for canonical androgen production , but little is known about the synthesis of backdoor androgens , despite their known critical role in masculinization . In this study , we have measured plasma and tissue levels of endogenous steroids in second trimester human fetuses using multidimensional and high-resolution mass spectrometry . Results show that androsterone is the principal backdoor androgen in the male fetal circulation and that DHT is undetectable ( <1 ng/mL ) , while in female fetuses , there are significantly lower levels of androsterone and testosterone . In the male , intermediates in the backdoor pathway are found primarily in the placenta and fetal liver , with significant androsterone levels also in the fetal adrenal . Backdoor intermediates , including androsterone , are only present at very low levels in the fetal testes . This is consistent with transcript levels of enzymes involved in the alternate pathway ( steroid 5α-reductase type 1 [SRD5A1] , aldo-keto reductase type 1C2 [AKR1C2] , aldo-keto reductase type 1C4 [AKR1C4] , cytochrome P450 17A1 [CYP17A1] ) , as measured by quantitative PCR ( qPCR ) . These data identify androsterone as the predominant backdoor androgen in the human fetus and show that circulating levels are sex dependent , but also that there is little de novo synthesis in the testis . Instead , the data indicate that placental progesterone acts as substrate for synthesis of backdoor androgens , which occurs across several tissues . Masculinization of the human fetus depends , therefore , on testosterone and androsterone synthesis by both the fetal testes and nongonadal tissues , leading to DHT formation at the genital tubercle . Our findings also provide a solid basis to explain why placental insufficiency is associated with disorders of sex development in humans .
The male external genitalia are the most common site of congenital abnormalities in the human , with up to 0 . 8% of male births affected [1 , 2] . The most frequent of these abnormalities is hypospadias , which is characterized by abnormal opening of the urethra on the ventral side of the penis . Normal masculinization of the fetus is dependent upon androgen secretion by the testis , and androgens act initially during a critical masculinization programming window to ensure normal male reproductive development [3–5] . In humans , male-specific morphological differentiation of the genital tubercle/penis begins around 10 weeks of gestation ( i . e . , 8 weeks postconception ) , with closure of the urethral groove [6 , 7] . The process is complete by about gestation weeks 15–16 . 5 [6 , 7] , although sexually dimorphic growth of the penis continues through the second and third trimesters [8] . The etiology of hypospadias is probably multifactorial , but it is likely that altered androgen exposure during the second trimester is a significant factor [9] . During masculinization , testosterone acts directly to stabilize the mesonephric ( Wolffian ) ducts and to induce testis descent . However , it is conversion of testosterone to the more potent 5α-dihydrotestosterone ( DHT ) at the target organ that leads to masculinization of the external genitalia [10] . In humans , testosterone is synthesized in the testicular Leydig cells through the canonical Δ5 pathway shown in Fig 1 [11 , 12] . More recently , however , it has been reported that an alternative pathway to DHT formation exists that does not require testosterone as an intermediate . This alternative , “backdoor” pathway ( Fig 1 ) was first described in the testes of pouch young marsupials [13] , and a similar pathway has since been reported in the prepubertal mouse testis [14] and the fetal human testis [15] . The importance of the backdoor pathway to normal human development was initially unclear , but studies by Flück and colleagues [15] have shown that disordered sex development ( DSD ) will arise if the pathway is disrupted . In one individual with mutations in aldo-keto reductase type 1C2 ( AKR1C2 ) and in another family with an added mutation in aldo-keto reductase type 1C4 ( AKR1C4 ) , there was failure of normal masculinization . Importantly , the consequences of these mutations in the backdoor pathway are similar to those seen in individuals with mutations in the canonical pathway [16] . These data demonstrate that both the canonical and backdoor pathways are essential for normal fetal masculinization . Currently , the accepted model for masculinization is that circulating DHT , formed via the backdoor pathway in the fetal testis [15 , 16] , is important for virilization alongside circulating testosterone . At present , however , our understanding of the regulation of human fetal masculinization is seriously hindered because we do not know which steroids are present in the male fetal circulation or fetal tissues , what the concentrations of these steroids are , whether there are sex differences , and which tissues are involved in steroid metabolism . This means that the circulating levels of DHT and potential substrates for DHT synthesis at the target organ , from either the canonical or backdoor pathways , remain unknown in the human fetus . In this study , we have measured ( i ) concurrent levels of fetal plasma and tissue steroids by hyphenated mass spectrometric tools , ( ii ) transcript levels of critical enzymes in the backdoor pathway in human fetal tissues , and ( iii ) canonical and backdoor androgen synthesis by the human fetal testis in vitro . Our results show that high levels of intermediates in the backdoor pathway are present in the human fetal circulation , that androsterone is the major circulating backdoor androgen , and that female fetuses have lower levels of circulating androsterone ( and testosterone ) . The results also show , however , that the fetal testis contains only very low levels of backdoor androgens and DHT and that androsterone is likely to be formed in nongonadal tissues , probably through metabolism of placental progesterone and adrenal dehydroepiandrosterone ( DHEA ) .
Overall levels of steroids involved in the synthesis of DHT in male fetal plasma ( obtained from cardiac puncture ex vivo ) are shown in Fig 2 . As expected , in males all steroids in the canonical Δ4 and Δ5 pathways were present in the fetal circulation . Noticeably , the data also show that intermediates in the backdoor pathway were present at significant levels , with the backdoor pathway apparently going from progesterone through 5α-dihydroprogesterone ( 5αDHP ) , allopregnanolone , 17α-hydroxyallopregnanolone , to androsterone ( Fig 2 ) . Circulating DHT , however , was not detectable in any of the 42 male fetuses ( <1 ng/mL ) . The Δ5 steroids pregnenolone , 17α-hydroxypregnenolone , and DHEA were present at the highest levels in the fetal male circulation , and these steroids probably come from the fetal adrenal gland [20 , 21] . Levels of progesterone were also high and were likely to be derived principally from the placenta [22] ( and see below ) . The key initial intermediates in the backdoor pathway , 5αDHP and allopregnanolone , were present in the fetal circulation at similar concentrations to progesterone ( means: progesterone , 258 ng/mL; 5αDHP , 135 ng/mL; allopregnanolone , 243 ng/mL ) . The principal Δ4 and backdoor androgens detectable in most samples were androstenedione , testosterone , and androsterone , all potential substrates for DHT synthesis . Most forms of 5α-androstanediol were undetectable in fetal plasma ( including 5α-androstane-3β , 17β-diol ) , although 5α-androstan-3α , 17β-diol ( labeled androstanediol in Figs 1 and 2 ) , which is a potential substrate for DHT synthesis , was detectable in 10/42 samples ( Fig 2 ) . Etiocholanolone , a metabolite of androstenedione , was also present in most samples ( S1 Fig ) . Levels of most steroids did not change over the course of the second trimester , with the exception of testosterone , which declined significantly ( P < 0 . 048 ) , and androstenediol , which increased ( P < 0 . 004 ) over the same period ( S2 Fig ) . Maternal smoking had no significant effect on fetal plasma steroid levels . A full list of steroids measured by gas chromatography–tandem mass spectrometry ( GC-MS/MS ) in human male fetal plasma is shown in S1 Table . To place male fetal plasma steroid levels into context , the circulating steroid levels in 16 second trimester female fetuses ( age matched with the 42 male fetuses ) were also measured ( Fig 2 ) . Levels of Δ5 steroids ( pregnenolone , 17α-hydroxypregnenolone , DHEA , and androstenediol ) did not differ between sexes . Progesterone levels were also similar , but the Δ4 steroids 17α-hydroxyprogesterone and androstenedione were significantly higher in females , while testosterone was significantly lower ( although there was some overlap between sexes ) . In the backdoor pathway , plasma allopregnanolone levels were higher in female fetuses , but 17α-hydroxyallopregnanolone and androsterone levels were significantly lower ( Fig 2 ) . Plasma levels of 5α-androstan-3α , 17β-diol ( androstanediol ) and DHT were undetectable in all female fetuses ( Fig 2 ) , while levels of the metabolite etiocholanolone were significantly lower than in males ( S1 Fig ) . There were no significant age-specific changes in any of the female plasma steroids measured over the course of the second trimester , although DHEA and androstenedione showed an interaction between age and maternal smoking ( S3 Fig ) . Levels of progesterone in female fetal plasma were significantly increased by maternal smoking ( S3 Fig ) . A full list of steroids measured by GC-MS/MS in human female fetal plasma is shown in S2 Table . Levels of major Δ4 , Δ5 , and backdoor androgens in placenta , fetal liver , fetal adrenal , and fetal testis , as measured by liquid chromatography–high-resolution mass spectrometry ( LC-HRMS ) , are shown in Fig 3 . Note that 17α-hydroxylated intermediates were not measured in this study , while matrix effects meant that the 5α-reduced androgen , androstanedione , was not detectable . The placenta contained high levels of progesterone , with lower amounts of 5αDHP and allopregnanolone . The backdoor androgens , androsterone , and androstanediol were detectable in about half the placentas , while the Δ4 steroids androstenedione and testosterone were detectable in most placentas . DHT was also detectable in about half the placental samples ( Fig 3 ) . The major steroids detectable in the fetal liver were progesterone , allopregnanolone , and DHEA . Low levels of androsterone and DHT were also detectable in most fetal livers , while androstanediol was detectable in about half the samples ( Fig 3 ) . The fetal adrenals contained high levels of pregnenolone , progesterone , and DHEA , and androsterone was present in most adrenals . The androsterone and DHEA in the adrenals were both sulfated , which is likely to be a reflection of the high levels of sulfotransferase type 2A1 ( SULT2A1 ) in this tissue [21] . Testosterone was present in about half the adrenals but other steroids were not detectable ( Fig 3 ) . The fetal testes contained high levels of pregnenolone and testosterone , with lower levels of progesterone and androstenedione . The backdoor intermediates 5αDHP and allopregnanolone were detectable in 6 and 9 testes , respectively ( out of 25 ) , but androsterone was not detectable , and androstanediol was only detectable in one testis . Low levels of DHT were detectable in 5 testes . To confirm the low/undetectable levels of 5α-reduced androgens in the fetal testis , testicular extracts from a further 6 fetuses were measured by GC-MS/MS to increase sensitivity ( see Materials and methods ) , and 4 ovarian samples of a similar age ( 15–19 weeks ) were included for comparison ( Fig 4 ) . In samples measured by GC-MS/MS , all of the backdoor steroids were detectable , but levels of 17α-hydroxyallopregnanolone and androsterone were very low . Low levels of androstanedione , androstanediol , and DHT were also detectable in all samples . Ovarian samples contained similar levels of progesterone and allopregnanolone compared with the testes , but all other steroids were reduced , and many were undetectable ( Fig 4 ) . To determine whether human fetal testes produce backdoor steroids under hormonal stimulation , dispersed fetal testicular cells were incubated with or without human chorionic gonadotropin ( hCG ) for 24 hours , and the steroids produced were measured by GC-MS/MS . In the canonical pathway , pregnenolone , DHEA , and androstenedione were detectable in most samples , as was pregnenolone at low levels ( S4 Fig ) . The presence of hCG had a stimulatory effect on DHEA levels . DHT was detected in one control culture . No backdoor androgens , or intermediates in their synthesis , were detectable in any testicular cell cultures . The critical entry point to the backdoor pathway is through 5α-reduction of progesterone or 17α-hydroxyprogesterone by steroid 5α-reductase type 1 ( SRD5A1 ) . The highest levels of SRD5A1 expression in the second trimester male fetus were in the liver , with significant but lower expression in the placenta , testis , and genital tubercle ( Fig 5 ) . Expression of steroid 5α-reductase type 2 ( SRD5A2 ) was only consistently detectable in the genital tubercle . The placenta and fetal liver are considerably larger than the other organs measured in this study ( Table 1 ) and , in terms of total fetal transcript levels , therefore , these tissues have about 1 , 000 times greater SRD5A1 expression than the testis . AKR1C2 is specific to the backdoor pathway and is critical for human fetal masculinization [15] . Mean AKR1C2 transcript levels were highest in the fetal liver and fetal testis ( Fig 5 ) , although taking tissue mass into account , liver and placenta each have about 200 times more total AKR1C2 transcript than the fetal testes . There was also significant AKR1C2 expression in the genital tubercle , which is likely to be important for local DHT synthesis from androsterone ( Fig 1 ) . AKR1C4 is the other backdoor enzyme that may be required for masculinization [15] , and transcripts were only consistently detected in the fetal liver ( Fig 5 ) . Expression of 17β-hydroxysteroid dehydrogenase type 6 ( HSD17B6 ) was highest in the testis , with transcripts also consistently detected in the adrenal and placenta , and lower expression in the genital tubercle ( Fig 5 ) . Transcripts encoding 17β-hydroxysteroid dehydrogenase type 3 ( HSD17B3 ) were expressed at similar levels in the fetal testis and liver , with very low or undetectable levels in the placenta , adrenal , and genital tubercle ( Fig 5 ) . Levels of aldo-keto reductase type 1C3 ( AKR1C3 ) transcript were highest in the liver , with low but detectable expression in other tissues . The cytochrome P450 enzyme 17α-hydroxylase/17 , 20 lyase ( CYP17A1 ) is essential in both canonical and backdoor pathways of androgen synthesis ( Fig 1 ) . Predictably , expression was high in both fetal adrenal and fetal testis , with the mean adrenal level about 6 times that of the testis ( Fig 5 ) . Expression was very low or undetectable in the genital tubercle , liver , and placenta . The enzyme 3β-hydroxysteroid dehydrogenase ( HSD3B ) is essential for de novo androgen synthesis ( in both canonical and backdoor pathways ) , with two isoforms ( HSD3B1 and HSD3B2 ) present on the human genome . Transcripts encoding HSD3B1 , which has a 5- to 10-fold higher substrate affinity than HSD3B2 [23] , were highly expressed in the placenta , with levels in other tissues either very low or undetectable . HSD3B2 was expressed predominantly in the testis and adrenal , although the placenta also contained HSD3B2 transcripts , and some low-level expression was seen in the fetal liver ( Fig 5 ) .
Masculinization of the fetus is dependent on the action of testosterone at the Wolffian ducts and on the action of DHT at the external genitalia [25] . The process of masculinization at the external genitalia starts in the late first/early second trimester , and the most intense phase of penile growth occurs later in the second trimester [8] . This is a critical period for normal masculinization , therefore , and it was assumed until recently that growth of the external genitalia was solely dependent on DHT formed in the target organ through 5α-reduction of testis-derived testosterone . However , the recent demonstration that the alternative , backdoor pathway to DHT synthesis is also required for normal human fetal male development [15 , 16] has shown that the process involves a complex interaction between different steroidogenic pathways . In this study , we now show that androsterone is the major circulating backdoor androgen in the human male fetus and that androsterone and testosterone are the only fetal androgens that are higher in the male circulation than the female circulation . In addition , most circulating androsterone in the fetal male appears to come from nongonadal tissues , probably using placental progesterone ( or its metabolites ) as substrate . Masculinization depends , therefore , not only on the fetal testes but also on other , nongonadal tissues . Data in Fig 2 indicate that in the fetal human , the backdoor pathway of androgen synthesis appears to depend initially on progesterone formed from pregnenolone , and in the fetus , the major de novo sources of pregnenolone are the adrenal and the testis . In both tissues , however , pregnenolone is metabolized predominantly through the Δ5 pathway to DHEA because pregnenolone is bound with a significantly higher affinity by human CYP17A1 ( Michaelis constant [Km] of 0 . 8 μM ) than by HSD3B2 ( Km of 5 . 5 μM ) [26 , 27] . In addition , HSD3B2 activity is likely to be relatively low compared with CYP17A1 in both tissues , based on transcript levels ( this study and [28–30] ) . It is likely , therefore , that most progesterone in the fetal circulation comes from the placenta , which secretes progesterone directly into the fetal circulation at high levels [31 , 32] , similar to those reported here . Several other human fetal tissues express CYP11A1 and may be capable of pregnenolone synthesis [30] but , based on transcript levels , they are likely to make a minor overall contribution to plasma levels . Metabolism of progesterone through the backdoor pathway requires the enzymes SRD5A1 , AKR1C2 , and CYP17A1 . Only the testes consistently express transcripts encoding all three enzymes , which would suggest that they are a likely source of backdoor androgens . Direct measurement of intratesticular steroid levels shows , however , that the testes contain only very low levels of androsterone and the potential substrate 17α-hydroxyallopregnanolone . Levels are lower than those of DHT and androstenedione , which are undetectable in fetal plasma ( Fig 2 ) , making it highly unlikely that testicular androsterone contributes significantly to fetal plasma levels . Enzyme kinetics suggest that in tissues such as the testes , which express all the necessary enzymes , the backdoor pathway can go from progesterone either through 17α-hydroxyprogesterone ( CYP17A1 Km for progesterone 0 . 7 μM [26] ) to 17α-hydroxyallopregnanolone or through 5αDHP ( SRD5A1 Km for progesterone 0 . 8 μM [33] ) and 17α-hydroxydihydroprogesterone ( CYP17A1 Km for 5αDHP 0 . 2μM ) to 17α-hydroxyallopregnanolone [19] . The failure of the testes to generate significant levels of 17α-hydroxyallopregnanolone may mean , therefore , that 17α-hydroxydihydroprogesterone is not a good substrate for human AKR1C2 . The alternative pathway through allopregnanolone is also limited by a relatively low affinity between allopregnanolone and CYP17A1 ( Km 18 μM ) , although 17α-hydroxyallopregnanolone is an excellent substrate for C17-20 lyase activity ( Km 0 . 6 μM ) [19] . In tissues that lack CYP17A1 but express SRD5A1 and AKR1C2 , progesterone will be metabolized to allopregnanolone and potentially become available for metabolism by other tissues that express CYP17A1 . Plasma steroid levels would support this pathway , although formation of 17α-hydroxyallopregnanolone is again limiting , probably reflecting the low affinity between allopregnanolone and CYP17A1 . This pathway depends initially on SRD5A1 , and transcript levels of this enzyme are highest in the liver ( Fig 5 ) , with lower levels also present in the placenta and testis , consistent with earlier studies of enzyme activity [34] . Tissue levels of 5αDHP are highest in the placenta , however , which probably reflects the high concentration of progesterone substrate in this tissue . The Km of SRD5A1 with progesterone as substrate ( 0 . 8 μM ) [33] is equivalent to about 250 ng/mL or 0 . 25 ng/mg tissue ( assuming a tissue density of 1 ) . The mean progesterone concentrations in the placenta and liver are 6 ng/mg and 0 . 4 ng/mg , which means that , in most samples , they exceed the enzyme Km . The highest consistent tissue concentrations of allopregnanolone are found in the placenta and fetal liver ( Fig 3 ) . Both AKR1C2 and AKR1C4 have a Km of 0 . 6 μM with 5αDHP as substrate [35] ( or 0 . 2 ng/mg tissue ) , which approaches 5αDHP concentrations in the liver ( mean 0 . 07 ng/mg ) and is exceeded by concentrations in the placenta ( mean 0 . 3 ng/mg ) . High concentrations of allopregnanolone in the fetal liver are likely , therefore , to be a reflection of high AKR1C2 and AKR1C4 . The placenta does not contain high levels of AKR1C2 transcript , and AKR1C4 is absent , but high substrate levels and lack of further metabolism via CYP17A1 probably explains the high tissue levels of allopregnanolone . The fetal adrenals do not express SRD5A1/2 and would not be expected to contribute to fetal 5αDHP or allopregnanolone production . Taking tissue mass into account , the liver and placenta are likely , therefore , to be the major sites of 5αDHP and allopregnanolone production in the second trimester fetus . Placental allopregnanolone production would also be consistent with the increasing plasma levels in pregnant women during gestation [36] . Circulating levels of backdoor steroids suggest that , in the fetus , the pathway goes largely through allopregnanolone and 17α-hydroxyallopregnanolone to androsterone . Allopregnanolone levels in human fetal plasma are relatively high ( male , about 200 ng/mL ) but androsterone levels are significantly lower in comparison ( male , about 3 ng/mL ) , which suggests that this is the limiting step in the pathway , probably reflecting the low affinity of human CYP17A1 for allopregnanolone . In female fetuses , allopregnanolone plasma levels are higher than in males , but downstream metabolites in the backdoor pathway are all reduced . This means that sex differences in the backdoor pathway are most likely to arise from reduced CYP17A1 activity in the female . CYP17A1 is expressed in many fetal tissues [30] but , with the exception of the adrenal and testis , transcript levels are low ( [30] and this study ) , suggesting enzyme activity is also probably low . In males , tissue androsterone levels are highest in the adrenals , which is consistent with high CYP17A1 transcript levels and with a previous study showing androsterone present in the fetal adrenal at the end of the first trimester [28] . The substrate for this reaction must , however , come from circulating allopregnanolone , because the adrenals lack SRD5A1 and appear unable to make 5α-reduced steroids . Our studies also show that the placenta contains detectable androsterone in most samples . There is some debate about whether the human placenta expresses significant CYP17A1 [37 , 38] , but our data show that , in the samples used for this study , transcripts are either absent or are present only at very low levels . This suggests , therefore , that most placental androsterone is likely to be derived from adrenal DHEA . Given the relative sizes of the fetal organs involved and the tissue concentrations , the placenta and the fetal adrenals are likely to be the major sources of androsterone production in the male fetus . Many human tissues show sexual dimorphism , including the placenta , fetal liver , and fetal adrenal [21 , 39 , 40] . However , the fetal liver and placenta are unlikely to be responsible for sex differences in circulating androsterone , as they express little or no CYP17A1 and so will not metabolize allopregnanolone ( the step at which sex differences are first seen in the backdoor pathway ) . In addition , we have measured androsterone levels in female fetal adrenals , and they do not differ significantly from the male ( male 0 . 330 ng/mg , female 0 . 342 ng/mg; 7/30 nondetectable in the male group , 8/30 nondetectable in the female group . S1 Data , Sheet 2 and Sheet 6 ) . Thus , while it remains to be established how sex differences in plasma androsterone levels arise , our data show that the testes are unlikely to be the primary cause and that differences in synthesis or metabolism must occur elsewhere . Sex differences in plasma testosterone were expected from our current understanding of sexual development and from earlier studies on human fetal plasma and amniotic fluid levels [41–45] . Nevertheless , despite the clear sex difference in mean plasma testosterone levels , there was considerable overlap between individual samples from either sex . Similar overlaps in plasma levels has been reported in one previous study [44] but not another [45] , while animal studies would suggest that overlapping testosterone levels in the fetus is common [46] . It is perhaps not surprising that testosterone is detectable in most female samples , as levels of both potential substrates ( androstenedione and androstenediol ) are present in the circulation , and the enzymes necessary to form testosterone ( HSD3B and HSD17B3 ) are expressed in nongonadal tissues . When the backdoor pathway was shown to be essential for human fetal masculinization , it was suggested that DHT is synthesized via this pathway in the fetal testes and released into the circulation [15 , 16] . We now show that this is very unlikely because DHT is undetectable , or present at very low levels , in testes from most fetuses and because circulating DHT levels are undetectable ( <1 ng/mL ) . These results are consistent with earlier studies that either failed to detect DHT in the human fetal testis [47 , 48] or found very low levels [49] . DHT can be metabolized to androstanediol by all AKR1 enzymes , and AKR1C2 in particular [12 , 50] , and given the high levels of AKR1 enzyme transcripts in the fetal liver , it is likely that any DHT released into the fetal circulation is rapidly metabolized . Our results show , therefore , that the major circulating backdoor androgen in the fetus is androsterone , which is present at similar levels to testosterone and is significantly higher in male plasma than female plasma . It remains to be shown whether the genital tubercle can convert androsterone to DHT , but the tissue expresses high levels of AKR1C2 , as well as detectable AKR1C3 , and AKR1C2 can catalyze both oxidation and reduction steps required for androsterone conversion to DHT ( Figs 1 and 5 ) . A schematic diagram of the proposed pathways involved in backdoor androgen synthesis in the human fetal male is shown in Fig 6 . In the DSD cases described by Flück and colleagues [15] , the 46 , XY karyotype patients carried hypomorphic mutations in either AKR1C2 and AKR1C4 , or AKR1C2 alone . From data reported here , reduced fetal AKR1C2 activity would be expected to affect backdoor pathways in the liver and testis , reducing production of allopregnanolone . In addition , loss of enzyme activity in the genital tubercle may affect production of DHT at the target organ . The potential involvement of placental AKR1C2 in the reported 46 , XY DSD patients is also of interest . The placenta develops from both maternal and fetal cells , and it is not known whether placental AKR1C2 is of fetal or maternal origin . It is of note , however , that two 46 , XY individuals who are known heterozygotes for a mutation in AKR1C2 show divergent phenotypes , with one showing a normal male phenotype and the other DSD [15] . The mother of the affected individual is also heterozygous for a mutation in AKR1C2 , and if placental AKR1C2 is of maternal origin , then this would be expected to affect backdoor steroid production in the placenta . Unfortunately , the genotype of the mother of the unaffected heterozygous individual is not available . Further strong evidence for a nontesticular pathway of backdoor androgen synthesis comes from patients with P450 oxidoreductase ( POR ) or 21-hydroxylase deficiency who show 46 , XX virilization . It is likely that virilization in utero in individuals with 21-hydroxylase deficiency is due to excessive backdoor androgen synthesis that is occurring despite the absence of testes [51] . In individuals with POR deficiency , the effects are more complex , as POR supports many P450 enzymes , and specific POR mutations can affect different enzymes [52–54] . Virilization of 46 , XX individuals can occur , for example , because of reduced placental CYP19A1 activity leading to accumulation of placental androgens [55] . It has also been shown , however , that some mutations of POR that have less effect on aromatase are associated with increased backdoor androgen production [56 , 57] . It has been postulated that the increase in fetal backdoor androgen production seen in POR or 21-hydrdoxylase deficiency comes from the fetal adrenals [58 , 59] . However , the lack of SRD5A1 and intermediates in the backdoor pathway in normal fetal adrenals make this unlikely , unless the condition itself increases adrenal SRD5A1 activity ( e . g . , through increased adrenal stimulation by adrenocorticotropic hormone ) . Our data would suggest that the increased fetal adrenal 17α-hydroxyprogesterone seen in these conditions acts initially as substrate for backdoor androgen production through 5α-reduction in other tissues—probably the fetal liver . If the placenta is a critical component of the fetal backdoor androgen pathway , as suggested by these data , then it has implications for our understanding of the regulation of masculinization and DSD . It is now established that placental insufficiency , associated with intrauterine growth restriction ( IUGR ) , is associated with abnormalities in development of the male external genitalia , and hypospadias in particular [60 , 61] . Severe forms of placenta-mediated IUGR start during the first trimester [60] and could interfere , therefore , with all aspects of fetal masculinization . It has been suggested that DSD associated with placental dysfunction could be due to reduced placental hCG production [61] , but other studies have shown that maternal hCG levels tend to be increased in placental insufficiency [60] . In contrast , maternal progesterone levels are reported to be reduced during IUGR [62] , suggesting that placental steroidogenesis is affected . If the placenta is central to fetal backdoor androgen production , as we suggest , then altered placental steroidogenesis may lead directly to abnormalities in masculinization . Studies in the 1950s and 1960s by Jost and others showed that androgen is required for masculinization of the external genitalia ( reviewed in [63] ) . Later studies found that testicular testosterone must be converted to DHT at the target organ to induce masculinization [64] . Most recently , it has been shown that the normal process of masculinization in the human depends on two separate pathways of androgen synthesis , the canonical and backdoor pathways [15] . We now report that the backdoor pathway in the human fetus is sexually dimorphic , even though the testes are unlikely to contribute significantly to the pathway . Instead , results suggest that the pathway is driven by placental progesterone production , which acts as substrate for androsterone synthesis , primarily in nongonadal tissues . Overall , these data show that current understanding of the endocrine control of masculinization in the human fetus should be revised . Our results indicate that the endocrine control of masculinization in the human fetus is mediated through circulating testosterone and androsterone and is dependent on a complex interaction and exchange between the testes and nongonadal tissues , particularly the placenta .
Collections of fetal material in Aberdeen and Stockholm and by the Human Developmental Biology Resource ( HBDR ) were respectively approved by the NHS Grampian Research Ethics Committees ( REC 04/S0802/21 and REC 15/NS/0123 ) , the Regional Ethics Committee of Stockholm ( EPN dnr 2014/1022-32 ) , and the relevant research ethics committees in accordance with the United Kingdom Human Tissue Authority ( HTA; www . hta . gov . uk ) Codes of Practice . Written , informed , maternal consent was received from participants prior to inclusion in the study . Three sources of human fetal tissues were used in this study: ( 1 ) in Aberdeen , human fetuses between 11 and 21 weeks of gestation and classified as normal at scan were collected from women over 16 years of age undergoing elective termination [21] . Fetal age was estimated initially by ultrasound scan , adjusted for days between scan and termination , and then cross-referenced with foot length . Information about maternal smoking during pregnancy was available for most fetuses . Fetuses were transported to the laboratory within 30 minutes of delivery , weighed , crown-rump length recorded , and sexed . Blood samples from a total of 42 male fetuses and 16 female fetuses were collected by cardiac puncture ex vivo , and plasma was stored at −80°C . Tissues were snap-frozen in liquid N2 and then stored at −80°C . ( 2 ) In Stockholm , human fetal testes were obtained for in vitro incubation studies after elective termination of pregnancy at 10–12 weeks of gestation . ( 3 ) Additional fetal livers and placentas were provided by the MRC/Wellcome-Trust–funded HBDR ( http://www . hdbr . org ) . Available fetal and maternal characteristics relevant to samples used in different parts of this study are shown in S3 Table . Total RNA was extracted from frozen fetal tissues either using TRIzol ( Life Technologies , Paisley , UK ) [65] or using Qiagen AllPrep kits ( Qiagen , Manchester , UK ) . Reverse transcription , primer design , and real-time PCR were carried out as previously described [66 , 67] , and the primers used are shown in S4 Table . RNA that is free of genomic DNA contamination is required to amplify SRD5A1 because of the presence of a processed pseudogene in the genome , and this was carried out using RNAeasy Plus Micro-columns ( Qiagen , Manchester , UK ) followed by DNase treatment ( DNA-free , Life Technologies , Paisley , UK ) . Transcripts encoding HSD3B1 and HSD3B2 have very similar sequences , but primers were designed that are specific to each transcript under the conditions used here ( S4 Table ) . To normalize data , Normfinder was used to identify the most stable housekeeping genes in each tissue using the housekeeping genes and primers described earlier [68] . The best combinations of housekeeping genes varied between tissues and , thus , to allow comparison of transcript expression between tissues , TATA box–binding protein ( TBP ) was used as housekeeping gene for all samples , because it was the most consistently stable transcript across all tissues [68 , 69] . Some steroid enzyme transcript data ( SRD5A1 , SRD5A2 , and CYP17A1 in liver [69] and HSD17B3 and CYP17A1 in testis [70] ) have been reported previously , relative to different housekeeping genes or external standards . These data are shown again here , relative to TBP , to allow comparisons between tissues . Additional fetal liver samples have also been included in the reported data . Fetal sex was confirmed by PCR of ZFX and SRY using genomic DNA and primers described in S4 Table [71] . Isolated fetal testes were treated with collagenase type I ( Sigma , St . Louis , MO ) ( 1 mg/mL for 35 minutes at 37°C ) and then disrupted mechanically . The testicular cells were collected by centrifugation at 300g for 7 minutes , washed in Hank’s balanced salt solution , and resuspended in DMEM-F12 supplemented with 1 mg/mL BSA , 100 IU/mL penicillin and 100 μg/mL streptomycin . For incubation , 100 μL of a suspension containing 1 . 5 × 105 cells/mL was plated onto 96-well plates ( Falcon , Franklin Lakes , NJ ) in the presence or absence of hCG ( 10 ng/mL ) to stimulate Leydig cell activity . Cells were incubated for 24 hours at 37°C under 5% CO2 , and steroid levels in the culture media were measured by GC-MS/MS as described below . Methods used to extract and profile steroid levels by GC-MS/MS in fetal plasma and culture media have been described elsewhere in detail [28 , 72 , 73] . Briefly , fetal plasma samples ( 50 μL ) or culture media samples ( 200 μL ) were spiked with internal standards ( stable isotope-labeled steroid analogues ) and an enzymatic deconjugation of phase II metabolites was performed ( Sulfatase , Sigma S9626 [100 U/mL] and β-Glucuronidase , Sigma G8132 [5 KU/mL] ) . Steroids were extracted twice with diethyl ether and a ChromP SPE cartridge was used for initial purification . Androgens and estrogens were separated by liquid/liquid partitioning with n-pentane and were further purified on a silica SPE cartridge . The androgen fraction was derivatized with MSTFA/TMIS/DTE , and 2 μL of each extract was injected onto a Scion 436 gas chromatograph coupled to a Scion TQ triple quadrupole mass spectrometer ( Bruker , Fremont , CA ) . Electron ionization ( 70 eV ) was used and two diagnostic signals ( SRM acquisition mode ) were monitored for identification and quantification of the targeted compounds . Levels of circulating testosterone in male fetuses , shown in detail here , have been reported previously as mean levels [74] . Extraction and quantification of tissue steroid levels by LC-HRMS have been described previously [21] . Tissue samples ( 25–65 mg ) from placenta , liver , and adrenals or whole testes ( 8–20 mg , one per fetus ) were initially processed to isolate RNA/DNA/protein using Qiagen Allprep kits , as above , and steroids were then extracted from the column-eluants following addition of internal standards [21] . Following extraction , steroids were separated by reverse-phase liquid chromatography on a PFP column with trimethyl silane ( TMS ) endcapping ( 50 mm , 2 . 1 mm , 2 . 6 μm , Accucore , Thermo Fisher Scientific , Waltham , MA ) and using an UltiMate 3000 RSLCnano autosampler/pump ( Thermo Fisher Scientific ) . Steroids were then ionized by electrospray in positive mode , and signals were recorded on a Q Exactive Orbitrap ( Thermo Fisher Scientific ) mass spectrometer . Sulfated steroids were detected in a second analysis after negative electrospray ionization . Levels of sulfated and nonsulfated DHEA have been combined in the reported results , as have sulfated and nonsulfated androsterone . The fetal testes used in this part of the study were extracted in two batches , and recovery of steroid sulfates in the first batch was poor , so testicular DHEA and androsterone levels have only been reported for the second batch of 10 samples . For the second batch of testicular tissue , an additional extraction step using chloroform/n-butanol was used to improve steroid sulfate extraction efficiency . As reported for other LC-MS methods [75] , the electrospray ionization efficiency can be low for 5α-reduced androgens , which results in a relatively high limit of quantification ( LOQ ) and limit of detection ( LOD ) value for these compounds . In a separate study , therefore , gonadal steroids from an additional 10 fetuses ( 6 male , 4 female ) were extracted , and separation and quantification of steroids in these samples was carried out by GC-MS/MS as described above for plasma samples . Data were checked for normality and heterogeneity of variance and normalized by log transformation as appropriate . Plasma steroid data were analyzed by two-factor ANOVA , with fetal age and maternal smoking as the factors . Correlations between steroid data and age were analyzed using Pearson correlation coefficient . The effect of hCG on steroid secretion by the fetal testes was analyzed by t test . Differences between male and female fetal plasma steroid levels were determined by t tests when 40% or more of the samples in each group had detectable levels of the steroid . The Cohen method of maximum likelihood estimation of the mean and variance was used to account for nondetectable samples in the analysis [76] . All raw data are shown in 5 separate sheets in S1 Data .
|
The human penis starts to develop before birth from a structure called the genital tubercle . This process is dependent on the secretion of testosterone from the fetal testes and subsequent conversion of testosterone into dihydrotestosterone ( DHT ) by enzymes in the genital tubercle . Recently , an alternative "backdoor" route to the formation of DHT , which does not require testosterone , has also been shown to be essential for normal development of the human penis . In this study we provide evidence indicating that androsterone is the major backdoor androgen involved in human masculinization and that it is produced in nongonadal tissues . Steroid hormone levels were measured in the plasma of second trimester human fetuses , and testosterone and androsterone were the only androgens with higher levels in males than in females . Analysis of tissue steroid levels showed that plasma androsterone did not primarily originate from the testes but , instead , was probably formed in other tissues via metabolism of placental progesterone . These data indicate , therefore , that masculinization of the human fetus depends on steroid hormone secretion from both the testes and the placenta , and would explain why placental dysfunction is associated with disorders of sex development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"body",
"fluids",
"chemical",
"compounds",
"organic",
"compounds",
"hormones",
"developmental",
"biology",
"testosterone",
"steroids",
"androgens",
"embryology",
"placenta",
"chemistry",
"blood",
"plasma",
"fetuses",
"biochemistry",
"testes",
"blood",
"organic",
"chemistry",
"anatomy",
"physiology",
"lipid",
"hormones",
"biology",
"and",
"life",
"sciences",
"progesterone",
"physical",
"sciences",
"genital",
"anatomy"
] |
2019
|
Alternative (backdoor) androgen production and masculinization in the human fetus
|
The lack of new anthelmintic agents is of growing concern because it affects human health and our food supply , as both livestock and plants are affected . Two principal factors contribute to this problem . First , nematode resistance to anthelmintic drugs is increasing worldwide and second , many effective nematicides pose environmental hazards . In this paper we address this problem by deploying a high throughput screening platform for anthelmintic drug discovery using the nematode Caenorhabditis elegans as a surrogate for infectious nematodes . This method offers the possibility of identifying new anthelmintics in a cost-effective and timely manner . Using our high throughput screening platform we have identified 14 new potential anthelmintics by screening more than 26 , 000 compounds from the Chembridge and Maybridge chemical libraries . Using phylogenetic profiling we identified a subset of the 14 compounds as potential anthelmintics based on the relative sensitivity of C . elegans when compared to yeast and mammalian cells in culture . We showed that a subset of these compounds might employ mechanisms distinct from currently used anthelmintics by testing diverse drug resistant strains of C . elegans . One of these newly identified compounds targets mitochondrial complex II , and we used structural analysis of the target to suggest how differential binding of this compound may account for its different effects in nematodes versus mammalian cells . The challenge of anthelmintic drug discovery is exacerbated by several factors; including , 1 ) the biochemical similarity between host and parasite genomes , 2 ) the geographic location of parasitic nematodes and 3 ) the rapid development of resistance . Accordingly , an approach that can screen large compound collections rapidly is required . C . elegans as a surrogate parasite offers the ability to screen compounds rapidly and , equally importantly , with specificity , thus reducing the potential toxicity of these compounds to the host and the environment . We believe this approach will help to replenish the pipeline of potential nematicides .
Over 60 species of nematodes parasitize humans . According to a 2005 report by the World Health Organization ( WHO ) , approximately two billion humans have helminth infections worldwide [1] , and the problem has worsened in the intervening decade [2] . Parasitic nematodes are the most common infectious agents and produce a global disease burden greater than other conditions such as malaria and tuberculosis [3] . Nematodes also imperil the world’s food supply , as they are infectious parasites of livestock and plants . Plant-parasitic nematodes are recognized as one of the greatest threat to crops throughout the world , destroying over 12% of global food crop production annually and costing an estimated 157 billion US dollars annually [4 , 5] . Unfortunately , the arsenal of effective compounds is limited . Ivermectin , a nematicide that activates glutamate-gated chloride channels resulting in nerve and muscle hyperpolarization and worm paralysis [6–8] is currently the drug of choice and is recommended by WHO to treat nematode infections . Nematode resistance to anthelmintic drugs in general , and ivermectin in particular , is growing worldwide which poses a problem for both human populations and livestock [9–15] . In some respects this resistance is expected because ivermectin has been used for large-scale public distribution on an annual basis since 1987 . Anthelmintic resistance can arise through genetic alteration of the drug target [16 , 17] or by different strategies that worms use to reduce access of the drug to the target ( reviewed in [18] ) . Recently , mutations in dyf-7 , a gene known to be involved in anchoring the dendritic tips of sensory neurons [19] has been shown to be associated with resistance to ivermectin/macrocyclic-lactone-related drugs worldwide [20] . These data support an earlier observation that amphid sensory neurons ( which communicate with the environment ) in ivermectin resistant Haemonchus contortus are defective , displaying reduced sensory cilia and general morphological degeneration [21] . It is speculated that the open channels of these sensory neurons offers a ready pathway for absorption of ivermectin , channels that are lost or collapsed in animals with dyf-7 mutations . These many potential modes of resistance , combined with widespread reports of worldwide anthelmintic resistance ( in particular to ivermectin ) , are cause for concern and have many organizations calling for the development of new drugs to treat nematode infections . Parasitic nematodes are often difficult to maintain in the laboratory , and the lack of molecular and cellular tools poses experimental challenges to studying the parasites directly in a controlled setting . Fortunately , many anthelmintics have the same biological effect on non-parasitic nematodes as they do on parasitic animals ( reviewed in [22 , 23] ) . This biological conservation opens opportunities to use non-parasitic nematodes such as C . elegans for discovering new compounds that act as anthelmintics . In their review on anthelmintic drugs Holden-Dye and Walker ( 2007 ) ask , “Is C . elegans a model parasite ? ” and while they conclude it is not appropriate for examining the parasite life cycle , it is a very good model for comparative physiology and pharmacology for the phylum Nematoda . This was demonstrated experimentally by the work of Dr . Anthony Stretton’s group in the 1980’s comparing the parasitic nematode Ascaris suum to C . elegans ( neural wiring , [24]; acetylcholine in excitatory and GABA inhibitory neurons , [25 , 26] ) . These studies plus many more in the intervening years lead us to contend that C . elegans can be used as an efficient surrogate for discovering small molecule perturbants of infectious nematodes . Comparative genomic studies suggest a great deal of sequence similarity across the phylum Nematoda [27] , but also some very notable differences , especially among the filarial group ( see Loa Loa sequence; [28] ) . Even considering these differences C . elegans shares almost 13 , 000 ( ~70% ) of its genes with other species of nematodes , including many shared genes with the filarial group [27] . Rand and Johnson [29] pointed out that the ease of handling and the tools available for this organism make C . elegans ideal for drug screening and the discovery and characterization of drug targets . It was they who coined the term ‘genetic pharmacology’ . Despite these attributes , there have been surprisingly few published studies that take advantage of this organism for primary drug screening ( reviewed in [23 , 30] ) with some notable exceptions ( see for example , [31–37] ) . There are probably a number of factors contributing to why C . elegans has not been more widely used for drug discovery , but a principal one is that , until recently , there were few reports utilizing high throughput phenotypic screening . Towards the goal of expanding the scope of C . elegans for drug screening we have developed a rapid , high throughput screening protocol that permits a wide sampling of the chemical landscape for potential nematicides in a very efficient manner . At a rate of 1 , 900 compounds per hour , the ease and pace of primary screening of nematode populations using this platform is unprecedented and has led to efficient identification of new lead chemicals to be developed as nematicides . Chemicals that will be effective on nematodes may be rare [38] , but by sampling a sufficiently large number of chemicals rapidly , we have a reasonable likelihood of identifying new anthelmintics . Here we report the results from screening of 25 , 986 compounds from the Maybridge and Chembridge chemical libraries . We have identified several hundred nematode-active compounds , and we focus on 14 compounds with biological effects on growth and fecundity . We also demonstrate that nematodes are more sensitive to several of these compounds compared to several other organisms . This suggests to us that it is possible to use this strategy to develop novel classes of compounds specific to anthelmintic , which should lower the overall environmental toxicity of these compounds . This should facilitate their use in multiple conditions , settings and locations thus making it possible to decrease the overall parasitic nematode burden in both humans and livestock .
C . elegans strains were maintained using standard techniques as previously described [39] . The following C . elegans strains were used: VC2010 , the Moerman lab subculture of the Bristol isolate of C . elegans ( N2; [40] ) for compound screening; DM7448 ( VC20019 carrying gkEx1 , an extrachromosomal array that confers body-wall muscle YFP ) , DR103 ( dpy-10 ( e128 ) unc-4 ( e120 ) ) , DR1705 ( lin-31 ( u301 ) dpy-10 ( e128 ) ) , and DR1489 ( dpy-17 ( e164 ) unc-36 ( e251 ) ) for resistance mutation mapping; and CB3474 ( ben-1 ( e1880 ) , benomyl resistant [41] ) , RB2119 ( acr-23 ( ok2804 ) , monepantel-resistant [31] ) , CB193 ( unc-29 , ( e193 ) levamisole resistant [42] ) , and DA1316 ( avr-14 ( ad1302 ) ; avr-15 ( vu227 ) ; glc-1 ( pk54 ) , ivermectin resistant [7 , 8] ) for testing known drug resistance pathways . We also used many strains from the C . elegans Million Mutation Project ( MMP [43] and http://genome . sfu . ca/mmp/search . html ) , or the C . elegans Gene Knockout Project [44] , in order to test pink-1 and mev-1 mutations for drug resistance and to explore modes of action . For pink-1 we used strains RB2547 , VC20205 , VC20423 , VC20470 , VC20521 , VC20546 , VC20588 , VC30182 , VC40096 , VC40194 , VC40287 , VC40373 , VC40385 , VC40392 , VC40489 , VC40527 , VC40694 , VC40738 , VC41008 and VC30104 . For mev-1 we used VC20501 , VC20602 , VC40781 , VC40799 , VC40934 , VC20417 , VC30090 , VC30107 , VC40073 , VC40304 , VC40350 , VC40391 , VC40533 , VC40576 , VC40631 , VC40764 , VC40770 , VC41025 , VC20401 , VC20587 , VC40186 , VC40193 , VC40364 , VC40423 , VC40752 , VC40765 , VC20295 , VC30120 , VC40386 , VC40570 and VC40903 . The following compound libraries were used: 3 , 584 compounds from the Canadian Chemical Biology Network library ( Prestwick , Sigma LOPAC , Microsource Spectrum and BioMol collections ) including FDA-approved drugs; 16 , 000 compounds from the Hitfinder library ( Maybridge ) ; and 10 , 000 compounds from the DIVERSet library ( ChemBridge ) . All compound libraries were stored in 96-well plates , with 80 drugs per plate ( columns 1 and 12 left empty ) . A BioGrid BG600 pinning robot ( Digilab / BioRobotics ) was used for compound transfer . The robot uses a 96-pin tool ( 0 . 7 mm dia . ) to transfer a nominal volume of 340 nL of compound . For retesting , compounds were hand pinned from the original library plates to assay plates using a single 0 . 7 mm dia . pin . The assay plates were 96-well clear flat-bottom microplates containing 2% χ1666 E . coli bacteria in 0 . 5X Liquid Nematode Growth Media ( LNGM ) plus cholesterol ( Bacteria were mass-grown in rich media , and harvested by centrifugation to produce a thick paste of bacterial cells that was kept frozen at -20°C . Cells were resuspended at 2% W/V in 0 . 5X LNGM to produce liquid worm food ) . Using a COPAS Biosort ( Union Biometrica ) , two L4 to young adult stage VC2010 worms were added to each well of the plate in a final volume of 45 μL . After five days of exposure to the compounds , the plates were scanned and the data analyzed . A five day time period was chosen to be able to observe effects over all stages of the life cycle and through at least one round of replication . The initial proof-of-principle primary screen was performed manually using a dissecting microscope . Phenotypes scored included decreased motility , reduced brood size , and/or death as described previously [45] . For high throughput screening of the Chembridge and Maybridge compound libraries we used the program WormScan [46] , which was adapted for screening 96-well flat-bottom LNGM plates . Using WormScan it was possible to screen four 96-well plates at a time . Plates were scanned twice , at a resolution of 1200 dots per inch , 16-bit grayscale , producing a jpeg image . The light intensity produced by the scanner was sufficient to cause negative phototaxis equivalent to a physical stimulus for mortality determination . The time required to scan four 96-well plates using an Epson Perfection V700 Photo Scanner was less than ten minutes . The time to screen a single plate was less than 1 . 2 minutes , with less than ten seconds between scans . WormScan was used to screen for the same phenotypes as in the manual screen ( reduced brood size , reduced behavior/motility response and increased mortality ) . Two sequential scans for each 96-well plate were aligned to a reference region of interest ( ROI ) [47] . Image analysis was based on a difference image score calculated for each ROI . The difference image score ( WormScan score ) was normalized using a “percent of control” method to derive a normalized WormScan score [48] . Fig 1 shows original scans , the difference image and normalized WormScan score for several controls and one of the positive test compounds . Wild-type growth in 1% DMSO ( Fig 1A ) results in a normalized WormScan score of 100 . Exposure to 50 nM ivermectin gives a score of 0 ( Fig 1C ) , and complete absence of worms gives a score of 0 ( Fig 1D ) . For the primary screen the WormScan score was calculated using custom macro scripts written for Fiji [49] . For secondary screening and any subsequent studies the WormScan score was calculated using a standalone Java program ( See supplemental ) . Probit regression of mortality was calculated using Mathematica 8 . 0 . Potential anthelmintic compounds identified from primary screening were further tested against mammalian cells and yeast to determine whether they had deleterious effects in those organisms . Human embryonic kidney ( HEK293 ) cells were grown in Dulbecco’s Modified Eagle’s Medium ( DMEM; Sigma ) containing 5% fetal bovine serum ( FBS; Sigma ) in 96-well plates tissue culture plates . Compounds were added 24h post-plating at 10 , 30 , 100 , or 300 μM final concentrations . Dimethyl sulfoxide ( DMSO ) was used as a negative control at 0 . 1% . Cells were treated for 24 or 48 hours , after which time viability was assessed using a 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) colorimetric assay [50] . Percent viability was determined by comparing treatment values to DMSO control values . The S . cerevisiae yeast strain BY4743 ( MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 met15Δ0/MET15 ura3Δ0/ura3Δ0 ) [51] was used for all yeast experiments and was grown using yeast extract peptone dextrose ( YPD ) media at pH 7 [52] . BY4743 was grown overnight to initial log phase and was then grown with the compound identified in the C . elegans assay . The growth rate was quantified by continuously monitoring the optical density of the liquid yeast growth at OD600 . The compound was screened in a dose-titration in YPD as described [53] . The IC50 was the concentration required to reduce the growth rate or Average G value by 50% compared to a vehicle control ( 2% DMSO ) . A laboratory population of the plant-parasitic northern root-knot nematode , Meloidogyne hapla , was initiated by inoculating a tomato seedling growing in a 500 ml pot of 50:50 pasteurized sandy soil:peat with ~100 infective second-stage juveniles ( J2 ) extracted from infested soil from a vineyard in the Okanagan Valley . The population was thereafter reared on tomato under greenhouse conditions . Prior to experimentation , galled roots were removed from heavily infested tomato plants , washed over a sieve with tap water , and then placed on Baermann funnels . J2 hatchings over the first three days were collected and suspended in tap water at a concentration of 100 J2/ml . Replicate 24-well cell culture plates were seeded with 0 . 2 ml of J2 suspension . Dilutions of CID 2747322 in distilled water were then added to the wells to result in four replicate wells of each of six concentrations in each plate . Concentrations tested spanned 0 to 320 μM and 0 to 160 μM in the two separate runs of the experiment . Nematodes were observed using an inverted microscope at 40X . On each observation date , a transect through each well was observed and J2 within the transect were scored as mobile or immobile . J2 that had taken on a straight posture were scored as immobile . Recently hatched J2 of M . hapla normally move continuously but slowly and always have a curved posture . The integrity of the esophageal-intestinal junction , which disintegrates in dead J2 [54] , was observed to eventually disintegrate in J2 immobilized by CID 2747322 . A standard ethyl methanesulfonate ( EMS ) mutagenesis protocol [55] was carried out using VC2010 in order to generate mutants resistant to CID 2747322 exposure . First larval stage ( L1 ) F2 progeny of mutagenized P0s were exposed to CID 2747322 at 200 μM in liquid worm food in 24-well tissue culture plates for five days at room temperature , and two independently isolated resistant lines ( VC3614 and VC3615 ) were identified . Resistant animals were identified as well moving viable animals . Each of these was outcrossed three or four times with DM7448 ( VC20019 strain with body-wall YFP marker ) , selecting after each outcross one animal homozygous for a mutation conferring resistance to the compound . The purpose of the crossing was to reduce unrelated EMS-generated mutations ( S1 Fig for details [56] ) . For each of the two independent mutations , the homozygous mutant line resulting from the final outcross ( VC3635 and VC3631 , respectively ) was kept for further analysis . Genomic DNAs from VC3635 and VC3631 were extracted and sequenced , and candidate mutations were identified [40] . Standard three-factor genetic mapping was done for each of the putative candidate regions . Two sets of crosses on chromosome II used DR103 ( dpy-10 ( e128 ) unc-4 ( e120 ) ) and DR1705 ( lin-31 ( n301 ) dpy-10 ( e128 ) ) , and another set of crosses on chromosome III used DR1489 ( dpy-17 ( e164 ) unc-36 ( e251 ) ) to identify the genes conferring resistance . Sequence reads from the MMP [43] were obtained from the Short Read Archive and realigned on the reference C . elegans genome using the BWA aligner . The mtDNA copy number was then estimated simply by scaling the number of reads aligning per base of mtDNA to the corresponding value for the autosomes and multiplying for a factor of two to account for diploidy . A 3D similarity score was generated with the chemistry informatics tool Screen3D [57] using the atom matching algorithm and allowing the query and template molecules to be flexible . The compounds were categorized into similar groups using the Library MCS hierarchical clustering program Chemaxon ( Chemaxon: Budapest H , Library MCS , version 6 . 0 . 1 , 2013 ) . Singletons ( molecules which did not belong to any cluster ) were removed and all the remaining scaffolds were saved . Protein homology models were generated for the four protein subunits of mitochondrial complex II for C . elegans , SDHA-1 , SDHB-1 , SDHC-1 ( MEV-1 ) and SDHD-1 using Modeller version 9 . 15 [58] . Primary sequence alignment of the template with Ascaris suum sequence was generated with ClustalW version 2 . 0 . 12 [59] . Screen3D was used to model the binding of CID 2747322 to the crystal structure of mitochondrial complex II . Structural models and docked ligands were visualized with pymol version1 . 7 . 4 [60] .
As a proof-of-principle experiment , we first carried out a manual phenotyping pilot study with a set of 3 , 584 compounds that includes many approved drugs and we identified over 95% of the known nematicides present in the collection , including ivermectin and other macrocyclic lactones , organophosphates , lisurides , emodepsides , benzimidazoles and their derivatives , and amine acetonitrile derivatives ( AADs ) . Most of these compounds are actually biocides , with the exception of AADs . This pilot study , done in a blinded manner ( with the compound identity unknown to the scorer ) , gave us the confidence that the screen is effective and efficient at identifying anthelmintic compounds ( S1 Table ) . We next screened 26 , 000 compounds from the ChemBridge DIVERset and Maybridge Hitfinder compound libraries . The initial 3 , 200 compounds ( 40 plates ) from these libraries were screened using the automated WormScan protocol and manually with 100% agreement for the top 37 hits . This gave us the confidence to do further screening using only WormScan . We normalized the raw WormScan scores to correct for plate-to-plate variability ( Fig 2a and 2b ) to arrive at a ranked list of nematode-active agents after 5 days of compound exposure . Whereas previous C . elegans high throughput drug screens were limited to measuring motility [61] , we are able to quantify a larger range of phenotypes ( for example , Fig 1b ) . We focused on the easily identified phenotypes of lethality , reduced fecundity , slow growth , and immobility . Besides their ease of scoring , these particular phenotypes report relevant characteristics we wish to observe in relation to any potential anthelmintic . Retesting active compounds from the primary screen two additional times resulted in the identification of 137 chemicals affecting nematode survival , fecundity or behavior ( Fig 2c ) . We collapsed these 137 compounds to 14 ( see Table 1 ) , using filters related to strength of phenotypic effect , public bioassay data and any known general toxicity and promiscuity of the compounds . To avoid “rediscovering” known drugs , we gave lower priority to compounds that had a described mechanistic activity or whose chemical structure was similar to established anthelmintics [62 , 63] . We reasoned that emphasizing novel chemical backbone structures might lead to identifying biochemical pathways in the nematode that have not yet been exploited [64] . Even so some known chemical backbones did come through our filters ( see below ) . In patent searches using the 14 selected compounds as queries , we found that Bayer AG recently patented one compound for use on intestinal parasites [65] . This patented compound ( 1–36 ) has structural similarity to our best hit , N-[2- ( 4-methoxyphenoxy ) ethyl]-2- ( trifluoromethyl ) benzamide ( CID 2747322 ) , with a 3D Tanimoto similarity score of 0 . 82 to our compound . This independent discovery can be viewed as validation of our screening approach for identifying new anthelmintic drugs . It does emphasize however , that our filters for unique chemical backbones are not full proof as they did not exclude some previously identified anthelmintics ( specifically note fluopyram in a later section ) . To do structural clustering of the fourteen compounds we followed the chemistry informatics protocol used by the Burns study [35] , which is a standard way of grouping compounds . Using the procedure two molecules are defined as being in a similar class if they have a Tanimoto score of greater than 0 . 55 ( pairwise Tanimoto/FP2 score <0 . 55 ) . A network of relatedness is established using a pairwise comparison of each hit to all hits ( both from this study and Burns et al [35] ) . S2 Table displays the 2-D structure for each of our 14 compounds plus the Tanimoto score for each compound . Five of our compounds , including three of our best hits , CID 2747322 , CID 2747279 , and CID 6741218 , fall into clusters defined in Burns et al [35] , four compounds , including CID 796072 , fall into orphan clusters identified by Burns et al [35] , and five appear to be new chemistry not previously identified to have any affect on nematodes . Note that similarity in structural class does not necessarily imply similarity in function or in mode of action . For example , CID:6741218 with a Tanimoto score of 0 . 93 is highly similar to the compound WACT162 identified in the Burns et al study [35] , but while CID:6741218 is highly toxic to nematodes , WACT162 only reduces growth . From the perspective of identifying potential anthelmintics , we are primarily interested in compounds whose activity is restricted to nematodes . Each of the 14 compounds identified was tested across different phylogenetic groups ( including yeast , mammalian cell lines and other nematode species ) . An order of magnitude difference in toxicity between mammalian cells and nematodes is ideal [66] . However , C . elegans have been shown to be more resistant towards established anthelmintics compounds when compared to parasitic nematodes [67] . To date we have identified five compounds that have a high “nematode index” , which we define as having a significantly more potent effect on nematodes than on other organisms . Two of these "nematode actives" are perhaps marginal as CID 796072 and CID 6741218 only have a four-fold greater sensitivity in C . elegans . The other three , CID 2740991 , CID 2747279 and CID 2747322 , are more convincing with each having a 10-fold or greater sensitivity ( see Table 1 ) . We wished to compare the relative effectiveness of our new compounds to known anthelmintics in this assay . For this purpose we chose ivermectin , benomyl and fluopyram . Floupyram is a Bayer produced fungicide that has recently been approved as an anthelmintic as well [35] . Not unexpectedly benomyl shows no special sensitivity in nematodes over mammalian tissue cells . In contrast ivermectin and fluopyram show a greatly increased effectiveness in nematodes over the mammalian tissue culture cells or yeast ( Table 1 ) . They are also effective on nematodes at a much lower concentration than any of our newly identified compounds . From our chemical relatedness analysis our compounds CID:2747279 and CID:2747322 are in the same chemical class as fluopyram and the Burns et al compound WACT 11 [35] . An advantage of using C . elegans to characterize new potential anthelmintics is that we can use the extensive genetic toolkit for this organism to explore the mode of action of prospective anthelmintics . For example , to better understand the potential development of resistance to our newly-identified compounds , we tested three of the new compounds we discovered , CID 796072 , CID 6741218 and CID 2747322 on strains that are resistant to the common anthelmintics benzimidazole , levamisole , ivermectin and AAD ( Table 2 ) . If these resistant mutant lines are sensitive to a compound at doses that kill wild type animals we can deduce that the compound acts through a different pathway and perhaps has a novel target and mechanism of action [68] . The known anthelmintic-resistant lines provide both positive and negative controls ( e . g . the ivermectin resistant worm should be resistant to ivermectin in our assay but not to benomyl , and vice versa ) . Encouragingly , examination of Table 2 reveals that CID 796072 , CID 6741218 and CID 2747322 are equally effective on the four resistant lines as on the wild type strain VC2010 and at similar concentrations . While we felt these results lent credence to the idea that we have indeed identified novel-acting anthelmintics further studies ( see below ) suggests that at least CID 2747322 acts through a similar biochemical pathway to fluopyram . WormScan is a powerful tool to identify compounds with potential effects on the nematode , but it is still necessary to do manual inspection of wells with few animals to determine details of growth , behavior and fecundity . Wild-type worms grown in 90 μM CID 2747322 have brood sizes reduced by 97% . These small broods of live animals are sickly and uncoordinated and arrest at the first larval stage ( L1 ) and do not develop further while maintained in drug ( See S2 Fig ) . All current classes of anthelmintic have greater specificity towards certain life-stages [69] . It is interesting that when removed from the drug L1s will recover and develop , and generally become fertile adults ( See S3 Fig ) . This suggests a reversible inhibition by CID 2747322 , which we do not observe with fluopyram . For fluopyram animals are killed . It is important to note that while such specificities of effect on C . elegans are desirable , the ultimate test requires direct testing on other nematodes , specifically parasitic nematodes . To this end , we have tested two species and both are sensitive to CID 2747322 . We first examined to see if other free-living nematodes are sensitive to the drug and found that Caenorhabditis briggsae displays a sensitivity range similar to C . elegans . The IC50 for C . briggsae is 16 μM , while for C . elegans the IC50 is 18 μM ( S4 Fig ) . More critical was our test of effects on infective J2 of the plant parasitic nematode , Meloidogynae hapla . Immobilization was observed in the 160 and 320 μM concentrations at 24 hours after exposure initiation , and increased through day 10 . For the 10 day exposure ( S5 Fig ) , the IC50 was calculated to be 129 μM or 44 μg/ml . Plant parasitic nematodes have found to be generally more resistant to anthelmintics compared to C . elegans [70] . While the M . hapla J2 appeared to be considerably more tolerant to the compound than C . elegans , it was comparable to the sensitivity of M . incognita J2 to avermectin [71] . Unlike C . elegans , which can recover after a several day exposure to the compound , M . hapla die after extended exposure to the compound . To aid in identifying affected pathways we selected for animals resistant to CID 2747322 after EMS mutagenesis . Resistant lines were outcrossed and subjected to genetic mapping and Sanger sequencing to identify the responsible genes and mutations . This forward genetics approach is a validated and time-honored means to identify genes involved in drug resistance [7 , 8 , 31 , 34] . Because of the unbiased nature of this approach , it can identify direct targets of the compound and/or the pathway affected by the compound as well as additional modes of resistance , such as drug uptake , export , or sequestration ( see for example dyf-7 , [20] ) . Screening the F2 progeny of 150 P0 animals subjected to mutagenesis resulted in two independently isolated CID 2747322 resistant lines , VC3631 and VC3635 . These two lines confer similar levels of recessive drug resistance . We used a variation on the outcrossing and Whole Genome Sequencing ( WGS ) strategy described by Zuryn and Jarriault ( [56]; see S1 Fig for details ) coupled to standard genetic three-factor mapping to identify the two genes responsible for the observed resistance . Analysis of the whole genome sequencing ( WGS ) data for VC3635 identified 23 unique Single Nucleotide Variants ( SNVs ) spread along the length of chromosome II , and presumably one of these SNVs is responsible for the observed drug resistance . Three-factor mapping using dpy-10 unc-4 yielded 15 Dpy recombinants that were all drug sensitive and eight Unc recombinants that were all drug resistant . Further three-factor mapping with lin-31 dpy-10 yielded 20 Lin recombinants , six of which were resistant to the drug . While there are nine SNVs within coding regions in the mapped interval , we focused our attention on three SNV-containing genes , dsh-1 , pho-1 and pink-1 . We sequenced the six drug-resistant Lin recombinants and found that all six carried a wild type allele at the dsh-1 locus and one of five carried a wild type allele at the pho-1 locus . Only the pink-1 locus carried the mutant allele in all the drug-resistant recombinant lines , strongly implicating pink-1 as the gene conferring resistance . We also undertook a reverse genetic approach , testing 20 strains from the Million Mutation Project ( MMP ) collection that harbor pink-1 mutations and found that six strains with pink-1 missense mutations are resistant to the drug ( Table 3 ) . We conclude that pink-1 ( EEED8 . 9 ) is the gene in strain VC3635 responsible for resistance to CID 2747322 . The pink-1 gene encodes a predicted serine/threonine kinase that is most similar to the Drosophila and human PINK1 ( PTEN-induced kinase-1 ) protein kinases . PINK-1 is an important protein involved in mitochondrial homeostasis . Because it is part of the pathway to remove damaged mitochondria by autophagy ( reviewed in [72] ) we considered if mitochondrial DNA ( mtDNA ) copy number may be altered in these mutants [73] . Reanalysis of existing WGS data for the MMP strains allowed us to examine the mtDNA copy number in six MMP pink-1 mutant strains , three that are sensitive to the drug , and three that are resistant . We compared the ratio of mtDNA copy number to that of the genome copy number and found no correlation between resistance and mtDNA copy number ( Table 4 ) . The largest ratio of mtDNA to genomic DNA is actually in a sensitive strain , but the ratios for all six are remarkably similar . Simple amplification of mitochondrial DNA copy number is therefore not the explanation for pink-1 resistance . Analysis of the WGS data for VC3631 identified nine unique SNVs clustered in the central region of chromosome III . Three-factor mapping using dpy-17 unc-36 yielded 15 Dpy recombinants , all resistant to 250 μM CID 2747322 , and seven Unc recombinants all sensitive to the drug . This suggested that the mutation conferring resistance lies to the right of unc-36 , a region containing mev-1 and Y39A1A . 9 . At this point we took a candidate gene approach , as mev-1 had recently been implicated in resistance to another potential anthelmintic [34] . We sequenced six drug-resistant Dpy recombinant lines and three non-resistant Unc recombinant lines for the mev-1 locus . All the drug-resistant recombinant lines have the mev-1 associated SNV and all the drug-sensitive Unc lines contain wild-type versions of mev-1 . We also examined five strains from the MMP collection harboring mev-1 missense mutations , and two of these , VC20602 and VC40781 , showed resistance at the same level as VC3631 ( Table 5 ) . We conclude that mev-1 ( T07C4 . 7 ) is the gene in VC3631 responsible for resistance to CID 2747322 . The mev-1 gene encodes the C . elegans ortholog of the succinate dehydrogenase cytochrome b560 subunit , an integral membrane protein that is a subunit of mitochondrial respiratory chain complex II ( ubiquinol-cytochrome c reductase ) . The newly identified mutations in these two genes are missense mutations; gk3613 in MEV-1 is a T66I change and gk3615 in PINK-1 is a G172E change . Null alleles for pink-1 do not confer resistance suggesting that altering the protein ( i . e . a hypermorphic or neomorphic allele ) is required to confer resistance . Null mutations in mev-1 lead to lethality so no null alleles were tested . To determine if the mutations in mev-1 or pink-1 acted via an indirect mode of resistance ( e . g . similar to dyf-7 ) , we exposed VC3631 and VC3635 animals to the three anthelmintics ivermectin , benzimidazole and levamisole ( Table 2 ) . In all three cases the strains displayed the same sensitivity to these drugs , as does the parental wild type strain , VC2010 , which leads us to conclude these mutations do not confer general drug resistance . CID 2747322 shares some structural similarity to the WACT-11 compound identified by [34] . That study also found that mutations in mev-1 confer resistance to the potential anthelmintic drug WACT-11 , and indeed one of their identified mutations is identical to gk3613 . Interestingly , they found that mutations in the other three members of the succinate dehydrogenase protein complex also confer resistance . This led us to examine MMP strains with mutations in the other three mitochondrial complex II subunits; sdha-1 , sdhb-1 and sdhd-1 , to see if any of these strains are also resistant to CID 2747322 . For thirteen strains with sdha-1 missense mutations , three are resistant . For eight strains with sdhb-1 missense mutations , two are resistant . For sdhd-1 , one of five MMP strains is resistant ( Table 5 ) . To better understand how these different alleles might alter drug effectiveness , we mapped all of these mutations plus gk3613 onto an X-ray resolved protein crystal structure of the mitochondrial complex II of Ascaris suum ( PDB: 3VRB ) [74] ( Fig 3a ) . The five MMP mutations that confer resistance to CID 2747322 are highlighted , as are the nucleotide positions that , when mutated , do not confer resistance ( Fig 3b ) . The gk3613 T66I mutation that confers resistance to CID 2747322 was found in the Burns study to confer resistance to the WACT-11 compound series [34] ( note position of other WACT-11 resistance sites ) . Consistent with these observations , we note that resistance mutations for complex II inhibitors found in the wheat pathogen Mycosphaerella graminicola [75] have the homologous succinate dehydrogenase position highlighted on the C . elegans structural model ( Fig 3c ) . It appears that the resistance mutation positions found for CID 2747322 resistance in VC3631 , WACT-11 compound family resistance mutations ( Fig 3d ) [34] and Mycosphaerella graminicola all cluster around a putative compound binding pocket . These mutations may change the binding pocket structure and prohibit the inhibitor from binding while still retaining the biological function of the quinone pocket [75 , 76] ( see below for modeling of CID2747322 in the quinone pocket ) . Our screening of the MMP strains reveals there are additional mutations outside the binding pocket that can also confer resistance . Such “allosteric” resistance mutations that lie outside of the binding pocket for Sclerotinia sclerotiorum are also described [77] . A SAR approach was undertaken for CID 2747322 by considering the 26 , 000 screened compounds to gain insight into the structural moieties of CID 2747322 important for biological activity and to identify potential avenues of new medicinal chemistry [78] . This SAR exploration can provide insight into what changes in the functional groups of CID 2747322 can be tolerated while still maintaining anthelmintic activity and can guide future medicinal chemistry studies to enhance the activity . The compounds from the SAR analysis are highlighted with respect to changes in their R-group ( s ) compared to CID 2747322 ( Fig 4a ) . Compounds with changes on the right R-group are grouped based on their 3D similarity score and depicted in blue . Only one compound from the set had a change restricted to the left R-group , whereas the majority of the CID 2747322 analogs display changes in both the left and right R-groups . Changes on the right and left R-groups of CID 2747322 , 2- ( trifluoromethyl ) benzene and 2- ( 4-methoxyphenoxy ) ethyl respectively , were used to group the most similar compounds of CID 2747322 into a hierarchical map ( Fig 4a ) . The patented Bayer AG molecule 1–36 was found to cluster closely to CID 2747322 with a 3D similarity score of 0 . 82 . Additionally , the anthelmintic WACT-11 compound [34] also clusters to CID 2747322 with a 3D similarity score of 0 . 66 . The flutolanil analog N-biphenyl-3-yl-2- ( trifluoromethyl ) benzamide , an established complex II inhibitor , also has a 3D similarity to CID 2747322 of 0 . 60 . The Bayer compound fluopyram [66] , recently approved as an anthelmintic has a 3D similarity score of 0 . 58 . These molecules have a common peptide–CO–NH–linker . The 50 compounds from our survey with the greatest 3D similarity scores to CID 2747322 were retested for biological activity against C . elegans . Often , structurally similar molecules are found to have similar activity [79] . As in the original screen only one of these 50 related compounds has strong anthelmintic activity in C . elegans . This compound is the primary hit CID 2747279 ( 3D Similarity Score of 0 . 74; Fig 4b ) . Our analysis using SAR and drug retesting indicates that only variations in the right R-group of CID 2747322 maintain anthelmintic activity and that no substituents at the left R-group are tolerated . Based on the predicted chemical properties of these substituents we can speculate as to why certain structures are active while others are not . For example , the electron-withdrawing property of CID 2747322 right R-group 2- ( trifluoromethyl ) benzene induces an uneven distribution of electric charge . In this scenario , the R-group takes on a partial negative charge and the benzene ring becomes an electron-deficient π molecular orbital . This is also found in the right R-group of CID 2747279 and not with the furan right R-group , which was actually scored to be more similar with Screen3D . Other R-group variations did not have any anthelmintic activity , including the substitution of a large hydrophobic quinoxaline group . This anthelmintic specificity of CID 2747279 and CID 2747322 suggests that the binding pocket of C . elegans mitochondrial complex II is responsible for the biological activity . We leveraged the fact that the antifungal compound flutolanil is an excellent inhibitor of A . suum mitochondrial complex II , which has previously been shown to bind within the quinone pocket of the crystal structure of A . suum complex II ( PDB: 4YTM ) [76] to further explore the compound-target interaction . CID 2747322 has structural similarity to the flutolanil analogue N-biphenyl-3-yl-2- ( trifluoromethyl ) benzamide ( Fig 4a ) . Furthermore , the A . suum mitochondrial complex II has conserved sequence homology with C . elegans ( Fig 5 ) , making it an excellent template for 3D homology modeling for C . elegans . To better understand how CID 2747322 interacts with the target site , we constructed a model of the C . elegans succinate dehydrogenase complex in which the flutolanil analogue is used to model the CID 2747322 3D position within the C . elegans protein model ( Fig 6A ) . The docked position of CID 2747322 was found to form essential non-covalent interactions annotated in the structure of flutolanil and A . suum complex II ( PDB: 4YTM ) [76] . The C . elegans homology structure shared the conserved TRP197 of SDHD-1 , which forms a hydrogen bond with the amide group of CID 2747322 . Additionally , the ARG89 of SDHB-1 can form a cation–π interaction with the right R group 2- ( trifluoromethyl ) benzene of CID 2747322 . The left R group of CID 2747322 ( 2- ( 4-methoxyphenoxy ) ethyl ) docks within the hydrophobic pocket between complex II subunits SDHB-1 and SDHC-1 . While flutolanil is a potent A . suum complex II inhibitor ( IC50 0 . 0245 μM ) , it has been shown to be a less effective inhibitor of the porcine complex II ( IC50 8 . 61 μM ) [80] . The aligned vertebrate porcine protein structure of complex II lacks a hydrophobic pocket for the left R group of D19 ( Fig 6B ) . The key residues important for this binding pocket are the proline 63 , tryptophan 68 and leucine 69 of MEV-1 in C . elegans . These critical residues of MEV-1 are present in free-living as well as plant and animal parasitic nematodes ( Fig 5 ) . These sites are not conserved in vertebrates , and a hydrophobic pocket for the left R group is therefore not available for docking . Additionally , CID 2747322 binding does not form interactions with the mammalian TRP197 of SDHD-1 and Arg89 SDHB-1 as favorable as those it forms with analogous residues in C . elegans , consistent with our observation that CID 2747322 is a more potent inhibitor of C . elegans than of mammalian cells in culture ( Table 1 ) . These structural differences may identify the key drug-target interactions responsible for the specificity of CID 2747322 in nematodes , and its lack of mammalian cell activity .
The need for new anthelmintics has been voiced by WHO and echoed by many other organizations , notably the Gates Foundation . However , this is no small task [81] . To identify a nematicide that is not also a biocide is difficult enough , but then to discover a compound that does not engage biochemical pathways targeted by currently used anthelmintics is another order of difficulty . Success will depend on screening a sufficiently large swath of chemical space quickly and efficiently and it requires a test organism that provides the means to identify pathways and discriminate among modes of biochemical action . The screening platform describe here meets these various criteria . The flatbed scanner method is quick and efficient [46] . As WormScan is also inexpensive it is relatively easy to scale up screening capacity through parallel processing to be truly high throughput , for example , by permitting screens of pharmaceutical chemical libraries of hundreds of thousands of synthetic or natural compounds . In addition , the reporter organism , C . elegans , is a good surrogate for parasitic nematodes [82] . The genome content has considerable overlap with many parasitic nematodes as does its physiology , but unlike most parasitic nematodes it is easy to grow in large numbers and there is an extensive genetic and molecular toolkit to tease apart the mode of action of any new potential anthelmintic ( reviewed in [23] ) . Furthermore , this platform is flexible . Incorporating reporter proteins or other readouts of phenotypic effects can be accommodated with the existing detection system , and refinements such as higher resolution imagers could further expand the repertoire of phenotypes that are assayable . We would stress that what we are addressing here is the issue of replenishing the pool of compounds that may be potential anthelmintics . As those working in the area of animal health discovery well know this is only the first step in a long process of drug discovery and drug development before commercial use and deployment . Our screen and data analysis allowed us to identify 14 new compounds that affect C . elegans growth and fecundity . Two of these compounds are in the same chemical class as flutolanil and fluopyram ( Fig 4a ) . Not all of these compounds are nematode specific , but our analysis suggests that nematodes are more sensitive to the action of at least five of the compounds than are other organisms ( Table 1 ) . We also demonstrated that three of these five compounds do not act through the biochemical pathways targeted by the known anthelmintics ivermectin , benomyl . levamisole or amino acetotnitrile ( the other two compounds have not yet been tested ) . Thus parasites present in the wild resistant to these anthelmintics should not be resistant to this new chemistry . Two of these compounds do appear to act through a pathway common to other complex II anthelmintics including fluopyram . While these compounds satisfy one of the major criteria necessary for any new anthelmintic: phylogenetic specificity , they do not all have a novel biochemical mode of action . Nevertheless , these compounds may eventually have applications for agricultural , livestock or even human nematode parasites . For the compound with the greatest differential effect in our phylogenetic assay , CID 2747322 , we identified two separate resistant nematode lines after mutagenesis and screening . One line contains an alteration in pink-1 , the nematode homolog of PINK1 , a serine/threonine kinase involved in monitoring mitochondrial homeostasis . The second line contains an alteration in the gene mev-1 , the nematode ortholog of a succinate dehydrogenase cytochrome b560 subunit . This protein is an integral membrane protein and is part of the mitochondrial respiratory chain complex II . The MEV-1 protein is required for oxidative phosphorylation . It is probably no coincidence that both genes are involved in the function of mitochondria , and this may be the key to understanding resistance to CID 2747322 . In both examples , resistance to the compound appears to require quite specific mutations . For example , loss of PINK-1 function does not offer resistance to the compound , only a subset of missense mutations confer resistance , and many of the MEV-1 amino acid alteration cluster around a quinone binding site . PINK-1 resistance may be a form of indirect resistance but at present we cannot explain how this may occur . From our analysis of mtDNA copy number in resistant strains the mechanism of resistance does not appear to act strictly through altering copy number . We are on somewhat better footing in regard to interpreting the relationship between mev-1 function and its resistance , when mutated , to CID 2747322 . Combining our structural analysis of the succinate dehydrogenase complex with those of Burns et al [35] on resistance to WACT-11 suggests that this complex is the direct target of both CID 2747322 and WACT-11 . Our gk3613 mutation is identical to one of the many mutations isolated for WACT-11 resistance , and this mutation affects the quinone binding pocket of the complex . The Burns et al group identified mutations in all the subunits of succinate dehydrogenase and most cluster around these amino acids . Guided by their observations , and since we only identified a single mutation in mev-1 , we took advantage of the Million Mutation Project to test a number of mutant alleles for all four units of succinate dehydrogenase . From this unbiased testing ( i . e . no prior selection for resistance ) we identified mutations throughout the protein that confer resistance to CID 2747322 . Our 3D modeling of the binding of CID 2747322 to the quinone pocket suggests that resistance occurs by altering the pocket just enough to prevent the binding of the anthelmintic but not of quinone . Using this information we could also model why the succinate dehydrogenase complex from mammals is not sensitive CID2747322 . The quinone pocket of the mammalian complex is altered just enough to prevent CID2747322 binding , thus conferring natural resistance . Importantly , these findings demonstrate that our post-screening analytic approach using the power of C . elegans genetics coupled with the MMP data makes it possible to rapidly generate important mechanistic information for previously uncharacterized compounds . The mitochondrial complex II appears to be a particularly engageable target for small molecule binding . Structurally similar inhibitors have been characterized by three independent groups as anthelmintic agents ( Bayer AG [65] , the Inaoka group [80] and the laboratory of Peter Roy [34] ) . Indeed the newly certified Bayer manufactured anthelmintic fluopyram is based on this same chemical backbone and mode of action . We independently discovered the same class of complex II inhibitors and further showed that CID 2747322 can be docked into the worm complex II structure using the molecular modeling approach reported by Inaoka DK et al [80] In this study we reaffirm what other recent studies have demonstrated; C . elegans is an excellent model for anthelmintic discovery and characterization . Granted there is much to do after a potential anthelmintic is identified but these results are encouraging first steps and suggest that the C . elegans model provides a potential solution to replenishing the early stage pipeline for anthelmintics . One clear lesson from our study is that it can be difficult to exclude redundancy of effort . Between our group and the Burns et al study we have examined almost 93 , 000 compounds . Also , between the compound libraries screened in the two studies there is only an overlap of 1 , 769 compounds . However , we still converged onto a common chemical backbone with one of our most promising hits . As these libraries are commercially available there is nothing to prevent others from discovering further redundancy . More encouragingly , a comparison of the hits from our screen ( 14 molecules ) to those identified in the Burns et al study reveals that while nine of our hits fall into clusters identified in the previous study , five are novel chemical identities ( see S2 Table ) . We should point out that even those hits falling into the same cluster are not the identical molecule . In future we will be exploring more natural compound libraries . After all , this is the origin of the avermectin family of compounds , first identified in the bacterium Streptomyces avermitilis , and these have proven to be the most effective anthelmintics for the past forty years ( reviewed in [83] , [84]; also see [85] ) . In future the usefulness of our approach will be evaluated by efficacy studies , including tests of compound hits in parasitic nematode models [86–88] .
|
With over two billion people infected and many billions of dollars of lost crops annually , nematode infections are a serious problem for human health and for agricultural production . While there are drugs to treat infections , many pockets of parasites have been identified worldwide that are developing immunity to the standard treatment regimen . In this study we describe a strategy using the model organism C . elegans as a surrogate parasite to identify several new chemical compounds that may offer additional treatments for infection . We demonstrate how to use our platform to identify compounds that are specific in their effect to nematodes and are not simply biocides . We also show through genetic and molecular analysis in this organism that we can quickly identify the mode of action of any new compound . Most critically , we show that a compound first identified in a free-living nematode , Caenorhabditis elegans , is also effective on a parasitic nematode , Meloidogyne hapla . With this result and considering the level of sequence conservation across much of the nematode phyla we believe our strategy can be more widely applied to find new anthelmintics .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"mitochondrial",
"dna",
"caenorhabditis",
"parasitic",
"diseases",
"animals",
"organic",
"compounds",
"nematode",
"infections",
"animal",
"models",
"caenorhabditis",
"elegans",
"model",
"organisms",
"forms",
"of",
"dna",
"mitochondria",
"molecular",
"biology",
"techniques",
"bioenergetics",
"dna",
"pharmacology",
"cellular",
"structures",
"and",
"organelles",
"quinones",
"research",
"and",
"analysis",
"methods",
"chemistry",
"molecular",
"biology",
"drug",
"discovery",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"organic",
"chemistry",
"drug",
"research",
"and",
"development",
"library",
"screening",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"energy-producing",
"organelles",
"organisms"
] |
2016
|
Using C. elegans Forward and Reverse Genetics to Identify New Compounds with Anthelmintic Activity
|
Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex ( V1 ) . However , the computational and ecological principles underlying contextual effects are incompletely understood . We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics , and we interpret the firing rates of V1 neurons as performing optimal recognition in this model . We show that this leads to a substantial generalization of divisive normalization , a computation that is ubiquitous in many neural areas and systems . A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence . We optimized the parameters of the model on natural image patches , and then simulated neural and perceptual responses on stimuli used in classical experiments . The model reproduces some rich and complex response patterns observed in V1 , such as the contrast dependence , orientation tuning and spatial asymmetry of surround suppression , while also allowing for surround facilitation under conditions of weak stimulation . It also mimics the perceptual salience produced by simple displays , and leads to readily testable predictions . Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs , and lends statistical support to the theory that V1 computes visual salience .
Contextual influences collectively denote a variety of phenomena associated with the way information is integrated and segmented across the visual field . Spatial context strongly modulates the perceptual salience of even simple visual stimuli , as well as influencing cortical responses , as early as in V1 [1]–[12] ( for a review , see [13] ) . At least two main lines of theoretical inquiry have addressed these influences from different perspectives . First , computational models have related perceptual salience to low-level image features [14]–[18] . Of particular note for us , [14] , [19]–[21] proposed that V1 builds a visual saliency map , performing segmentation where the spatial homogeneity of the input breaks down ( e . g . , at the border between textures ) . A model of the dynamical , recurrent , interactions among nearby cortical neurons induced by long-range horizontal connections realized this theory , accounting for cortical and perceptual contextual data , including popout , visual search asymmetries , and contour integration [14] , [19]–[24] . Second , the hypothesis that sensory processing is optimized to the statistics of the natural environment [25]–[27] , has led to successful models of the linear and non-linear properties of V1 receptive fields ( RFs ) [21] , [28]–[35] . However , although collectively covering a huge range of computational , psychophysical , and neural data , these two theoretical approaches have not been unified . To this end , we introduce a computational model of the statistical dependencies of neighboring regions in images , rooted in recent advances in computer vision [36]–[39] . This model provides a formal treatment of the idea of statistical homogeneity vs heterogeneity of visual inputs , to which V1 has been proposed to be sensitive [20] . Correct inference in the model involves a novel , generalized , form of divisive surround normalization [30] , [40]–[43] , that is engaged by stimuli comprising extended single objects ( e . g . in Fig . 1 the image patch inside the region with uniform vertical texture ) , but not by stimuli involving independent visual features ( e . g . in Fig . 1 the patch across the zebra and the background ) . We focused in particular on the dependencies between orientations across space , optimizing model parameters based on natural scenes . We used the resulting model to simulate neural and perceptual responses to stimuli used in physiological and perceptual experiments to test orientation-based surround modulation . Note that we did not fit the model to experimental data from individual cells or subjects , but rather compared the qualitative behavior of a model trained on ecologically relevant stimuli with general properties of early visual surround modulation . There is a wealth of experimental results on surround modulation , some classical , and some still subject to debate . We chose to model a set of findings that we intend to be canonical examples of both sorts of results . We included data that have been the subject of previous theoretical treatments , but paid particular attention to phenomena that lie at the boundary between integration and segmentation . Indeed , one claim from our approach is that subtleties at this border might help explain some of the complexities of the experimental findings . We make predictions for regions of the stimulus space that have not yet been fully tested in experiments . In sum , we show that the statistical principles introduced can account for a range of neural phenomena that demand tuned surround suppression as well as facilitation , and encompass V1 as a salience map [20] .
We concentrated on the statistical dependencies between V1-like filters ( or receptive fields , RFs; see also Text S1 ) across space in natural images ( the images are shown in Fig . S1 ) . We adopted RFs derived from the first level of a steerable pyramid [44]; more details are provided below . For conciseness , we will refer to the projection of a visual stimulus onto an RF as the RF output . Fig . 2A–D show the joint conditional histograms of the output of one vertical RF given the output of another vertical RF in a different spatial position . In the case of white noise image patches , outputs are linearly correlated for RFs that overlap in space ( Fig . 2A ) , but not for RFs that are farther apart and so non-overlapping ( Fig . 2B ) . Natural scenes differ from white noise . The characteristic bowtie shape of Fig . 2C , D indicates statistical coordination in the form of a higher-order variance dependency [30]: i . e . , the variance of one RF depends on the magnitude of the output of the other . Further , elongated structures , such as edges and contours , cause strong co-activation of RFs with particular geometrical configurations such as collinearity , leading to linear correlations between non-overlapping collinear RFs ( the tilted bowtie shape of Fig . 2D ) , but not parallel RFs ( Fig . 2C ) . Natural images are also spatially heterogeneous: different image regions can elicit different levels of dependence between RFs outputs [45] . Extreme examples are regions involving single objects with a uniform texture ( homogeneous patches ) which show a strong variance dependence ( Fig . 2E ) , as opposed to regions spanning multiple objects or objects and background ( heterogeneous patches ) for which the dependence is weaker ( Fig . 2F ) . We extended a well-known probabilistic model of the variance dependence of Fig . 2C , D ( the Gaussian Scale Mixture , or GSM; [46] ) to capture the variability across image regions exemplified in Fig . 2E , F . The GSM describes an RF output , k , as a random variable obtained by multiplying a Gaussian variable ( i . e . a random variable that is Gaussian distributed ) , , and a second random variable that takes only positive values , , also called the mixer . ( 1 ) The mixer in the model can be shared between multiple RFs ( we then describe these RFs as being co-assigned to the same mixer ) , and can therefore generate statistical coordination , and can be intuitively thought of as representing a relatively global image property , such as contrast , that changes smoothly across space . In contrast , the Gaussian variable in the model is local to each RF . Consider the case of two RFs , ( k1 , k2 ) , whose respective Gaussian variables are multiplied by a common mixer . Then the coordination is generated in the following way: in a specific instance where has a large value , both Gaussian variables are multiplied by a large value and therefore k1 and k2 are more likely to be large together . Conversely , when takes a small value , k1 and k2 will likely be small together . This generates precisely the type of dependency observed in Fig . 2C , D . The tilt of the bowtie evident in Fig . 2C comes from linear correlation between the Gaussian variables , captured by their covariance matrix C . We then introduced a variant of the GSM ( see also [37]–[39] , [47] ) that could capture both dependent and independent RFs outputs . Since we were interested in center-surround effects , we designed the model to approximate variance dependencies between a center group of RFs and a surround group , using a combination of: 1 ) a standard GSM for the case in which the center and surround groups are dependent ( they are co-assigned to a common mixer , and can be linearly correlated ) ; and 2 ) an independent GSM model , whereby the center and surround do not share a common mixer and lack linear correlation . This model , which combines the two extreme cases , is technically a Mixture of GSMs ( MGSM [39] , [47] ) . We implemented the model with a bank of 72 bandpass oriented filters or RFs taken from the first level of a steerable pyramid [44] , [48] ( see Text S1 ) . We used 8 RFs in a central position ( center group , whose outputs are denoted in the following by k , and the corresponding Gaussian variables by ) comprising 4 orientations , 0 , 45 , 90 and 135 degrees from vertical , each at 2 phases . The inclusion of multiple orientations in the center group was motivated by the strong variance dependence typically observed across oriented RFs at a fixed position; it also guaranteed local contrast normalization of the model responses ( see below ) as commonly assumed in divisive normalization [41] . The 2 phases correspond to a pair of even- and odd-symmetric RFs ( quadrature pair ) . For the surround we used 64 RFs ( whose outputs are denoted in the following by S , and the corresponding Gaussian variables by ) comprising 4 orientations , 2 phases , and 8 positions on a circle surrounding the center RFs with radius 6 pixels . We organized the surround RFs into 4 separate groups , each including all RFs of a given orientation . RFs had peak spatial frequency of 1/6 cycles/pixel , and a diameter of 9 pixels and were partly overlapping . The orientation bandwidth was chosen to approximate the median value found in V1 by Ringach et al . ( [49] 23 . 5 degrees half-width at 70% height of the tuning curve ) . The qualitative character of the results did not change when using different bandwidths . We included multiple surround groups to guarantee that all orientations were treated equally . Fig . 3 illustrates the spatial layout of the RFs , and their structural dependencies in the different mixture components . The leftmost panel depicts the component ( denoted by ) in which none of the surround groups is co-assigned with the center ( each RF group in the surround has a different color ) . The remaining four components ( denoted by for ) comprise the cases in which all the center RFs , and just those surround RFs that favor orientation , are co-assigned to a common mixer . For example , under the same mixer multiplies all the center Gaussian variables and the Gaussian variables for the vertical surround , and the corresponding RFs are co-assigned ( indicated by black color ) . Note that even when the center and surround are independent , we still assume that the RFs in the surround share the same mixer , an approximation that was needed for computational tractability ( see Discussion ) . The parameters governing RF interactions – i . e . the covariance matrices and the prior probability of each component of the MGSM – were optimized by maximizing the likelihood of an ensemble of natural scenes ( downloadable from http://neuroscience . aecom . yu . edu/labs/schwartzlab/code/standard . zip ) . Mathematical details are provided at the end of Materials and Methods . Fig . 4A depicts the resulting covariance matrices . Notice that in the co-assigned components ( top row ) but not in the independent component ( bottom row ) , the model found larger variances for each central RF and its collinear neighbors , and larger covariance between them , than for its parallel neighbors . For all the results in the paper except where noted , and in Text S4 , we renormalized the covariance matrices to make them identical under rotation of the spatial configuration of RFs: e . g . , the covariance between the vertical central RF and its collinear neighbors ( vertical , above and below the center ) under , was forced to be the same as the covariance between the horizontal central RF and its collinear neighbors ( horizontal , left and right of the center ) under . In practice , the effect of the renormalization is to guarantee that model responses corresponding to different RF orientations are identical when the respective input stimuli are rotated copies of each other . However , to explore the effects of cardinal axis biases and variability across different scenes , we also trained the model separately on each of 40 natural images from the Berkeley Segmentation Dataset ( http://www . eecs . berkeley . edu/Research/Projects/CS/vision/bsds ) , and did not renormalize the covariances . The training was repeated 3 times for each image from random starting points . Training on individual images converged to similar values each time , except for one image on which it did not converge . This image was therefore excluded from further analysis . To relate the model to contextual modulation in the visual cortex we assume that firing rates in V1 represent information about the Gaussian variables associated with the center RFs . We choose because it represents the local structure of the RFs , which is what contains specific information about the local input image patch . By contrast , encodes more general information , i . e . the average level of activity in a neighborhood of RFs across orientations and spatial locations . The MGSM is a statistical model of images , which is sometimes called a generative or graphics model [50] . The task as a whole for vision is to invert this model – using Bayes rule to find the posterior distribution over and . However , here , we compute just the expected value of the Gaussian variables , which we assume to be related to V1 firing rates ( via Equation 3 , below ) : in the rest of the paper we also call such estimates the model responses . In practice , we obtained model responses in two steps , i . e . we first collected linear RFs outputs with a given input stimulus , and then we used them to compute the Gaussian estimates; this is an abstract schematization of how V1 firing rates are produced , conceptually similar to the canonical linear-nonlinear scheme [51] , and does not imply that the two steps are actually preformed by separate V1 mechanisms . We will address in the following section the relation with divisive normalization and the possible neural mechanisms underlying the computations presented in the remainder of this section . For a given input stimulus , we first collected the outputs of center RFs k and surround RFs S . We then computed the expected value of the Gaussian variable corresponding to a center RF ( e . g . , tuned to vertical ) : ( 2 ) Equation 2 comprises the sum of the estimates under each of the mixture components ( respectively denoted and ) , weighted by the posterior probability of the components , and . In the first term the surround is not co-assigned . The second term sums over each of the components corresponding to the co-assigned surround orientations . We derived an analytical form for both the mean estimates and the posterior probability , as described below . Eventually , we combined the estimates ( Equation 2 ) for the two phases of the RF ( denoted by and ) , to obtain orientation tuned , phase invariant responses: ( 3 ) We obtained the mean estimates under each of the components as follows . In the GSM , given knowledge of the value of the mixer , one can obtain by definition as . However , the visual system does not know the value of , and so can only perform statistical Bayesian inference using information about the prior distribution of . We chose a Rayleigh prior for the mixer variables which gave analytically tractable solutions; however , the qualitative behavior of our simulations would be the same for a range of priors ( similar to [37] , [47]; and see also priors in [46] ) . This choice resulted in the following estimates for the co-assigned components: ( 4 ) and similarly , we obtained the estimate for the component in which the surround is not co-assigned: ( 5 ) where nk represents the number of RFs in the center group , and n the total number of center and surround RFs . The terms denoted by are generically defined as follows: ( 6 ) where , in , x is to be replaced with the center filter outputs and C is to be replaced with ; and in , x is to be replaced with the center filters and the surround filters with orientation , and C is to be replaced with . We introduced a small constant ε which sets a minimal gain when the filter activations are zero , to prevent infinities in Equations 4 , 5 when the RF outputs are zero . In all the simulations , was set to a value ( 10−10 ) several orders of magnitude smaller than the smallest value observed for the other term under the square root . is the modified Bessel function of the second kind , and the ratio of Bessel functions diverges at and asymptotes to 1 at infinity , at a rate that depends on n; it remains approximately constant over the range of values of in all our simulations . Eventually , we inferred the posterior probability , , that the center group shares a common mixer with the surround group labeled , using Bayes rule: ( 7 ) and substituting the first term on the r . h . s . with the learned co-assignment prior , and the second term with its analytic form ( see below , Equation 10 ) . The inferred model responses ( Equations 2 , 4 , 5 ) thus constitute a form of divisive normalization [40]–[42] , where the terms in the denominator represent the normalization signal , and the RFs that contribute to form the normalization pool . It has been shown that divisive normalization has the effect of reducing the higher-order dependencies illustrated in Fig . 2C , D , and accounts for some data on V1 surround modulation [30] . However , our model generalizes standard divisive normalization in two substantial ways . First , consider the effect of covariance . Normalization allows cells to discount a global stimulus property that is shared across RFs , i . e . contrast in simple divisive normalization , or the mixer value in the GSM . The mixer corresponds to RFs that are statistically coordinated and tend to be high or low together in their absolute value . However , in the generative model , large outputs from RFs that often respond together ( i . e . RFs with large covariance or linear correlations ) could be generated either by a large value of the mixer or by similar draws from the correlated Gaussians; whereas similar , large outputs from RFs that rarely respond together ( small covariance ) are more likely to have been generated by a large value of the mixer . Therefore linearly correlated RFs should contribute less to the estimate of the mixer ( which is loosely proportional to the normalization signal ) . This arises in the model since the covariance matrices learned from scenes weight the contribution of the RFs to the normalization signal ( Equation 6 ) . For instance , a pair of RFs with large variances and large covariance between them ( often leading to negative values in the corresponding off-diagonal term of C−1 ) exerts less normalization than a pair of RFs with the same variances but small covariance . In addition , an RF with large variance ( corresponding to small diagonal terms in the inverse covariance ) weights less than one with small variance . In the visual cortex , the effect of such weights may be represented indirectly in the strength of excitatory connections that are set with development; indeed the higher correlations , in the model , between overlapping RFs , as well as non-overlapping collinear RFs , are qualitatively in agreement with the known specificity and anisotropy of horizontal and feedback connections [52]–[58] ( see also Discussion ) . Second , Equation 2 uses a stimulus-dependent normalization pool , since , for any given input stimulus , only RFs that are inferred as being statistically coordinated and thus to share a common mixer , are jointly normalized ( the normalization pool comprises different RFs for each mixture component in Equations 4 , 5 ) . The same RFs can be coordinated for some stimuli and not for others . The computation involved , which in the model is distinct from the evaluation of the corresponding normalization signals ( Equation 6 ) , does not necessarily have to be segregated in the biological system , and may be achieved by a number of neural mechanisms . One possibility may be that the normalization signals are computed by inhibitory interneurons that pool the outputs of distinct subpopulations: the different firing thresholds or diversity across types of interneurons [59] , [60] have been previously indicated as mechanisms that may control whether or not surround inhibition is active on a given input . A complementary view is that surround modulation is an emergent property of the cortical network; in this scenario , the strength of the surround influence may be determined by stimulus-dependent switching between cortical network states [61] or changes in functional connectivity [62] , or by the exact balance between excitatory and inhibitory conductances , which are known to change in parallel with surround stimulation [63] . For a given input stimulus , we inferred the posterior co-assignment probabilities of each mixture component , , using Bayes rule ( see Equation 7 ) . These probabilities measure how well each component explains the input data . Intuitively , the probability is large when the stimuli in the center and surround are similar ( e . g . for gratings of similar orientation and contrast ) , and for a given stimulus , it tends to be larger at high contrast as illustrated in Fig . 5 . More precisely , is a function of the center and surround RFs outputs , combined using the corresponding covariance matrices as in Equation 6 . In our implementation , for a given input stimulus the probability of co-assignment does not vary across center orientations ( because we grouped together all center RFs with different orientations ) . The surround RFs are grouped together according to their orientation , and so all RFs within one surround group have the same probability of being co-assigned with the center , but each surround group has a different probability ( e . g . for a large vertical grating , the vertical surround has high probability , while the horizontal surround has low probability ) . On each given stimulus , the probabilities across the 4 surround groups , plus the probability of no assignment ( i . e . that no one of the surrounds is co-assigned with the center ) , jointly add up to 1 . Note also that , differently from standard divisive normalization which is inherently suppressive , our model encompasses both surround suppression and facilitation as summarized in Fig . 6: a strongly driven surround suppresses center responses , whereas a weakly driven surround facilitates , and the relative modulation is larger when the center RF is weakly driven . The full distribution of the RFs variables under the MGSM is given by the mixture model: ( 8 ) in which the terms represent the joint distribution of the center and surround RF outputs k and S under each of the five possible mixture components , weighted by their prior probabilities . The terms can be derived analytically as follows . First , we assumed a Rayleigh prior on the mixer variables: ( 9 ) Under the configuration in which the surround of orientation is co-assigned with the center , we can exploit the independence among groups that do not share a mixer , and obtain: ( 10 ) where , in the first line , denotes the surround RFs with orientation , and similarly for . In the second line the factors are derived as in a standard GSM , as detailed in [37] , [47]; nk and nS represent the number of RFs in the center and surround groups respectively , and n = nk+nS; is the covariance matrix among the center group and the surround group with orientation ; is the covariance of the surround with orientation ; is the modified Bessel function of the second kind . The terms denoted by are defined as in Equation 6 . Similarly , we can derive the distribution conditional on , namely in the case that all groups are mutually independent: ( 11 ) where is the covariance matrix of the center Gaussian variables , and is defined as in Equation 6 . The parameters governing RF interactions – i . e . the covariance matrices and the prior probability of each component of the MGSM – need to be optimized for an ensemble of natural scenes . The training data were obtained by randomly sampling 25000 patches from an ensemble of 5 natural images from a database of standard images used in image compression benchmarks ( known as Einstein , boats , goldhill , mountain , crowd; see Fig . S1; the images are downloadable from http://neuroscience . aecom . yu . edu/labs/schwartzlab/code/standard . zip ) . The parameters to be estimated were the covariance matrices associated to the different components of the model , collectively denoted by , as well as the prior probability of each component , denoted by and . The model learned the full covariances , including the terms coupling opposite RFs phases which were typically close to zero . To find the optimal parameters we maximized the likelihood of the data under the model; we implemented a Generalized Expectation-Maximization ( GEM ) algorithm , where a full EM cycle is divided into several sub-cycles , each one involving a full E-step and a partial M-step performed only on one covariance matrix . In the E-step we computed an estimate , , of the posterior distribution over the assignment variable , given the RF responses and the previous estimates of the parameters ( denoted by the superscript old ) . This was obtained via Bayes rule: ( 12 ) and similarly for . Then in the M-step we adopted conjugate gradient descent , to maximize the complete–data Log Likelihood , namely: ( 13 ) The training was unsupervised , i . e . we did not pre-specify the co-assignments of the training set , but rather let the model infer them at each E-step . The EM algorithm is not guaranteed to find a global maximum; however , repeated training runs with different randomly chosen starting points produced convergence to similar parameter values . More details are provided in Text S2 ( see also [47] ) .
We learned the parameters of the model ( i . e . , the covariance matrices and the prior co-assignment probabilities ) entirely from the natural scenes , and then fixed them . We first evaluated the model by comparing its response ( Equation 3 ) to presentations of different forms and sizes of sinusoidal gratings with those described in previous neurophysiological studies of spatial contextual modulation . Physiology experiments have made extensive use of gratings to show that stimuli presented in regions of visual space that do not drive the neuron ( i . e . the surround ) can still strongly modulate the responses to a stimulus presented within the RF . First , we tested gratings of variable size and contrast whose orientation and spatial frequency match the chosen RF . Experiments in cat and macaque monkey V1 [8] , [64] , [65] show that at fixed contrast , neural responses typically increase as a function of stimulus size up to a peak value , and then decrease for larger stimuli that recruit the suppressive surround . The peak responses correspond to larger diameters at low than high contrasts ( Fig . 7A-left; the studies cited above reported an average expansion factor across the population in the range 2 . 3 to 4 ) . Fig . 7A-right shows the similar behavior of our model , including a contrast-related expansion of similar magnitude from high to low contrast in our model . For intermediate contrasts we observed a gradual peak shift . The contrast-dependence of size tuning has previously been ascribed to divisive normalization [30] , [35] . Cavanaugh et al . [8] showed that a divisive model with variable gains for the center ( numerator ) and surround ( denominator ) accounted well for the contrast-related expansion; model parameters obtained from data fitting showed that the relative influence of the surround increased with contrast , suggesting that the inhibitory surround is less sensitive than the center at lower contrasts ( but see also [62] , that supported the alternative hypothesis that low contrast enhances recurrent excitatory interactions ) . In our model , the flexible assignment process provided a related , but statistically motivated explanation of the expansion . For gratings of intermediate sizes that covered the surround only partially , the co-assignment probability was larger when the contrast was high than when it was low ( Fig . 7B; see also Fig . 5 ) . Therefore larger stimuli were necessary to recruit surround modulation at low than high contrast . This pattern of assignments was obtained directly from statistical inference rather than from data-fitting constraints , and did not depend on any asymmetry between center and surround . We next addressed three sets of data related to the orientation tuning of surround modulation ( Fig . 8 ) , for which the involvement of multiple surround normalization pools in the model provided a novel , potentially unifying explanation . We first verified that the model produces contrast-invariant orientation tuning curves ( Fig . S2 ) . We then assessed how a surround annular grating modulated the response to a fixed , optimally-oriented central grating , as a function of their orientation difference . Without loss of generality , we refer to the central grating as being vertical , both for the data and the model . Several studies , e . g . [7] , [9] , [10] , [66]–[68] , reported that the strongest suppression ( relative to the response to the center grating alone ) occurred when the center and surround were similar in orientation , while large orientation differences led to little or no suppression , as exemplified by the cell in Fig . 8A-top-left [10] . Our model reproduced these features of surround orientation tuning ( Fig . 8A-top-right ) . We also explored the model behavior as a function of the stimulus contrast and the sizes of the central grating patch and surround annulus ( see Text S3 ) . Generally speaking , in the model we observed surround facilitation when the center was not optimally stimulated , and the surround was weakly driven and co-assigned ( Fig . 6 ) . For instance , when we introduced a gap to make the center patch smaller than the center RFs , and with the surround annulus covering only part of the surround RFs , we found facilitation for large orientation differences between center and surround . The response was maximized with annuli tilted less than 90 degrees relative to the center ( Fig . 8A-bottom-right ) . These conditions have not been explored systematically in experiments , but some evidence for the above effects was reported by studies that tested surround orientation finely ( e . g . , Fig . 8A-bottom-left , reported in [10] using mid contrast; [67] using low contrast in the center and high in the surround ) , and recent models of the perceptual tilt illusion have implicated facilitation from non-orthogonal surrounds [69] , [70] . On the other hand , other experimental studies have not reported facilitation at all [7] , [68] , and [8] reported that an iso-oriented surround stimulus is always suppressive , regardless of the contrast of the center and surround stimulus . Our model partly failed to reproduce this observation , since it produced facilitation when we combined small center patches with large gap sizes at low contrasts ( see Discussion; see also Text S3 ) . The dependence of surround tuning on contrast , and more generally on how strongly the RF is driven , has yet to be explored more systematically . The responses of the model ( Fig . 8A-right ) depended on the interaction between different surround components as follows ( Fig . 8B; see also Text S3 for more examples ) . In the top row , the center grating patch partly activated surround RFs , leading to a high co-assignment probability for the vertical surround , regardless of the annulus orientation: the strength of the suppression then simply depended on how much the surround annulus increased the outputs of the vertical surround RFs . In the bottom row , we reduced the size of the center stimulus . In this case , for stimuli with small orientation differences ( less than 45 degrees ) between the ( vertical ) center and the surround we observed large co-assignment probability for the vertical surround group: in this regime , surround modulation was suppressive . Modulation switched to facilitation as the orientation difference increased and surround strength decreased ( corresponding to reducing for fixed in Fig . 6 ) . At much larger orientation differences , the co-assignment probability decreased for the vertical surround group , but increased for the surround groups oriented similarly to the annulus , leading to less , and eventually no , facilitation . The model response thus combined , via Equation 2 , facilitation from the ( weakly driven ) vertical surround with the suppressive influence of the ( strongly driven ) diagonal and horizontal surrounds . Interestingly , the presence in the model of multiple surround groups also allowed it to reproduce data on surround modulation of a non-optimal center stimulus ( Fig . 8C ) . With this type of stimulus , several groups reported that maximal suppression occurs more often when the surround is oriented similarly to the center , than when the surround is oriented similarly to the cell's preference [9] , [10] , [54] , [71] . For the simulations we fixed the orientation of the central patch to 45 degrees from vertical , but continued to report the posterior mean of k0 . Model responses showed the same effect , since stimuli with small orientation differences between center and surround led to large co-assignment probability for the surround group oriented 45 degrees from vertical ( Fig . 8D ) , even though this orientation differed from the vertical orientation on which the model's response was based . To our knowledge , the result of Fig . 8C has not previously been modeled . Our statistical explanation is based on the possibility of using several , differently tuned normalization pools . Finally , we compared the orientation tuning curves measured with small gratings confined to the center RF , and large homogeneous ( single orientation ) gratings that also covered the surround . Chen et al . [11] reported that increasing the grating's diameter , up to 5 times the center RF size , often leads to narrower tuning curves in V1 ( Fig . 8E; Vinje and Gallant [72] previously reported that , with natural images , stimulation of the RF surround enhanced the cells' selectivity ) . In our simulations , the surround was not co-assigned when the grating was small . For large gratings that covered the full extent of the surround , center responses were normalized by the surround group with orientation closest to that of the stimulus ( Fig . 8F ) . Therefore , when the orientation changed gradually from optimal to orthogonal , the vertical central RF output decreased , but the outputs of the co-assigned surround group remained approximately constant . This led to stronger suppression ( relative to the small stimuli ) at non-optimal orientations , i . e . a narrower tuning curve . We then addressed the spatial organization of surround modulation , to illustrate the interplay between the assignment process and the spatial structure of correlations learned by the model from natural images . Different regions of the RF surround can produce different levels of modulation when stimulated with grating patches oriented similarly to the center ( called positional bias ) . Walker et al . [7] and Cavanaugh et al . [9] reported that the location of the surround that elicits maximal suppression varies across the population , but is found approximately 2 to 3 times more often near the end of the RF ( a collinear arrangement ) than near the sides ( a parallel arrangement ) . The cell of Fig . 9A-left illustrates the positional bias with stronger suppression for collinear stimuli . The model reproduced the same qualitative behavior with similar stimuli ( Fig . 9A-right ) , reflecting a higher co-assignment probability for the collinear arrangement ( Fig . 9E ) as explained below . To elucidate the origins of this response of the model , we systematically tested a larger range of contrast and size values than has so far been experimentally examined ( Fig . 9B , C ) . First , consistent with the above , we observed strong suppression for large collinear stimuli at moderate to high contrasts . We also found that collinearity could lead to facilitation under some conditions such as low contrast , thin , stimuli . Conversely , the parallel arrangement produced little modulation , mostly suppressive . Direct comparison of the two arrangements ( Fig . 9D ) showed that , depending on where the stimuli fell in the contrast/size space , the collinear surround could elicit both larger ( low contrast , thin stimuli ) and smaller ( mid contrast , fat stimuli ) model responses than the parallel arrangement . Model simulations across such stimulus space thus offered a specific , testable prediction . Our model explains the observed spatial asymmetry of modulation by the form of the covariance matrices learned from natural scenes . In the mixture component with dependent center and vertical surround groups , the variances of the vertical center RF and its collinear neighbors were similar , and higher than the variances of the parallel neighbors ( Fig . 4A ) . Recall that the co-assignment probability is a function of the terms , and it is larger when they have a similar magnitude; these terms are computed using the covariance matrices ( see Materials and Methods ) and were more similar between center and surround for stimuli that covered the center and collinear surround , than for stimuli covering the parallel surround . This made collinear stimuli more likely to be co-assigned ( Fig . 9F , G ) and modulate center responses . At mid contrast , fat surround , this led to stronger collinear suppression . The sign of the modulation switched to facilitation when we used low contrast , thin collinear stimuli ( thinner stimuli produce weaker surround RFs outputs thus reducing for fixed ; see Fig . 6 ) . Collinear facilitation with grating stimuli has been observed in V1 , although with the contrast not matched between center and surround; for instance , Polat et al . [73] reported that a high contrast collinear surround could facilitate the responses to a low contrast central grating patch , but suppress a high contrast center . Kasamatsu et al . [74] quantified the average modulation across their population for collinear flankers presented at 0 . 5 and 0 . 8 contrast , and reported a peak facilitation of approximately 17% when the center contrast was similar to the contrast threshold of the cells , and weak suppression in the range 2–5% for larger center contrasts ( up to >10 times the cells' threshold ) . We observed similar changes in model responses: the average modulation , for flankers presented at contrasts ranging between 0 . 5 and 0 . 8 , showed 16% peak facilitation at low center contrast ( 0 . 2 ) , and suppression in the range 1–6% for larger center contrast ( 0 . 35 to 0 . 8 ) . Interestingly , the model also reproduced “far” surround facilitation ( see e . g . large gap sizes in the first figure of Text S3 ) , which was observed in [12] often with low contrast center but only rarely with high contrast center . Our model provides a common explanation for the two phenomena: A low contrast central grating by itself did not recruit surround modulation ( see Fig . 5B ) , but the composite stimuli did . In both experiments , the surround was weakly driven , thus leading to facilitation ( corresponding to small for fixed in Fig . 6 ) . Conversely , a high contrast center grating by itself recruited the surround , and so the addition of the surround stimuli only increased the normalization signal , albeit weakly , and therefore became suppressive . In addition to the effect we discussed , Cavanaugh et al . [7] reported a complementary , although weaker , positional bias when using surround patches orthogonally oriented relative to the center grating: They observed more often stronger suppression from orthogonal patches placed at the side , rather than at the end , of the RF . Our model qualitatively reproduced this finding , as illustrated in Text S4 . However , that there is a considerable scatter of positional biases due to cell-to-cell variability in [7] , [9] ( Fig . 10A–C ) . We explored in the model whether some of the variability could relate to the properties of natural scenes; note that for these simulations ( Fig . 10 and Text S4 ) we did not impose the rotational invariance of the covariances , as explained in Materials and Methods . First , we found that , due to the predominance of cardinal orientations in scenes , the bias was stronger for the model units with cardinal orientations than with diagonal orientations ( Text S4 ) . We then expanded on the previous observation , by optimizing the model on several different natural images separately , and indeed found a large scatter of positional biases ( Fig . 10D–F ) qualitatively consistent with the data , although to a smaller degree . Our model may be missing an additional source of variability related to scene statistics , due to the constraint of always grouping together iso-oriented RFs at all surround locations: it is possible that a more flexible assignment scheme with multiple mixer pools and RFs across space ( e . g . [37] ) could capture statistical regularities in scenes beyond collinearity ( see also Discussion ) . The data presented so far provided an illustration of the biological significance of the two key components of the model , i . e . the assignment and the covariances . As a control , we presented the same stimuli to a reduced model that ignored the assignments and assumed that center RFs were always normalized by the vertical surround group . Such reduced model produced a much weaker contrast-dependence of size tuning of the surround compared to Fig . 7; and it did not produce maximal responses for intermediate orientation differences as in Fig . 8A , nor could it capture the effects of Fig . 8C , E . We also considered a reduced model that assumed diagonal covariance matrices , rather than learning them from natural images . As opposed to Fig . 9 , this model produced no difference in co-assignments , nor in surround strength , from different surround locations . We then asked whether the same statistically motivated computational principles underlie perceptual salience: local image regions are deemed salient when e . g . they can be detected more readily , appear to have higher luminance , or attract human gaze more often compared to the remaining parts of the image ( see [15] , [20] and references therein ) . Several models [15]–[18] have related bottom-up salience to the local contrast in image features ( e . g . , luminance , orientation , color channels ) . Most pertinently for us , the idea that V1 is a neural substrate of salience processing has been formalized theoretically and supported experimentally [20] , [21] , [75]–[77] . To address saliency effects , we combined model responses corresponding to all four center orientations , as is typical in saliency computations . Specifically , for a given image , at each input location we computed the responses as in Equation 3 separately for each orientations , and then took the maximum [22] . The result was interpreted as a ( statistically-based ) salience map . The stimulus shown in Fig . 11A-left illustrates the phenomenon of perceptual pop-out of a central target that differs from a uniform background of distractors by a single feature , in this case orientation . Consistent with the percept , the target has by far the greatest salience in the output salience map ( Fig . 11A-right ) . Nothdurft [3] reported that — with a luminance matching experiment above detection threshold — the relative target saliency saturated with a center-surround orientation difference around 45° ( Fig . 11B ) . By comparison , many salience models exhibit a linear increase in response ( see [16] for discussion ) . Our model correctly captured the form of the nonlinearity , with the specific nature of the saturation depending on stimulus design: with high contrast , 5 pixels long bars , the vertical surround group always normalized the responses at the location of the center bar , and therefore surround bars oriented 45 degrees or more from vertical modulated center responses only weakly ( Fig . 11C; see Fig . S3 for examples with different bar contrasts/sizes/separations ) . Another important class of saliency effects involves collinear facilitation . We tested the model with simple arrangements of bars that are known to produce higher perceptual salience for collinear ( Col ) than parallel sets of bars ( Par ) , relative to a homogeneous texture ( Txt ) of iso-oriented bars ( Fig . 12 ) . We used the same length , contrast , and spacing of bars as in Fig . 11 . To explain the salience maps , first note that model responses at the center of each bar were always normalized by the outputs of the surround group with the same orientation ( i . e . the co-assignment probability was large at every location , due to partial encroachment of the center bar on surround RFs and vice versa ) ; therefore the differences between Col and Par could not be attributed to different assignments as in Fig . 9 , but rather to the asymmetry of the covariance matrices as explained below . First , we consider the bars on the boundary regions . In Fig . 12A , the boundary includes the middle row of collinear horizontal bars ( Col ) , and the neighboring rows of parallel vertical bars above and below ( Par ) . For such bars , only a portion of the surround has the same orientation as the center , leading to overall less surround activation at the boundary than inside the homogenous regions ( Txt; i . e . , in Fig . 12A , where each vertical bar is entirely surrounded by vertical bars ) . Since the co-assignment probability was large for both the border and homogenous region , we observed weaker surround suppression , and thus larger salience , at the border than in the homogenous regions . In addition , we observed a larger salience enhancement for the middle row of bars , relative to the homogeneous region , when they were collinear ( Fig . 12A ) than parallel ( Fig . 12B ) . Collinear and parallel salience enhancement were defined as ( Col-Txt ) /Txt and ( Par-Txt ) /Txt , respectively . The larger collinear enhancement was due only in part to the stronger RF linear outputs for collinear stimuli ( since surround bars encroached on the center RF ) and was largely attributable to the covariance matrices learned from scenes . As explained in Materials and Methods , in the co-assigned model components , the collinear surround RFs had higher variance , and higher covariance with the center RF , than the parallel RFs . This led to smaller normalization signals , and therefore weaker suppression , for collinear than parallel stimuli . As a control , we computed the salience maps with a reduced version of the model that assumed diagonal covariance matrices and equal variances , and found that the difference of salience enhancement between Col and Par was reduced for Fig . 12A , while it was increased for Fig . 12B , relative to the full model . Therefore , the control was less compatible with collinear facilitation . The two examples above are to some extent combined in the border effect in stimuli such as Fig . 12C , where the side of the border on which the set of bars adjacent to and defining the border are collinear is perceptually more salient than the other [75] . The salience map computed by the model also showed this effect , due to the covariance structure , as explained above .
Two main statistical innovations were critical for capturing the full set of biological data . First , our model provides a formal treatment of the idea of statistical homogeneity vs heterogeneity of visual inputs . We proposed that V1 is sensitive to whether center and surround responses to a given stimulus are inferred to be dependent or independent based on the statistical structure of natural scenes . This sensitivity is actually required to capture the stronger statistical dependence apparent within spatially extended visual objects . In our model , it implies that surround modulation is fully engaged by stimuli comprising extended single objects , but disengaged by stimuli with entirely independent center and surround . This ( dis ) engagement proved to be crucial to obtain the neural effects in Fig . 7–9 . In particular , the model , by choosing among multiple normalization pools with different tuning characteristics , captured the observation that the most suppressive surround orientation is the one matched to the center stimulus , not the center preference ( Fig . 8C ) , which had not previously been modeled . Cavanaugh et al . [9] attributed the effect partly to the encroachment of the surround annulus on the center RF , a factor that they reduced , but could not eliminate entirely , by introducing a gap in the stimuli . In the model we had fuller control over the RFs and the encroachment for the stimuli of Fig . 8A , C , suggesting that at least in part the effect might reflect genuine surround modulation , and can be explained on a statistical basis . The inclusion of multiple surround pools also allowed our model to capture the narrowing of orientation tuning curves with large stimuli ( Fig . 8E; see also [35] , [81] ) . The assignments inferred in Fig . 8A are also consistent with ( and thus provide statistical support for ) those assumed by fiat in the idealized model of [70] , where they were important for capturing repulsive and attractive perceptual biases in the tilt illusion . The flexibility of the normalization pool also implied that the model could exhibit surround facilitation . While a strong ( and co-assigned ) surround was suppressive , a weak ( and co-assigned ) surround was facilitative , when compared to responses in the absence of surround ( not co-assigned ) . The magnitude of the modulation was larger for weaker center RFs outputs ( Fig . 6 ) . We suggest that this feature of the model can unify disparate experimental observations that weak surround stimulation ( e . g . using large gaps , or small collinear stimuli ) in conjunction with weak center stimulation ( e . g . low contrast ) can lead to facilitation [10] , [12] , [73] , [74] . However , the precise dependence of surround tuning , and facilitation , on the optimality of the center stimulus has not been explored systematically , and further experiments will be required to test this general prediction of the model . We should note also that facilitation of an optimal center stimulus by a surround stimulus with largely different or orthogonal orientation , which has also been observed experimentally [9] , [10] , [67] , could be explained more simply by dis-inhibition ( or inhibition of the suppressive surround , [9] , [35] ) . Such an explanation demands some form of recurrent processing that is missing in our current model . The second key statistical feature in our model , namely the RFs covariance matrices , reflects the structure of linear correlations found in natural images . For image patches that the model determined to be homogenous , we observed larger variance for the central RF and its collinear neighbors , and larger covariance between the collinear neighbors , than for any other surround location ( Fig . 4 ) . This impacted divisive normalization in two important ways that have not been explored previously , and that allowed us to address both the spatial asymmetry of cortical surround modulation , and collinear enhancement of salience with simple displays . At high contrast , collinear surrounds suppress targets more than parallel surrounds . The model reproduced this characteristic ( Fig . 9A ) because collinear stimuli are more likely to engage surround modulation . In addition , it predicted that whether collinear grating patches suppress or facilitate a target depends on stimulus contrast and angular size ( Fig . 9B ) , consistent with the limited data on collinear facilitation for unmatched contrasts ( low in the center , high in the surround [73] ) . The latter crucially depended on the larger probability for a high than low contrast central stimulus to engage surround normalization by itself , a fact that also led to “far” surround facilitation [12] . Also , by learning the covariance structure individually for different natural images ( Fig . 10 ) , we suggested a possible relation between the scatter of positional biases observed experimentally with both iso- and orthogonally oriented surround patches , and scene statistics . Furthermore , the model generated salience maps that exhibited collinear enhancement and texture border assignment with simple bar displays ( Fig . 12 ) . These stimuli always engaged surround modulation in the model . Due to the correlation structure of Fig . 4 , for any bar in the display , the collinear neighbors contributed less to the normalization signal , and so were less suppressive , than the parallel neighbors ( Materials and Methods ) . This might also provide a basis for contour integration , namely the pop-out of a contour composed by collinear , but spatially separated , oriented bars surrounded by randomly oriented distractors . Excitatory interactions among collinear RFs led to contour integration in a dynamical model of V1 saliency [75] and in other computational approaches [82]–[85] . In addition , Li et al . [86] reported that the firing rates of V1 neurons in monkey reflect contour integration only after the animals learned to perform contour detection . Our model produced only marginal contour enhancement with complex bar stimuli . However , since we derived collinear interactions from scene statistics , it is possible that supervised training on contours may further amplify such interactions . Alternatively , our model may need to include higher order statistics [87]–[89] that could be learned at higher processing stages [90] . Our statistical modeling relates most closely to a number of recent powerful approaches in computer vision and statistical learning which have not been directly applied to contextual effects in neuroscience . These include hierarchical models for unsupervised learning of statistical structure in images ( e . g . [91] ) , extensions of Independent Component Analysis ( e . g . [31] , [92] ) , and models that explain image patches based on the competition , rather than linear superposition , of independent components ( e . g . [93] ) . Other variants of the GSM have proposed schemes to learn RF covariance matrices with applications to computer-vision problems such as image denoising and quality assessment ( e . g . [94] , [95] ) ; in particular , Hammond and Simoncelli , 2008 [96] used a mixture of GSMs that allowed for mixing based on an orientedness prior and allowed them to improve noise removal in local oriented structure . The model of Schwartz et al . [37] allowed for arbitrary pooling of RFs under multiple mixers . This started from a bank of linear RFs over the full visual field , and a collection of normalization pools . Each RF learned a prior affiliation with a number of the pools across a whole collection of image patches; but for any single patch , could only be normalized by a single pool . This resulted in an arrangement in which pools varied in shape and composition thus covering a region of visual space with some probabilistic overlap ( see Fig . 11 of [37] ) . This , and other related scene statistics models [36] , perhaps suggest some analogy of RF arrangements to what has been found in the visual periphery ( e . g . [97] ) . However , the models in [36] , [37] were not examined from the perspective of biological data . In order to address cortical data , our current approach explored the simplest alternative which still permitted formal separation between center and surround: this required an approximation in which multiple surround RFs can coordinate with a center RF as a group , but not individually . This simplification was crucial to make the covariance optimization computationally tractable , but in the simulation results it was counterbalanced by the fact that the covariance allowed for different weightings of the surround ( e . g . Fig . 9 , 10 where the collinear and parallel regions of the surround receive different assignments ) . However , for more complex stimuli than those considered in this paper , it remains to be seen how well the approximation works in capturing neural outputs; furthermore , relaxing the approximation will be necessary when considering e . g . simultaneous responses of populations of neurons . It will be important in future work to explore models of intermediate complexity that would still allow flexibility in the surround assignments . Certain other statistically-minded approaches to computer vision have addressed the implications for biological vision . Schwartz and Simoncelli [30] linked surround divisive normalization to scene statistics by learning divisive weights; however they did not consider linear correlations , nor the concept of stimulus-dependent normalization pools . Karklin and Lewicki [34] also proposed that neural responses encode local statistical variations in the inputs . This led to an impressive set of complex-cell non-linearities , though not explicitly related to divisive normalization , nor covering as wide a range of surround effects . Other computational paradigms of predictive coding [98] modeled divisive surround modulation in depth [35] , but did not address spatial non-homogeneities . Some models ( e . g . [15] , [16] ) explained a large range of perceptual salience effects based on cortical-like center-surround interactions , but these have not reproduced V1 surround modulation in detail . Furthermore , by contrast with the dynamical modeling [14] , [19]–[21] , [75] , [76] , none of the above accounts tied together neural and perceptual salience effects . A natural extension of our approach would be to optimize the sampling of visual features and test how well the model predicts V1 responses to complex scenes; and their effects on perceptually assessable factors such as salience detection under controlled manipulation of the assignments , and gaze patterns on natural images [15] . Recurrent implementations of V1 saliency [75] captured also subtle perceptual phenomena , such as the medial axis effect; these elude our model , and might require more cooperative interactions among the units . More generally , context is pervasive in time as well as space [99]: extending the model to learn statistical regularities in natural movies could provide an underpinning for the temporal characteristics of V1 responses and perceptual reports , such as adaptation and its dual in the form of surprise responses [100] . It remains an open question as to whether these contextual effects collectively represent the zenith of V1 processing , and whether and how the same computational principles apply to V2 and beyond .
|
One of the most important and enduring hypotheses about the way that mammalian brains process sensory information is that they are exquisitely attuned to the statistical structure of the natural world . This allows them to come , over the course of development , to represent inputs in a way that reflects the facets of the environment that were responsible . We focus on the case of information about the local orientation of visual input , a basic level feature for which a wealth of phenomenological observations are available to constrain and validate computational models . We suggest a new account which focuses on the statistics of orientations at nearby locations in visual space , and captures data on how such contextual information modulates both the responses of neurons in the primary visual cortex , and the corresponding psychophysical percepts . Our approach thus helps elucidate the computational and ecological principles underlying contextual processing in early vision; provides a number of predictions that are readily testable with existing experimental approaches; and indicates a possible route for examining whether similar computational principles and operations also support higher-level visual functions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"computer",
"science",
"computational",
"neuroscience",
"synthetic",
"vision",
"systems",
"biology",
"sensory",
"systems",
"neuroscience"
] |
2012
|
Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics
|
In all animals managing the size of individual meals and frequency of feeding is crucial for metabolic homeostasis . In the current study we demonstrate that the noradrenalin analogue octopamine and the cholecystokinin ( CCK ) homologue Drosulfakinin ( Dsk ) function downstream of TfAP-2 and Tiwaz ( Twz ) to control the number of meals in adult flies . Loss of TfAP-2 or Twz in octopaminergic neurons increased the size of individual meals , while overexpression of TfAP-2 significantly decreased meal size and increased feeding frequency . Of note , our study reveals that TfAP-2 and Twz regulate octopamine signaling to initiate feeding; then octopamine , in a negative feedback loop , induces expression of Dsk to inhibit consummatory behavior . Intriguingly , we found that the mouse TfAP-2 and Twz homologues , AP-2β and Kctd15 , co-localize in areas of the brain known to regulate feeding behavior and reward , and a proximity ligation assay ( PLA ) demonstrated that AP-2β and Kctd15 interact directly in a mouse hypothalamus-derived cell line . Finally , we show that in this mouse hypothalamic cell line AP-2β and Kctd15 directly interact with Ube2i , a mouse sumoylation enzyme , and that AP-2β may itself be sumoylated . Our study reveals how two obesity-linked homologues regulate metabolic homeostasis by modulating consummatory behavior .
The human genes TFAP2B ( encoding AP-2β ) and KCTD15 were strongly linked to obesity in multiple genome-wide association studies ( GWAS ) [1]–[4] , though it is still not understood how they regulate obesity . In the fruit fly Drosophila melanogaster , TFAP2B and KCTD15 are both highly conserved , encoded by TfAP-2 and Tiwaz ( Twz ) , respectively [5]–[7] . Recently , it was shown in zebrafish embryos that Kctd15 interacts directly with AP-2α ( Tfap2a ) to inhibit AP-2α function , and in Drosophila there is evidence for an association between TfAP-2 and Twz [6] , [8] , [9] . TfAP-2 and Twz had a strong interaction in a large scale yeast two-hybrid screen using almost the entire Drosophila proteome [9] . Moreover , we have shown in adult males that TfAP-2 and Twz genetically interact to control aggressive behavior by regulating octopamine production and secretion , which in turn regulates the expression of the Drosophila cholecystokinin ( CCK ) homolog Drosulfakinin ( Dsk ) [6] . Interestingly , we demonstrated that overexpression of Dsk was sufficient to induce aggressive behavior in males . CCK , a mammalian gastrointestinal hormone , is secreted by the gut when nutrients enter the lumen . After being released CCK binds to the cholecystokinin A receptor ( CCKAR ) located on vagal sensory terminals , this pathway delivers satiation signals to the nucleus of the solitary tract ( NTS ) to inhibit feeding [10] , [11] . Similar to mammalian CCK , in Drosophila adults Dsk is necessary to inhibit overeating after starvation [12] . Furthermore , it was reported that Dsk is necessary in larvae and adult Drosophila to determine food palatability [12] . More recently , it was determined that octopamine also has an important role in determining the palatability of food [13] . These results led us to ask the following questions: Are TfAP-2 and Twz involved in regulating Drosophila adult feeding behavior ? Does Dsk regulate normal ab lib feeding behavior ? Do octopamine and Dsk interact to regulate feeding in adult flies ? Here , using genetic tools to manipulate their expression , we have investigated the function of TfAP-2 and Twz in the regulation of feeding behavior . Our data suggest that TfAP-2 and Twz control feeding through octopamine signaling . Furthermore , we demonstrate that octopamine and Dsk interact in a negative feedback loop to control the frequency of meals . Moreover , this function may be conserved in mammals , as we discovered that mouse AP-2β and Kctd15 proteins directly interact in a mouse hypothalamic cell line and co-localize in areas of the mouse brain involved in modulating feeding behavior . Finally , we demonstrate that similar to other members of their protein families both AP-2β and Kctd15 interact directly with the sumoylation enzyme Ube2i .
Previously , we demonstrated that TfAP-2 and Twz function in octopaminergic neurons to regulate the expression of the Drosophila cholecystokinin ( CCK ) homologue Drosulfakinin ( Dsk ) , via octopamine signaling [6] . It was reported that Dsk is involved in regulating consummatory behavior [12] , to understand if TfAP-2 and Twz could also be involved in commsumatory behavior we performed qPCR to determine their transcript levels after different dietary regiments . Intriguingly , compared to flies fed ab lib , starving males for 24 h significantly increased TfAP-2 expression ( 1 . 66-fold , SE ±0 . 07 , P<0 . 005 ) ( Figure 1A ) , but not Twz expression . Starving the males for 48 h significantly increased the expression of both genes ( Figure 1A ) . Next , we determined if macronutrient content influenced TfAP-2 and Twz expression . The transcript levels of TfAP-2 and Twz in males fed a normal diet ( 10 g·dl−1∶10 g·dl−1 sucrose∶brewer's yeast S:Y ) were set as 100% , represented as 1 on the graph ( Figure 1B ) . Keeping males for 5 days on a the low calorie diet ( 2 . 5 g·dl−1∶2 . 5 g·dl−1 S:Y ) increased the expression of TfAP-2 , but not significantly ( 1 . 56-fold , SE ±0 . 23 , P = 0 . 056 ) . Feeding males a high calorie diet ( 40 g·dl−1∶40 g·dl−1 S:Y ) or just a high protein diet ( 10 g·dl−1∶40 g·dl−1 S:Y ) significantly reduced the expression of TfAP-2 ( high calorie diet: 0 . 66-fold SE ±0 . 07 , P<0 . 05; high protein diet: 0 . 37-fold , SE ±0 . 05 , P<0 . 005 ) . The high sugar diet ( 40 g·dl−1∶10 g·dl−1 S:Y ) also decreased TfAP-2 expression , but not significantly ( 0 . 60-fold , SE ±1 . 26 , P = 0 . 059 ) ( Figure 1B ) . None of the diets had a considerable effect on Twz expression ( Figure 1B ) . From these results it is evident that TfAP-2 transcript levels are up-regulated under conditions of dietary restriction and down-regulated when flies are fed a high calorie diet; while Twz transcription is only influenced by severe starvation . We already established that Twz was necessary for TfAP-2 expression in octopaminergic neurons ( Text S1 , Figure S1A ) [6] . To understand if Twz was necessary for the increase observed in TfAP-2 expression when flies were starved or raised on a low calorie diet , we knocked down Twz in octopaminergic neurons and performed qPCR analysis . The transcript levels of TfAP-2 in males fed ab lib on a normal diet ( 10 g·dl−1∶10 g·dl−1 S:Y ) was set as 100% , represented as 1 on the graph ( Figure 1C ) . As seen previously , starving control males ( Tdc2-GAL4+/− ) for 24 h significantly increased TfAP-2 expression ( 1 . 72-fold , SE ±0 . 07 , P<0 . 005 ) , whereas keeping them for 5 days on a low calorie diet ( 2 . 5 g·dl−1∶2 . 5 g·dl−1 S:Y ) also increased TfAP-2 levels , but not significantly ( Figure 1C ) . Interestingly , knocking down Twz expression in octopaminergic neurons was sufficient to significantly inhibit TfAP-2 expression in starved flies , as well as flies raised on a low calorie diet ( Figure 1C ) . Previously we have reported that overexpression of TfAP-2 in octopaminergic neurons is sufficient to induce the expression of two gene necessary for octopamine production and secretion , Tyramine β hydroxylase ( Tbh ) and Vesicular monoamine transporter ( Vmat ) [6] . Since starvation had a significant effect on TfAP-2 transcription , to understand if starvation influenced Tbh and Vmat transcriptional levels we performed qPCR analysis on adult males starved for 24 h or 48 h . Intriguingly , compared to flies fed ab lib , starving males for 24 h significantly increased Vmat expression ( 1 . 59-fold , SE ±0 . 11 , P<0 . 05 ) ( Figure 1D ) . By 48 h of starvation Vmat expression was back to control levels . Starving males for 48 h significantly decreased Tbh expression levels ( 0 . 48-fold , SE ±0 . 03 , P<0 . 05 ) compared to controls . Next , we determined if macronutrient content influenced Tbh or Vmat expression . While none of the diets had a considerable effect on Vmat expression , a slight increase in Tbh expression was observed in flies fed a low calorie diet , and a significant decrease was observed in flies fed a high calorie diet ( 0 . 79 , SE ±0 . 05 , P<0 . 05 ) ( Figure 1E ) . From these results it appears that starvation conditions regulate both Tbh and Vmat transcription , but only Tbh is regulated by macronutrient content . The Drosophila CCK homologue Dsk is necessary to limit meal size after starvation [12] , and we saw that both TfAP-2 and Twz transcript levels were increased under starvation conditions ( Figure 1A ) . To discover if TfAP-2 and Twz could be involved in regulating consummatory behavior after starvation we performed a re-feeding assay . Normally flies re-introduced to food after overnight starvation will eat for ∼20 minutes [14] . We allowed starved flies to feed on normal food for 20 minutes before letting them feed for a further 10 minutes on normal food containing blue food dye . Overeaters were determined as those males having blue food in their guts when observed under a stereomicroscope [12] , [14] ( Figure 2A ) . Only 3% ( SE ±2 ) of Tdc2-GAL4+/− control flies ate the colored food , indicating that most control flies were satiated after 20 minutes , while 25% ( SE ±7 . 7 , P<0 . 05 ) of TfAP-2RNAi1 and 22% ( SE ±6 . 5 , P<0 . 05 ) of TfAP-2RNAi2 flies overate; 21% ( SE ±5 . 3 , P<0 . 05 ) of TwzRNAi1 and 19% ( SE ±4 . 8 , P<0 . 05 ) of TwzRNAi2 males overate ( Figure 2B ) . Intriguingly , 50% ( SE ±4 . 8 , P<0 . 005 ) of TfAP-2OE males overate after being starved for 16 h , while only 10% ( SE ±4 . 2 , P<0 . 05 ) of the TfAP-2OE;TwzRNAi1 flies continued to eat beyond the initial 20 minutes ( Figure 2B ) . Similar results were obtained using green food dye; to exclude any possible effects caused by the blue food coloring ( Figure 2C ) . Interestingly , similar to loss of Dsk in the insulin-producing cells , knocking down Tfap-2 or Twz in octopaminergic neurons causes them to overeat after starvation , indicating they may be upstream of Dsk in regulating consummatory behavior . Of note , when Tfap-2 was overexpressed in octopaminergic neurons an even stronger overeating phenotype was observed than with loss of Tfap-2 . To understand if TfAP-2 and Twz were involved in regulating normal consummatory behavior in adults , a CAFE assay was performed to measure how much food flies fed ad lib consumed during a 24 h period [15] . While Tdc2-GAL4+/− control flies ate 270 nl ( SE ±60 . 9 ) , TfAP-2RNAi1 and TfAP2RNAi2 knockdown flies ate significantly more , 511 . 8 nl ( SE ±76 . 7 , P<0 . 005 ) and 499 . 3 nl ( SE ±61 . 1 , P<0 . 05 ) , respectively ( Figure 3A ) . Similar results were obtained when we knocked down Twz ( TwzRNAi1: 460 . 2 nl , SE ±61 . 1 , P<0 . 005; TwzRNAi2: 472 nl , SE ±43 . 3 , P<0 . 005 ) . Of note , overexpressing TfAP-2 specifically in octopaminergic neurons also induced flies to eat significantly more than controls ( 490 nl , SE ±73 , P<0 . 005 ) ( Figure 3A ) . The increase in meal size due to TfAP-2 overexpression was rescued by simultaneously knocking down Twz ( TfAP2OE;TwzRNAi1: 290 nl , SE ±63 . 3 , P = 0 . 86 ) . To observe each genotype's consummatory behavior , individual CAFE assays were videoed , allowing us to determine both the number of feeding bouts ( fb ) and the average meal size . Controls had 9 fb per fly ( SE ±1 . 1 ) over a 24 h period , while flies overexpressing TfAP-2 in octopaminergic neurons had 28 fb ( SE ±5 . 1 , P<0 . 005 ) ( Figure 3B ) . Compared to TfAP-2OE males , TfAP-2OE;TwzRNAi1 males had significantly fewer feeding bouts per fly ( 14 fb , SE ±3 . 2 , P<0 . 005 ) . Meal size was also affected by the various genotypes . Control flies ate on average 29 nl per meal ( SE ±3 . 0 ) , while TfAP-2RNAi1 and TfAP-2RNAi2 knockdown males ate significantly larger meals , 43 nl ( SE ±6 . 0 , P<0 . 05 ) and 48 . 2 nl ( SE ±4 . 5 , P<0 . 05 ) , respectively . TwzRNAi1 and TwzRNAi2 males ate slightly , but not significantly , larger meals ( TwzRNAi1: 36 . 8 nl , SE ±6 . 7 , P = 0 . 3; TwzRNAi2: 35 . 9 nl , SE ±5 . 5 ) ( Figure 3C ) . Overexpression of TfAP-2 in octopaminergic neurons induced a significant reduction in average meal size ( 18 nl , SE ±2 . 0 , P<0 . 005 ) ( Figure 3C ) , which was rescued by simultaneously knocking down Twz ( TfAP2OE;TwzRNAi1: 26 nl , SE ±6 . 0 , P = 0 . 68 ) . The increase in feeding bouts observed when TfAP-2 was overexpressed in octopaminergic neurons might explain overeating after starvation , since overexpression of TfAP-2 induced flies to feed . To determine if TfAP-2 and Twz were regulating feeding through octopamine signaling , we fed flies 3 mM of the octopamine antagonist phentolamine , a concentration known to reduce TfAP-2OE induced hyperactivity to control levels , while not reducing the activity of controls [6] . A CAFE assay was performed to determine the total food intake and the average number of feeding bouts per fly over 24 h . While Tdc2-GAL4+/− and TfAP-2OE+/− control flies ate 246 . 4 nl ( SE ±22 . 2 ) and 246 . 8 nl ( SE ±32 . 9 ) respectively , similar to before , overexpressing TfAP-2 in octopaminergic neurons significantly increased the total food intake to 548 . 8 nl ( SE ±66 . 3 , P<0 . 005 ) . Feeding 3 mM phentolamine had no significant affect on the total food intake of control flies , but significantly reduced the total intake of TfAP-2OE males to control levels ( 312 . 5 nl , SE ±34 . 7 , P = 0 . 573 ) ( Figure 4A ) . Overexpressing TfAP-2 in octopaminergic neurons significantly increased the number of feeding bouts ( 33 . 3 fb , SE ±6 . 6 , P<0 . 005 ) , while TfAP-2OE flies fed 3 mM phentolamine only had 13 . 9 fb ( SE ±2 . 3 , P = 0 . 642 compared to Tdc2-GAL4+/− controls ) ( Figure 4B ) . Interestingly , feeding control flies 3 mM phentolamine had no significant affect on the number of feeding bouts . Finally , in order to determine if octopamine had a direct effect on consummatory behavior , we expressed a UAS-transgene for the voltage-activated , bacterial sodium channel NaChBac [16] in octopaminergic neurons using the Tdc2-GAL4 driver and then performed a CAFE assay as before . Of notable interest , overexpressing NaChBac in octopaminergic neurons significantly increased total food intake over a 24 h period ( 501 . 1 nl , SE ±16 . 9 , P<0 . 005 ) compared to the Tdc2-GAL4+/− control males ( 252 . 3 nl , SE ±18 . 9 ) ( Figure 4C ) . We noticed that flies usually feed within the first 3 h after lights on , so we videoed the flies during this time and counted the number of feeding bouts . Overexpressing NaChBac in octopaminergic neurons also significantly increased the number of feeding bouts over a 3 h period ( 2 . 36 fb , SE ±0 . 43 , P<0 . 05 ) compared to the Tdc2-GAL4+/− control males ( 0 . 44 feeding bouts , SE ±0 . 14 ) ( Figure 4D ) . In the CAFE assay the UAS-NaChBac control line increased both total food intake and feeding bouts , indicating that this line has low levels of expression even without the GAL4 driver . These results indicate that TfAP-2 induced feeding behavior requires octopamine signaling . Furthermore , increasing octopamine signaling is sufficient to induce consummatory behavior in adult males . Next to ascertain if octopamine signaling was directly influencing Dsk transcript levels we expressed the voltage-activated , bacterial sodium channel NaChBac [16] in octopaminergic neurons using the Tdc2-GAL4 driver and performed qPCR to observe Dsk levels ( Figure 4E ) . The Dsk transcript levels of Tdc2-GAL4+/− males was set as 100% , represented as 1 on the graph . Compared to Tdc2-GAL4+/− controls ( SE ±0 . 06 ) , males where NaChBac was overexpressed in octopaminergic neurons had significantly more Dsk transcript ( 1 . 75-fold , SE ±0 . 04 , P<0 . 005 ) ( Figure 4E ) . From this result we conclude that activating octopaminergic neurons is sufficient to induce Dsk expression in the CNS . Overexpression of Dsk in the insulin-producing cells ( IPCs ) is sufficient to mimic TfAP-2OE induced male aggression phenotypes [6] . To discover if overexpression of Dsk could mimic TfAP-2OE feeding phenotypes we performed a CAFE assay on flies fed ab lib . Dsk was overexpressed in the IPCs ( Dilp2-GAL4 ) , as well as pan-neuronally ( elav-GAL4 ) , and the feeding behavior phenotype was compared to males where TfAP-2 was either knocked down ( TfAP-2RNAi ) or overexpressed ( TfAP-2OE ) in octopaminergic neurons . Similar to what was observed before knocking down or overexpressing TfAP-2 increased total food intake . However , overexpressing Dsk pan-neuronally or specifically in the IPCs had no affect on the amount of food adult males consumed ( Figure 5A ) . Also , flies overexpressing Dsk in either the IPCs ( 11 . 2 fb , SE ±2 . 3 , P = 0 . 93 ) or pan-neuronally ( 6 . 2 fb , SE ±1 . 4 , P = 0 . 23 ) had no significant change in the number of feeding bouts , when compared to controls ( 8 . 7 fb , SE ±1 . 4 ) ( Figure 5B ) . Unlike TfAP-2OE males , which had an average meal size of 18 . 4 nl ( SE ±1 . 7 , P<0 . 005 ) , control flies ate on average 28 . 5 nl per meal ( SE ±3 . 1 ) and males overexpressing Dsk in IPCs or pan-neuronally ate normal size meals , 32 . 1 nl ( SE ±4 . 8 , P = 0 . 56 ) and 37 . 8 nl ( SE ±5 . 4 , P = 0 . 16 ) , respectively ( Figure 5C ) . Starvation had a significant effect on TfAP-2 transcription and to a lesser degree on Twz expression ( Figure 1A ) . To understand if starvation influenced Dsk transcriptional levels we performed qPCR analysis on adult males starved for 24 h or 48 h . Compared to flies fed ab lib , starving males for 24 h significantly increased Dsk expression ( 1 . 42-fold , SE ±0 . 10 , P<0 . 05 compared to controls ) , yet after 48 h of starvation Dsk expression was back to control levels ( Figure 5D ) . Finally , we determined if macronutrient content influenced Dsk expression , but none of the diets had a considerable affect , though a slight increase in Dsk expression was observed in flies fed a low calorie diet , and a decrease in expression was observed in flies fed a high proteins diet ( Figure 5E ) . From these results is appears dietary conditions regulate Dsk transcription in a manner similar to Vmat . Overexpressing Dsk pan-neuronally ( elav-GAL4 ) induced a slight , albeit not significant , decrease in the number of feeding bouts compared to controls ( Figure 5B ) . This , along with the earlier study that determined loss of Dsk caused starved flies to overeat , could mean Dsk actually signals to inhibit feeding behavior [12] . In order to understand if TfAP-2 was regulating feeding behavior through Dsk in a negative feedback loop , we fed control ( Tdc2-GAL4+/− ) and TfAP-2OE males the mammalian CCK antagonist SR 27897 before performing a CAFE assay [17] , [18] . The CCK antagonist significantly increased total food intake in adult males ( 507 . 3 nl , SE ±68 . 2 , P<0 . 005 ) , compared to controls fed normal food ( 124 . 7 nl , SE ±55 . 4 ) . Similar to what was observed previously , TfAP-2OE males ate significantly more food ( 258 . 3 nl , SE ±1 . 4 , P<0 . 005 ) than controls ( Figure 6A ) . Feeding these males the CCK antagonist had an additive effect , causing a drastic increase in total food intake ( 786 . 1 nl , SE ±88 , P<0 . 005 ) . The CCK antagonist also significantly increased the number of feeding bouts in Tdc-GAL4+/− controls ( 38 . 6 fb , SE ±5 . 2 , P<0 . 005 ) , which was more similar to TfAP-2OE males ( 28 fb , SE ±4 . 7 , P<0 . 005 compared to controls ) and significantly higher than controls ( 8 . 7 fb , SE ±1 . 4 ) ( Figure 6B ) . Feeding the antagonist to TfAP-2OE males increased the number of feeding bouts even further to 76 ( SE ±7 . 9 , P<0 . 005 ) . The antagonist SR 27897 had no effect on the meal size of either control or TfAP-2OE males ( Figure 6C ) . To verify Dsk function in feeding behavior , as well as determine if the mammalian CCK antagonist SR 27897 was actually inhibiting Drosophila Dsk signaling , we fed flies 1 mM SR 27897 where Dsk was knocked down in either the IPCs using the Dilp2-GAL4 driver or in all Dsk expressing cells using the Dsk-GAL4 driver [19] , [20] and performed a CAFE assay . Dilp2-GAL4 , Dsk-GAL4 and UAS-DskRNAi heterozygous controls ate 137 . 5 nl ( SE ±12 . 3 ) , 125 . 5 nl ( SE ±15 . 3 ) and 187 . 5 nl ( SE ±15 ) respectively , feeding these controls 1 mM SR 27897 significantly increased their total food intake over a 24 h period ( Dilp2-GAL4 , 232 . 8 nl , SE±18 . 7 , P<0 . 05; Dsk-GAL4 , 254 . 2 nl , SE ±20 . 3 , P<0 . 05; UAS-DskRNAi , 292 . 9 nl , SE ±23 . 4 , P<0 . 05 ) . Knocking down Dsk in the IPCs ( Dilp2-GAL4 ) or in all Dsk expressing cells ( Dsk-GAL4 ) significantly increased the total food intake over a 24 h period , but feeding these flies the CCK antagonist SR 27897 did not further increase total intake ( Figure 6D ) . The heterozygous controls Dilp2-GAL4 , Dsk-GAL4 and UAS-DskRNAi had 10 ( SE ±1 . 1 ) , 11 . 8 ( SE ±1 . 8 ) and 15 ( SE ±1 . 4 ) feeding bouts over a 24 h period , respectively ( Figure 6E ) . Feeding these controls 1 mM SR 27897 significantly increased the number of feeding bouts ( Dilp2-GAL4+/−: 24 . 4 fb , SE ± 2 . 1 , P<0 . 005; Dsk-GAL4+/−: 29 . 2 fb , SE ±3 . 4 , P<0 . 005; UAS-DskRNAi+/−: 28 . 1 , SE ±1 . 6 , P<0 . 005 ) . Knocking down Dsk in the IPCs or in all Dsk expressing cells significantly increased the number of feeding bouts ( Dilp2-GAL4; DskRNAi: 32 fb , SE ±3 . 6 , P<0 . 005; Dsk-GAL4;DskRNAi: 30 . 3 fb , SE ±3 . 4 , P<0 . 005 ) , but feeding these flies the CCK antagonist did not further increase feeding bouts ( Dilp2-GAL4;DskRNAi: 32 . 7 fb , SE ±3 . 2 , P = 0 . 885; Dsk-GAL4;DskRNAi: 33 . 8 fb , SE ±3 . 8 , P<0 . 861 ) ( Figure 6E ) . From the above data we present the following model: TfAP-2 and Twz regulate the production and secretion of octopamine , which in turn initiates feeding , while at the same time , in a negative feedback loop , octopamine induces the expression of Dsk to inhibit feeding frequency ( Figure 6F ) . To ascertain if , similar to Drosophila , the mammalian Tfap2b ( encoding AP-2β ) and Kctd15 genes could be involved in regulating feeding behavior , immunohistochemistry was performed to investigate if AP-2β and Kctd15 co-localized in the mouse brain , and known markers for neurons and glial cells were used to investigate in what cell type they were expressed , see Figure 7 . AP-2β and Kctd15 had similar expression patterns with high and exclusive expression in parts of cerebral cortex , cerebellum and hypothalamus . Overlapping expression was seen for AP-2β and Kctd15 in the arcuate hypothalamic nucleus ( Arc ) and the ventromedial hypothalamic nucleus ( VMH ) ( Figure 7A ) . Co-localization of AP-2β and Kctd15 immunoreactivity was also seen in the core of the accumbens nucleus ( AcbC ) in ventral striatum ( Figure 7B ) . These are all areas that are known to be involved in the regulation of food intake [21] , [22] . The neuron-specific enolase ( NSE ) was used to visualize the neuronal expression of AP-2β and Kctd15 . Interestingly , overlapping expression was seen for AP-2β and the neuronal marker NSE [23] in cerebral cortex ( Figure 7C ) . Kctd15 showed a similar pattern with strong co-localization with NSE in cerebral cortex ( Figure 7D ) . The expression of the astrocyte marker glial fibrillary acidic protein ( GFAP ) [24] did not overlap with neither AP-2β nor Kctd15 in the brain ( Figure 7E and 7F , respectively ) . To conclude , AP-2β and Kctd15 immunoreactivity co-localize in specific areas of the mouse brain and are localized to neurons . To study how the dietary status influenced the mRNA expression of Kctd15 and Tfap2b in the hypothalamus , mice were assigned to different food restrictions; 1 ) fed normal chow- , 2 ) fed normal chow , but food-deprived for 24 h- and 3 ) fed high fat diet to induce obesity before analyses . The expression levels of Tfap2b and Kctd15 in normal chow fed mice were sat at 100% , represented as 1 in the graphs ( Figure 8A and 8B ) . Both genes were up-regulated by fasting , but only Tfap2b was significantly changed , with relative expression at 3 . 22 ( SD ±0 . 50 , P>0 . 05 ) for Tfap2b and 1 . 38 ( SD ±0 . 06 ) for Kctd15 ( Figure 8A ) . Further , Tfap2b was also affected by the high-fat diet with up-regulated relative expression levels at 4 . 78 ( SD ±0 . 67 , P>0 . 01 ) , while Kctd15 was unaffected by obesity ( 0 . 99±0 . 09 ( ±SD ) ( Figure 8B ) . Mice fed a high-fat diet for 6–8 weeks had significantly higher weight than mice fed normal chow ( Figure 8C ) . Hence dietary status affects the expression of Tfap2b and to a much lesser extent Kctd15 , in the hypothalamus in mice , this is similar to what was observed for the Drosophila homologues TfAP-2 and Twz ( Figure 1A and 1B ) To ascertain if AP-2β and Kctd15 can directly interact in vivo , a proximity ligation assay ( PLA ) was performed using mHypoE-N25/2 cells . PLA can readily detect and localize proteins with single molecule resolution , allowing for the determination of directly interacting proteins [25] . Using this method , PLA signals were observed evenly distributed throughout the mHypoE-N25/2 cells , where both AP-2β and Kctd15 primary antibodies were added ( Figure 9A , red dots ) , while no signals were observed in the control cells lacking primary antibodies ( Figure 9A1 ) . All cells were counter stained with DAPI to highlight the nuclei ( blue staining ) . In mice AP-2β interacts with the sumoylation enzyme Ube2i [26] and in Drosophila Twz interacts directly with the Drosophila Ube2i homologue Lesswright ( Lwr ) [9] . Furthermore , mouse AP2γ is sumoylated [26] and we have evidence that mouse AP-2β is also sumoylated ( Figure 9D ) . Western blot analysis of mouse proteins recovered from the entire brain was performed using an anti-AP-2β antibody . Two bands were visible on the gel , the lower band corresponded to the predicted size of AP-2β ( 48 kDa ) , the upper band corresponded to sumoylated AP-2β ( ∼60 kDa ) ( Figure 9D ) . This result led us to perform a proximity ligation assay ( PLA ) using mHypoE-N25/2 cells to understand if Kctd15 and AP-2β directly interact with Ube2i . Using this method PLA signals were observed in punctate points throughout the mHypoE-N25/2 cells , when either AP-2β or Kctd15 primary antibodies were added together with Ube2i antibodies ( Figure 9B and 9C respectively , red dots ) , while no signals were observed in cells lacking primary antibodies ( Figure 9B1 and 9C1 respectively ) .
Previously we have shown that the obesity-linked homologues TfAP-2 and Twz interact genetically to positively regulate TfAP-2 activity [6] . This interaction could be regulated by orexigenic signals , including starvation or low macronutrient meal content ( see figures 1A–C ) . In order to increase octopamine signaling , TfAP-2 overexpression induces the expression of genes involved in producing and secreting octopamine , for example Tyramine β hydroxylase ( Tbh ) and Vesicular monoamine transporter ( Vmat ) [6] . Octopamine signaling initiates consummatory behavior , and in a negative feedback loop to prevent overeating , signals to induce the expression of Dsk; the Dsk anorexigenic signal then inhibits feeding . Intriguingly , we demonstrate that loss of TfAP-2 or overexpression of TfAP-2 in octopaminergic neurons induces an overeating phenotype , but by different mechanisms . Knocking down TfAP-2 caused flies to eat larger meals , but did not influence the number of meals per day; while ovexpressing TfAP-2 significantly increased the number of meals taken per day . The overexpression phenotype can be explained by our model ( see figure 6F ) . Increased octopamine signaling would induce flies to eat , but at the same time increase Dsk signaling to inhibit feeding . This feedback loop would lead to an increase number of feeding bouts , due to octopamine signaling , but premature cessation of food intake due to increased Dsk expression . The increased meal size due to loss of TfAP-2 is harder to explain , but points to parallel pathways involved on regulating meal size . It was recently reported that similar to Dsk , another neuropeptide Crustacean cardioactive peptide ( Ccap ) , Drosophila vasopressin/oxytocin homologue , is also expressed in the insulin-producing cells [27] . Intriguingly we have evidence that Ccap is involved regulating meal size in adult Drosophila ( M . Williams , unpublished observations ) , both vasopressin and oxytocin have been shown to regulate meal size in mammals [28]–[30] A recent paper demonstrated that the Drosophila noradrenalin analogue octopamine was involved in regulating consummatory behavior in larvae [13] . In that particular study it was determined that octopamine drives the response for continual eating of palatable food , unlike the NPY-like system that was shown to initiate eating of less palatable food under adverse conditions [31] , [32] . Of note , chronic infusion of the mammalian octopamine analogue noradrenalin into the ventromedial hypothalamic nucleus ( VMH ) of rats induces obesity , most likely due to hyperphagia and increased levels of circulating insulin and triglycerides [33] , [34] . Furthermore , dietary amino acid deficiency inhibited noradrenalin release within the VMH , probably stimulated by an aversion response to inhibit consummatory behavior [35] . Interestingly , we found that in the Drosophila CNS or mouse hypothalamus starvation and low macronutrient meal content induced the expression of TfAP-2 ( see figure 1A and 1B ) or Tfap2b ( see figure 8A and 8B ) . Furthermore , when we stained mouse brains to look for possible Tfap2b ( encoding AP-2β ) and Kctd15 interactions , we observed a strong co-localization of AP-2β and Kctd15 within neurons located in the VMH . Moreover , overexpressing TfAP-2 in Drosophila octopaminergic neurons induces hyperphagia , which could be rescued by an octopamine antagonist . Finally , hyperactivating octopaminergic neurons is sufficient to induce hyperphagia in adult male Drosophila . These findings suggest that in flies and mammals the initiation and cessation of consummatory behavior is controlled by a conserved signaling system . CCK , a mammalian gastrointestinal hormone secreted by the gut when nutrients enter the lumen , binds to the cholecystokinin A receptor ( CCKAR ) located on vagus sensory terminals . The vagus nerve then delivers satiation signals to the nucleus of the solitary tract ( NTS ) [10] , [11] . Under some experimental conditions exogenous CCK signaling elicits satiety and reduces meal size in several species [36]–[41] . Interestingly , our experiments show that although feeding wild-type flies a CCK antagonist has no affect on meal size , there is a significant increase in the number of feeding bouts ( see figure 6B ) . Moreover , feeding TfAP-2 overexpressing males , which already undergo significantly more feeding bouts than controls , a CCK antagonist had an additive effect . Finally , knocking down the Drosophila CCK homologue in the insulin-producing cells ( IPCs ) was sufficient to induce hyperphagia ( see figure 6D and 6E ) . This differs somewhat to what is observed in mammals , where inhibition of CCK increases meal size . Although there have been no studies reporting the effect of noradrenalin on CCK transcription or signaling , it has been shown that CCK signaling to the VMH can inhibit noradrenalin release [42] . It could be that CCK released from the gut inhibits meal size , while CCK functions within the hypothalamus to block noradrenalin release , thus inhibiting the number of feeding bouts . Previously , we reported that overexpression of TfAP-2 or feeding Drosophila the octopamine analogue chlordimeform is sufficient to induce Dsk transcription , and that Dsk induction by TfAP-2 overexpression could be blocked by feeding these flies an octopamine antagonist [6] . Here we demonstrate that specifically activating octopaminergic neurons using a bacterial sodium channel is sufficient to induce hyperphagia ( see figure 4C and 4D ) . Also , a recent study demonstrated that octopamine signaling induced fervent feeding behavior when larvae were presented with palatable food [13] . Furthermore , it was shown that Dsk is necessary to inhibit well-fed larvae or adults from eating less palatable foods [12] . In both flies and mammals it is possible that there exists a conserved negative feedback loop to inhibit overeating , as well as allow for the differentiation between palatable or non-palatable foods . Our results indicate that there is an epistatic interaction between TfAP-2 and Twz . In all the assays performed , where TfAP-2 was overexpressed , loss of Twz was able to rescue the TfAP-2 overexpression phenotypes . Also , we have previously reported , and again shown here that Twz is necessary for proper TfAP-2 transcription [6] . Furthermore , in mHypoE-N25/2 cells , which are derived from the mouse hypothalamus , a proximity ligation assay showed that throughout the cytoplasm mouse AP-2β and Kctd15 interact directly ( see figure 9A ) . The question is what is the function of this interaction ? In a yeast two-hybrid screen , using a human brain cDNA library , KCTD1 was identified as a binding partner for AP-2α , a paralogous protein to AP-2β . Transient transfection assays , using AP-2-binding site containing promoters , established that KCTD1 actively repressed AP-2α-mediated transactivation , demonstrating that the function of KCTD1 was to inhibit TFAP2α activity . This is not what we observed for Drosophila TfAP-2 and Twz , where Twz was required for TfAP-2 activity . Mouse AP-2β interacts with Ube2i [26] , a sumoylation enzyme , and in Drosophila Twz interacted directly with the Drosophila Ube2i homologue Lesswright ( Lwr ) in a large-scale yeast two-hybrid screen [9] . We show that in mHypoE-N25/2 cells mouse AP-2β and Kctd15 , both directly interact with the sumoylation enzyme Ube2i . It is possible that Twz/Kctd15 acts like a scaffold where TfAP-2/AP-2β is either sumoylated or ubiquitinated , as many KCTD family members also interact with the ubiquitination apparatus [43] , [44] . This post-translational modification might be required for TfAP-2/AP-2β activation . In both Drosophila CNS and mouse hypothalamus TfAP-2 and Tfap2b transcription levels went up after starvation . In Drosophila this increase in transcription required Twz . It could be that satiation levels regulate TfAP-2/Tfap2b and Twz/Kctd15 interactions , which in turn regulates TfAP-2/Tfap2b post-translational modification to increase TfAP-2/Tfap2b activity . During starvation or in conditions of dietary restriction TfAP-2 and Twz could interact to activate TfAP-2 and thus induce TfAP-2 expression ( see figure 1A and 1C ) . Furthermore under extreme conditions , more Twz may be needed , this is supported by the fact that Twz was transcriptionally induced only after 48 h of starvation . We have demonstrated previously that Twz expression does not require TfAP-2 [6] . Although we have not demonstrated it , in the mouse hypothalamus under low nutritive conditions Tfap2b and Kctd15 could interact to initiate sumoylation and activation of Tfap2b , which could in turn induce consummatory behavior . In conclusion , our data suggests that the human obesity-linked genes TFAP2B and KCTD15 could directly interact in regions of the brain known to regulate feeding behavior . We demonstrated that not only do the Drosophila homologues genetically interact , but in a mouse hypothalamic cell line the mouse homologues physically interact . Furthermore , AP-2β and Kctd15 co-localize in regions of the mouse hypothalamus known to regulate feeding behavior . In this model Kctd15 could act like a scaffold where AP-2β would bind to be either sumoylated , in a fashion similar to the Kctd1 and AP-2α , or possibly ubiquitinated . This post-translational modification would then change AP-2β function and allow it to induce noradrenalin signaling to induce consummatory behavior .
w* , P{w[+mW . hs] = GawB}elav[C155] , w*; P{w[+mC] = Tdc2-GAL4 . C}2 , y1 w[*]; P{w[+mC] = UAS-AP-2 . PB}a4-2 , y1 w*; P{w[+mC] = UAS-NaChBac-EGFP}4 and the RNAi lines y1 v1; P{TRiP . JF01908}attP2 Dsk , y1 v1; P{TRiP . JF03500}attP2 Tfap-2 ( referred to as TfAP2RNAi2 ) and y1 v1; P{TRiP . JF01867}attP2 Twz ( referred to as TwzRNAi2 ) were received from the Bloomington Stock Center ( Table 1 ) . TfAP-2 ( y1w3; P{KK109052}VIE-260B , referred to as TfAP-2RNAi1 ) and Twz ( y1w3; P{KK107922}VIE-260B , referred to as TwzRNAi1 ) RNAi flies were obtained from the Vienna Drosophila RNAi Centre ( VDRC , Vienna , Austria ) ( Table 1 ) . w; Dilp2-GAL4 was a gift from Dr . Eric Rulifson [45]; w; Dsk-GAL4 and w; UAS-Dsk line were a gift from Dr . Barry Ganetzky [19] , [20] . All flies , unless otherwise stated , were maintained on enriched Jazz mix standard fly food ( Fisher Scientific ) . Flies were maintained at 25°C in an incubator at 60% humidity on a 12∶12 light∶dark cycle . Flies crossed to GAL4 drivers and controls were raised at 18°C until the adults emerged; once collected adults were raised at 29°C for the appropriate times . In all assays , the GAL4 drivers and UAS transgenic flies were crossed to w1118 flies and their F1 progeny used as controls . This method was modified from Ja et al . 2007 [15] . A vial , 9 cm by 2 cm ( height×diameter ) , containing 1% agarose ( 5 cm high ) to provide moisture and humidity for the flies , was used for this assay . A calibrated capillary glass tube ( 5 µl , VWR International ) was filled with liquid food which contains 5% sucrose , 5% yeast extract and 0 . 5% food-coloring dye . Mineral oil was used to prevent the liquid food from evaporating . The vial was covered with paraffin; a capillary tube was inserted from the top through the paraffin . The experimental set up was kept at 25°C and activity was recorded for 24 h using a HD camera ( Panasonic SDS90 ) . The initial and final food level in the capillary tube was marked to determine total food intake . Number of feeding bouts per fly was counted from the recording; average meal size was calculated by dividing the total food intake by the number of feeding bouts . Five 5–7 day old males per vial were used for this assay . Twenty 5–7 day old males were maintained in a vial containing 1% agarose for 16 h . They were then transferred to normal food vials ( 5% sucrose , 5% yeast extract and 1% agarose ) and allowed to feed for 20 minutes , then transferred to a second food vial containing normal food ( 5% sucrose , 5% yeast extract and 1% agarose ) and 2% blue or green food-coloring dye ( Dr . Oetker ) and allowed to feed for 15 minutes . After this time the abdomen of each fly was observed using dissecting microscope ( DV4 , Zeiss ) and the color of the gut was scored . The percentage of overeaters was calculated by dividing those with blue colored guts by the total number of flies observed . Newly eclosed TfAP-2 overexpressing male flies were collected and isolated on normal food for 3 days . After this time they were fed by CAFE assay method [15] . Calibrated capillary glass tubes ( 5 µl , VWR ) were filled with either 1 mM SR 27897 [17] , [18] or 3 mM of Phentolamine [46] , prepared in liquid food ( 5% of sucrose and 5% yeast extract ) , a layer of mineral oil was used to prevent the liquid food evaporation from the capillary tube . These tubes were inserted from the top through paraffin film into the chambers . After 2–3 days of feeding the flies were used for the various assays performed . The phenol-chloroform method was used for RNA extraction from tissue samples [47] . Fifty fly heads were homogenized with 800 µl TRIzol ( Invitrogen , USA ) , 200 µl Chloroform ( Sigma-Aldrich ) was added and samples were centrifuged at 12000 rpm for 15 minutes at 4°C . The aqueous layer , which contained RNA , was separated and 500 µl isopropanol ( Solvaco AB , Sweden ) was added . The RNA was precipitated by storing the samples at −32°C for 2 h . Samples were centrifuged at 12000 rpm for 10 minutes at 4°C , to collect the RNA pellets , which were then washed with 75% ethanol ( Solvaco AB , Sweden ) to remove the organic impurities . Samples were allowed to air dry to remove any traces of ethanol . Dried RNA pellets were dissolved in 21 . 4 µl of RNAse free water ( Qiagen GmBH , Germany ) and 2 . 6 µl of DNAse incubation buffer ( Roche GmBH , Germany ) . The samples were incubated at 75°C for 15 minutes to ensure complete dissolution of RNA-pellets . 2 µl of DNAse I ( 10 U/µl , Roche GmBH , Germany ) was added to each sample , and incubated at 37°C for 3 hr to remove DNA contamination . DNAse was deactivated by incubating the samples at 75°C for 15 minutes . Removal of DNA was confirmed by PCR using Taq polymerase ( 5 U/µl , Biotools B & M Labs , Spain ) , followed by agarose gel electrophoresis . The RNA concentration was measured using a nanodrop ND 1000 spectrophotometer ( Saveen Werner ) . cDNA was synthesized from RNA template using dNTP 20 mM ( Fermentas Life Science ) , random hexamer primers and M-MLV Reverse Transcriptase ( 200 U/µl , Invitrogen , USA ) by following manufactures instructions . cDNA synthesis was confirmed by PCR followed by agarose gel electrophoresis . Relative expression levels of three housekeeping genes ( EF-1 , Rp49 & RpL11 ) and of the genes of interest were determined with quantitative RT-PCR ( qPCR ) . Each reaction , with a total volume of 20 µl , contained 20 mM Tris/HCl pH 9 . 0 , 50 mM KCl , 4 mM MgCl2 , 0 . 2 mM dNTP , DMSO ( 1∶20 ) and SYBR Green ( 1∶50000 ) . Template concentration was 5 ng/µl and the concentration of each primer was 2 pmol/µl . Primers were designed with Beacon Designer ( Premier Biosoft ) using the SYBR Green settings . All qPCR experiments were performed in duplicates; for each primer pair a negative control with water and a positive control with 5 ng/µl of genomic DNA were included on each plate . Amplifications were performed with 0 . 02 µg/ml Taq DNA polymerase ( Biotools , Sweden ) under the following conditions: initial denaturation at 95°C for 3 min , 50 cycles of denaturing at 95°C for 15 sec , annealing at 52 . 8–60 . 1°C for 15 sec and extension at 72°C for 30 sec . Analysis of qPCR data was performed using MyIQ 1 . 0 software ( Bio-Rad ) as previously reported [48] . Primer efficiencies were calculated using LinRegPCR [49] and samples were corrected for differences in primer efficiencies . The GeNorm protocol described by Vandesompele et al . [50] was used to calculate normalization factors from the expression levels of the housekeeping genes . Grubbs' test was performed to remove outliers . Differences in gene expression between groups were analyzed with ANOVA followed by Fisher's PLSD test where appropriate . P<0 . 05 was used as the criterion of statistical significance . The following primers were used: EF-1 F: 5′-GCGTGGGTTTGTGATCAGTT-3′ , R: 5′-GATCTTCTCCTTGCCCATCC-3′; Rp49 F: 5′-CACACCAAATCTTACAAAATGTGTGA-3′ , R: 5′-AATCCGGCCTTGCACATG-3′; RpL11 F: 5′-CCATCGGTATCTATGGTCTGGA-3′ , R: 5′-CATCGTATTTCTGCTGGAACCA-3′ , TfAP-2 F: 5′-CTAAGAGCAAGAACGGAG-3′ , R: 5′-AACCAAGGATGTCAGTAG-3′; Tiwaz F: 5′-GCCACATTCTGAACTTTATG-3′ , R: 5′-CACCAAATAGTTGCCATT-3′; Dsk F: 5′-CCGATCCCAGCGCAGACGAC-3′ , R: 5′-TGGCACTCTGCGACCGAAGC-3′ The immortalized embryonic mouse hypothalamus cell line N 25/2 were grown ( Detailed methods on cell culture maintenance can be found in SI Experimental procedures ) on glass slides ( coated with 10 µg/ml poly-L-lycine ) were fixed in 4% paraformaldehyde ( Sigma-Aldrich , USA ) for 15 min and rinsed with PBS . The Duolink II fluorescence kit ( orange detection reagents , Olink Biosciences , Sweden ) was used to run in situ proximity assay ( PLA ) on the fixed cells according to manufacturer's instruction . Primary rabbit-anti-AP2β , mouse-anti-KCTD15 , goat-anti-UBC9 antibodies ( Abcam , England ) were diluted 1∶100 , 1∶100 , 1∶200 correspondently in Antibody Diluent supplied by the kit . Negative controls were run without primary antibodies . Protein interactions were detected with PLA probes in combination anti-rabbit PLUS and anti-mouse MINUS , anti-rabbit PLUS and anti-goat MINUS or anti-mouse PLUS and anti-goat MINUS . Slides were mounted using Duolink in situ Mounting Medium with DAPI . Pictures were taken using a fluorescent microscope ( Zeiss Axioplan2 ) connected to a camera ( AxioCam HRm ) with the Carl Zeiss AxioVision version 4 . 7 software ( Carl Zeiss , Germany ) . The immortalized embryonic mouse hypothalamus cell line N 25/2 ( mHypoE-N25/2 , Cellutions Biosystems Inc . , Canada ) was cultured in DMEM cell culture medium ( Invitrogen ) with 10% Fetal Bovine Serum ( Invitrogen ) , 1% Penicillin Streptomycin ( Invitrogen ) , 1× Amphotericin B ( Invitrogen ) in a humidified atmosphere with 5% CO2 in air at 37°C . Growth medium was changed every 2 days and cells were split with trypsin regularly with a ratio of 1∶2–1∶3 usually every 3 days . We performed a western blot analysis of AP-2β in brain tissue from adult , male C57Bl6/J mice ( Taconic M&B , Denmark ) . Briefly , the tissue was homogenized in homogenization buffer ( 50 mM Tris , 150 mM NaCl , 4 mM MgCl , 0 . 5 mM EDTA , 2% Triton X-100 and 1 mM Protease inhibitor PMSF ( Sigma-Aldrich , USA ) diluted in isopropanol ) . Protein concentrations were determined by protein assay DC ( Bio-Rad , Hercules , USA ) according to the manufacturer's instructions . Equal amounts of protein ( 200 µg or 13 µg/µl ) were separated , together with PageRuler prestained protein ladder ( Fermentas , Canada ) , on a Mini-Protean TGX gel ( 4–10% , Bio-Rad , Hercules , USA ) in running buffer ( 0 . 1% SDS , 0 . 025 Tris base and 0 . 192 M glycine ) by gel electrophoresis . The proteins were transferred to a Immobilon-P polyvinylidene fluoride ( PVDF ) membrane ( Millipore , Billerica , USA ) in transfer buffer ( 0 . 025 Tris base , 0 . 192 M glycine and 20% methanol ) and pre-blocked for 1 h in blocking buffer ( 5% non-fat dry milk ( Bio-RAD , Hercules , USA ) diluted in 1 . 5 M NaCl , 0 . 1 M Tris , 0 . 05% Tween-20 , pH 8 . 0 ) . The membrane was cut in the middle , giving two membranes with equally loaded protein samples . One half of the membrane was hybridized with the primary antibody against AP-2β ( diluted 1∶200 , rabbit-anti-AP2β , Abcam , England ) . The other half of the membrane was hybridized with AP-2β primary antibody that was pre-blocked with excess of the same synthetic peptide ( sequence ( NH2- ) MHSPPRDQAA IMLWKLVENV KYEDIYEDRH DGVPSHSSRL SQLGSVSQGP ( -CONH2 ) , Abcam , England ) that was used to generate the antibody . The hybridization was then performed overnight at 4°C . After washes in water , the membranes were incubated for 1 h with horseradish peroxidase conjugated secondary antibody ( diluted 1∶10000 , goat-anti-rabbit , Invitrogen , USA ) followed by detection with the enhanced chemiluminescent ( ECL ) method . The membranes were incubated for 3 min in a 1∶1 mixture of luminol/enhancer and peroxidase buffer solutions ( Immun-Star HRP , Bio-Rad , Hercules , USA ) and developed on High performance chemiluminescence film ( GE healthcare , Waukesha , USA ) . Mean and standard error from all replicates of each experiment was calculated . All analysis was performed with GraphPad Prism 4 , and used ANOVA with appropriate post hoc analysis for multiple comparisons . The type of analysis performed for each assay is specified in the appropriate Figure legend .
|
The size of individual meals and feeding frequency are important for homeostatic control . Due to the complex neuroendocrine system regulating human food intake it is difficult to uncover the mechanisms underlying eating disorders . The genetically tractable model system Drosophila melanogaster has a comparatively simple brain; yet , similar to humans , its eating behavior can adapt to respond to nutritional needs . Our study describes how the obesity-linked homologues TfAP-2 ( human TFAP2B ) and Tiwaz ( human KCTD15 ) regulate a unique feedback system involving noradrenalin-like octopamine and the CCK homolog Dsk , that exert positive and negative effects on Drosophila feeding behavior . Our findings provide insight into how two conserved obesity-linked genes regulate feeding behavior in order to maintain metabolic balance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"arthropoda",
"animal",
"models",
"invertebrates",
"behavioral",
"neuroscience",
"drosophila",
"melanogaster",
"model",
"organisms",
"research",
"and",
"analysis",
"methods",
"gene",
"expression",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"genetics",
"mouse",
"models",
"drosophila",
"neuroscience",
"animals",
"insects",
"organisms",
"gene",
"function"
] |
2014
|
Obesity-Linked Homologues TfAP-2 and Twz Establish Meal Frequency in Drosophila melanogaster
|
Two-component systems ( TCSs ) are important for the adaptation and survival of bacteria and fungi under stress conditions . A TCS is often composed of a membrane-bound sensor histidine kinase ( SK ) and a response regulator ( RR ) , which are relayed through sequential phosphorylation steps . However , the mechanism for how an SK is switched on in response to environmental stimuli remains obscure . Here , we report the crystal structure of a complete cytoplasmic portion of an SK , VicK from Streptococcus mutans . The overall structure of VicK is a long-rod dimer that anchors four connected domains: HAMP , Per-ARNT-SIM ( PAS ) , DHp , and catalytic and ATP binding domain ( CA ) . The HAMP , a signal transducer , and the PAS domain , major sensor , adopt canonical folds with dyad symmetry . In contrast , the dimer of the DHp and CA domains is asymmetric because of different helical bends in the DHp domain and spatial positions of the CA domains . Moreover , a conserved proline , which is adjacent to the phosphoryl acceptor histidine , contributes to helical bending , which is essential for the autokinase and phosphatase activities . Together , the elegant architecture of VicK with a signal transducer and sensor domain suggests a model where DHp helical bending and a CA swing movement are likely coordinated for autokinase activation .
Protein phosphorylation is an essential signal carrier . Bacteria respond to transient living environments through transmembrane-integrated sensor histidine kinases ( SKs ) , which act in concert with their intracellular cognate response regulators ( RRs ) to elicit necessary adaptive responses that are critical for their survival and virulence . The SKs and RRs have evolved into a two-component signal transduction system ( TCS ) , whereby stimulation of the SK autophosphorylates at a conserved histidine residue to initiate a signaling cascade [1] . The phosphoryl group is transferred from the SKs to their cognate RRs , some of which lead to quickly reprogram bacteria by altering the transcriptional level of specific downstream target genes [2] . Because of the wide prevalence in bacteria and fungi , TCSs have been considered attractive targets for the development of potential therapeutics to control bacterial infections [3] , [4] . Sensor domains are key modulators for SKs [5]–[7] . PAS domains ( acronym for Per , ARNT , and SIM from Drosophila ) are sensors in a majority of SKs , which respond to alterations in the redox potential , oxygen content , light , and small molecules in their environments [8] , [9] . Because of their broad involvement in biological processes , the structure and function of the PAS domains in interactions with a variety of ligands have been extensively studied [10] , [11] . The oligomeric dynamics of PAS domains in cooperation with local conformational changes can affect the stability of the entire SK , which is thought to be part of the mechanism of signal sensing and transduction [10] , [12] . The enzymatic activities of SKs are modulated by HAMP domains , which are commonly found in histidine kinase , adenylyl cyclase , methyl-accepting chemotaxis , and phosphatase proteins [6] . Structural dynamics of the HAMP domain are believed to mediate transmembrane signal transductions [13] . The NMR structure of a HAMP domain from the putative transmembrane receptor Af1503 in Archaeoglobus fulgidus revealed an unusual knobs-to-knobs interhelical structure , which suggests a coordinated helical rotation model for the HAMP domain in signal transmission [14] . This model was further supported by a series of experimental structures of the HAMP mutants and detailed bioinformatics analyses [15] , [16] . Several other laboratories reached a consensus conclusion using homologous HAMP domains from different TCSs that the dynamic properties of the HAMP domains are essential in mediating signal transduction [17]–[22] . The C-terminal catalytic and ATP-binding domain ( CA ) of the SKs , also called HATPase_c , in addition to the DHp domain ( dimerization and histidine phosphorylation domain ) , phosphorylates a conserved histidine residue in the middle of DHp helices [2] , [23] . The active site of an SK is assembled with the CA and DHp domains [23] , [24] . The plasticity of the DHp domain and CA positioning is implicated in the on/off switch of the temperature sensor DesK kinase from Bacillus subtilis [25] . VicRK is a well-characterized TCS that is highly conserved and essential for survival and virulence in a wide range of firmicute bacteria , including Streptococci , Bacilli , and Staphylococci [26] . Because of its critical role in cell wall synthesis , VicRK is also referred to as WalRK [27] . The VicRK in S . mutans , which is an important pathogen in caries etiology , regulates acid production and tolerance conducive to dental caries , including proton expulsion ( F1F0-ATPase ) [28]–[31] . VicK belongs to the type IA family based on sequence conservation of the DHp and CA domains [32] . In addition , VicK has one HAMP domain and one PAS domain . However , the function of the PAS domain is not clear because no ligand has been identified [26] . Recently , a deletion experiment indicated that the HAMP and PAS domains are essential for VicK phosphatase activity [33] . Given the vital role of TCSs in bacterial adaptation and pathogenicity , decades of research have focused on the molecular mechanisms of TCS signaling cascades [34]–[36] . Although the structure and functions of individual domains are well known , the signal transduction mechanisms remain largely unknown . Toward this end , we determined a crystal structure for a streptococcal VicK that harbors HAMP transducer and PAS sensor domains . Our crystal structure of the nearly full-length VicK comprises an elegant construction of multiple domains and reveals novel insights into the molecular mechanisms of the VicK histidine kinase .
The S . mutans VicK has one transmembrane domain ( TM , aa 9–30 ) that anchors itself to the cytoplasmic membrane ( Figure 1A ) . Following the TM domain , a HAMP signal transducer domain and PAS sensor domain are directly connected to the CA domain through a DHp domain . As several attempts to express full-length VicK ( aa 1–450 ) resulted in insoluble protein , the entire intracellular region ( TVicK , aa 31–450 ) was successfully purified and crystallized . TVicK had a Km of 44 . 5 µM and a Kcat of 0 . 413 min−1 ( Table S1 ) . Its kinetic parameters were similar to the TM-truncated VicK homologue of S . pneumoniae [33] . Static light scattering revealed a single species of VicK at a molecular weight of 103 . 2 kDa , indicating that the holoenzyme exists as a stable dimer in solution ( Figure 1B ) . VicK crystals diffracted slightly better than 3 Å; however , because of strong anisotropy , the final structure was refined up to only 3 . 3 Å ( Table S2 ) . The overall structure of VicK comprises a dimer in the shape of a long slim rod ( Figure 1C ) . The longest dimension of this molecule is nearly 150 Å ( Cα distance ) . Each monomer contains a series of helices ( α1–α11 ) . One asymmetric unit contains two VicK dimers . Remarkably , the total buried surface area is 7590 . 8 Å2 upon VicK dimerization . The dimer interface consists of three tight hydrophobic contact patches ( Figure 1D , I–III ) . In addition , the HAMP , PAS , and DHp domains are organized as dimers , which are connected by long , straight coiled-coils of helices α2 and α6 ( Figure 1C ) . The C-terminal ends of the VicK dimer harbor two monomeric CA domains ( Figure 1C ) . The N-terminal end of monomer A ( Na , aa 31–37 ) and both C-terminal tails ( Ca or Cb , aa 433–450 ) are disordered . The HAMP domain ( aa 36–86 ) is located at the uppermost position within the N-terminal region of the VicK structure ( Figure 1C ) . Helices α1 and α2 of each VicK monomer form a parallel four-helical coiled-coil that is connected with loops L1 ( Figure S1A ) . S . mutans HAMP shares approximately 45% identity to other Streptococci; however , it shows little ( ∼5% ) identity to A . fulgidus Af1503 , although the critical residues at positions a and d are mostly conserved ( Figure 2A ) . The helical interactions within the HAMP domain can be grouped into three shells ( Figure S1B ) . The outer shell is formed by hydrophilic and polar residues at positions b , e , and g of the coiled-coil in addition to two residues from loop L1 ( Figure 2A ) . The residues at the b and g positions in helix α1 are rich in basic residues with long side chains , whereas the corresponding positions in α2 are rich in polar residues ( Figure S1B , gold ) . The middle shell is formed with hydrophobic residues , including leucine , isoleucine , and valine ( Figure S1B , green ) . These residues form the canonical knobs-into-holes packing of coiled-coils [37] . Central to the VicK HAMP bundle are the hydrophobic residues that are all in van der Waals contacts and display knobs-to-knobs or x-layer packing ( Figures 2A and S1B , blue ) . The HAMP outer shell is distinct with three bound rings visible on the electrostatic surface ( Figure S1C ) . Within this bundled structure , there are three pairs of hydrophobic residues in the knobs-to-knobs packing solely from two α2 helices . Two Leu71 residues compose the inside core of the first ring ( Figure 2B ) and two Leu78 residues are inside the core of the second ring ( Figure 2C ) . Strikingly , the Phe82 pair forms π-π stacking inside the third ring , which is further stabilized by two Tyr56 residues from two L1 loops ( Figure 2D ) . Indeed , Tyr56 is completely conserved in Streptococci , whereas Phe82 can be replaced by leucine in Af1503 ( Figure 2A ) . In other HAMPs , position 82 can be Ile , Val , or Leu , whereas position 56 is typically Leu , Phe , or Tyr [15] . These residues are also capable of making van der Waals contacts if placed into the VicK HAMP domain ( unpublished data ) . Notably , Gly54 is absolutely conserved in these HAMP homologs ( Figure 2A ) . The PAS domain ( aa 87–198 ) in VicK , which is located downstream of the HAMP domain , adopts a canonical fold ( Figure 3A ) . Both PAS domains can be well aligned ( root mean square deviation [rmsd] of 1 . 2 Å ) except for the large shift of loop L5 ( Figure S2A ) . The five β-strands form a core of β-sheets , which are sandwiched by two α2 helices on one side and helices α3–5 on the other . The loop L3 and the connected helix α5 form a surface layer on the top of the β-sheet with two flexible loops , L4 and L5 , on both edges . Three substantial binding pockets ( S1–3 ) with a large cavity and inside tunnel are observed on this surface ( Figure 3B ) . The tunnel is lined by mostly hydrophobic residues , including Leu108 , Ile116 , Leu127 , Ile142 , Phe178 , and Leu195 . The VicK PAS domains form a unique dimer , which is mediated by a leucine-zipper ( Figure 3C ) . This leucine-zipper further forms hydrophobic networks with the canonical PAS domain , which suggests that the leucine-zipper is an integral part of the VicK PAS domains ( Figure S2B ) . The residues Leu89 , Leu96 , Leu100 , and Lys93 are in key positions to make van der Waals contacts ( Figure 3C ) . The substitution of Leu100 with arginine was previously shown to disrupt VicK autokinase activity in S . pneumoniae [38] . It is likely that the L100R mutation destabilizes the dimerization of the VicK PAS domains , which sheds light on the functional importance of PAS dimerization . To look for clues of ligands for VicK PAS domains , we compared the VicK PAS domains with representative structures of all ligand-bound PAS domains according to a recent comprehensive review by Henry and Crosson [11] . Despite their limited sequence identities ( 6%–10% ) , all PAS domains can be structurally aligned to VicK with low rmsd of 2–2 . 5 Å ( Figures S3 and S4 ) . The relatively large shifts were observed in loops L3 to L5 and helix α5 of the PAS domains of NifL , FixL , DcuS , and PhoQ , which form pockets for a variety of ligands to bind . Therefore , the VicK PAS domain may bind some ligands differently from these PAS domains because it has a unique cavity and tunnel properties . The C-terminal end of VicK ( aa 199–450 ) contains a histidine-specific ATPase , which is often divided into one DHp domain and one CA domain with a short linker ( aa 270–278 ) ( Figure 1A ) . The dimeric architecture of these domains looks like a butterfly , wherein the DHp domain ( aa 199–269 ) is a four-helix bundle of helices α6 and α7 with a phosphoryl receptor histidine located in the middle ( Figure S5A ) . Surrounding this helical core are two CA domains with a layer of four helices ( α8 to α11 ) on a layer of β-sheets ( β7 to β12 ) , wherein α10 is a short helix ( Figure S5B ) . The two loop regions , L7 ( aa 303–308 ) and L11 ( aa 391–395 ) in the CA domain , called the lid , could not be well defined because of a weak electron density and are labeled with light dashed lines . One linker ( aa 270–274 ) , which connects the DHp and CA domains , was also disordered ( Figure S5A , the monomer in magenta ) . Both CA domains ( aa 278–450 ) of VicK adopt a classical histidine kinase fold and are well aligned with an rmsd of 1 . 3 Å despite the missing loop L11 ( Figure S5B ) . The CA domain of VicK is similar to HK853 ( rmsd of 1 . 7 Å ) except for structural differences in loops L7 and L8 in addition to the loop L11 region ( Figure S5C ) . We could not clearly define ATP binding because of the weak electron density at the current resolution . Unlike the HAMP domain , the DHp domain forms an anti-parallel four-helical coiled-coil . Interestingly , the two monomers of the DHp domain bear an asymmetric fold ( Figure 4A ) . The two monomers are well aligned with an rmsd of 2 . 2 Å for the bottom part of the coiled-coil ( aa 219–255 ) , and the relatively large rmsd of the alignment is mostly caused by flexibility of loop L6 . However , the upper parts of helices α6 and α7 are remarkably different , wherein helix α6 bends ∼25° at Pro222 toward the central DHp axis and α7 moves ∼11° away in the opposite direction to avoid a possible clash . When aligning the VicK DHp domain with T . maritima HK853 , which is a symmetric four-helix coiled-coil , we observed similar bending of monomer A ( Figure 4B ) . The bending is coordinated with shifts of the upper helices , whereas the bottom helices remain stationary , and the upper part of helix α6 of monomer A moves toward the center axis , which possibly drives the other helices away . Helix α6 of the DHp domain has an intrinsic plasticity for bending ( Figure 4C ) . The bending region is absolutely conserved among over 50 VicK homologs in Streptococci , Lactobacilli , Lactococcus , and Enterococci ( Figures 4C , the bottom and S6 ) . Consistently , the DHp domain region ( aa 211–225 ) was predicted to have low helical probability ( Figure 4C , the top ) . Therefore , we hypothesized that the low helical propensity of the DHp domain may play a role in His217 phosphorylation . To test this , we mutated Pro222 to glycine and measured its autokinase activity ( Table S1 ) . The P222G mutant retained full activity when compared to wild-type ( wt ) VicK , as did the T221A mutant . We continued to mutate residues in the bending region , including Val212 , Val215 , Ser213 , and Ser216 to alanine ( VS2AA ) , which is statistically favorable in an α-helix [39] . All mutants , including the combined mutants VSP2AAA , VST2AAA , and VSTP2AAAA , bound S . mutans VicR similarly to wt VicK , which suggested that they likely retain the correct conformation ( Figure S7 ) . The autokinase activity of the single and combined mutants described above was analyzed using γ32P-ATP ( Figure 4D; Table S1 ) . As the Thr221 and Pro222 mutants ( T221A , P222A , P222G , and TP2AA ) retained nearly full activity compared to wt VicK , the two combined mutations of VSP2AAA and VSTP2AAAA showed significantly reduced activity . Although we cannot rule out other effects from multiple sites of mutation , these data are consistent with the model that the low helical propensity of the DHp domain in addition to Pro222 are important for VicK autokinase activity . As VicK is a multifunctional enzyme , we tested whether the helical bending region of the DHp domain is important for phosphatase activity . Here , we used Phos-tag gel mobility shift assay ( PMS ) to detect the phosphorylated S . pneumoniae VicR . As a control , over 90% of VicR was phosphorylated by acetyl-phosphate ( AcP ) , which resulted in a mobility retardation shift ( Figure 5A , lane 2 ) . Further incubation with wt VicK completely removed the phosphate group from VicR ( lane 3 , top gel ) . It is interesting to note that in the absence of 5 mM ATP , little dephosphorylation of VicR was observed ( lane 3 , middle gel ) , which suggested that ATP is required for VicK phosphatase activity . We tested the phosphatase activity of the DHp mutants described above . To our surprise , the single proline or threonine mutations ( P222A and T221A ) abolished VicK phosphatase activity ( Figure 5A , lanes 5 and 7 , top gel ) . In contrast , the P222G and VS2AA mutants , similar to wt VicK , dephosphorylated VicR within the time course of this assay ( lanes 6 and 9 ) . Consistently , the combined mutants VSP2AAA , VST2AAA , and VSTP2AAAA , which contain Pro222 and Thr221 substitutions , also showed little phosphatase activity ( lanes 10–12 ) . It is interesting to note that the VSP2AAA mutant had substantially more phosphatase activity than the single P222A mutant likely because VSP2AAA mutant lost partially kinase activity ( Figure 4D ) . We also analyzed some of these mutants using high performance liquid chromatography ( HPLC ) . Phosphorylated S . pneumoniae VicR eluted at ∼7 . 6 min compared with the native VicR at ∼8 . 8 min ( Figure 5B , run 1 ) . After incubation with VicK in the presence of ATP , phosphorylated VicR completely shifted back to 8 . 8 min ( Figure 5B , run 2 ) . A clear conversion to unphosphorylated VicR was also observed when incubated with the VS2AA mutant ( Figure 5B , run 3 ) . All other VicK mutants , including T221A , P222A , TP2AA , and VST2AAA , failed to dephosphorylate VicR ( Figure 5B , runs 4–7 ) . Together , our data suggest that Pro222 and its neighbor , Thr221 , are the key residues in the bending region required for the phosphatase activity of VicK . Although the overall conformation of each CA domain in VicK is the same , their positions relative to the DHp domain show dramatic differences , which generates an asymmetrical ATPase dimer ( Figure 6A ) . The CA domain rotates ∼61° and further translates ∼20 Å down along an axis parallel to the DHp domain . When aligning the CA domain with the symmetric dimeric structure of T . maritima HK853 , we found that monomer A takes completely different positions ( Figure S8 , magenta ) . The large shift of the CA domain of monomer A moves its active site to the in cis phosphoryl acceptor His217 . Recently , a crystal structure of the B . subtilis YycG CA domain bound to ATP was solved , which is 47% identical to the CA domain of S . mutans VicK [40] . These two structures of the CA domains aligned well with an rmsd of 1 . 47 Å of all backbone atoms , which is where the ATP from the structure of the YycG CA domain was modeled nicely in the active pocket of the VicK CA domain . To our surprise , we found that the γ-phosphate of ATP approached His217 to form two hydrogen bonds with εΝ or δN of His217 ( Figure 6B , yellow dashed lines ) . In addition , Leu265 from monomer B is close to His217 to form van der Waals interactions ( Figure 6B , grey dashed lines ) . Arg220 of monomer A and Arg269 of monomer B are also in close contact with His217 . In addition to the ATP binding pocket , the CA domain positions itself toward the DHp domain to form a large interface ( Figure S9 ) . Many residues are involved in the direct interaction between the DHp and CA domains ( Figure 6C ) . Arg294 , Asp326 , Gln330 , Asn334/337 , and Arg385 of the CA domain form hydrogen bonds with Glu218 , Thr221 , Glu253 , Arg256 , and Arg259 residues around the middle region of the DHp domain . In addition , Arg382 and Asp387 form hydrogen bonds with Arg206 and Glu207 at the upper part of the DHp domain . Phe295 , Ile298 , Phe383 , and Ile403 of the CA domain also form van der Waals contacts with Asn214 , Thr224 , Ser225 , and Tyr229 of the DHp domain . Ile403 , which is located on the back of the ATP binding pocket of VicK , is one of the key residues that contribute to the hydrophobic interaction with Leu267 and the aliphatic side chain of Glu218 . To test whether these interactions are important for autokinase activity , we generated a series of mutants and analyzed their autokinase activity ( Figure 6D ) . The Arg294 , Gln297 , Ile298 , Asn334 , and Asn337 mutations ( R294A , Q297A/I298A , and N334A/N337A ) were nearly as active as wt VicK ( Figure 6D , lanes 5 , 7 , and 9 ) . In contrast , mutations of Asp326 , Gln330 , Arg382 , Arg385 , and Phe383 ( D326A/Q330A , R382A/R385A , and F383A/R385A ) dramatically suppressed VicK autokinase activity ( Figure 6D , lanes 8 , 10 , and 12 ) , whereas the dual mutations of D326A/N337A and R382A/R385A completely eliminated VicK autokinase activity ( Figure 6D , lane 13 ) . As references , two mutations in the ATP-binding pocket ( K341A/Y342A ) and a deletion of the first G loop ( del392–395 , RAQG ) negatively affected autokinase activity ( Figure 6D , lanes 11 and 14 ) . Two Ile403 mutations I403W and I403S did not significantly affect VicK autokinase activity ( Figure 6D , lanes 3 and 4 ) . Interestingly , the I403W mutation was found to dramatically increase autokinase activity in HK853 ( I448W ) [23] . Together , the CA domain of monomer A can position itself toward the DHp domain to form a large interface , which is further centered by two groups of key residues . One group is D326/Q330 , which forms hydrogen bonds with Arg259 of the DHp domain . Another group is R382 and R385 , which form hydrogen bonds with Glu207 and Glu218 of the DHp domain . F383 inserts into a hydrophobic pocket in the DHp domain and further stabilizes the interactions between the CA and DHp domains . Therefore , a mutant ( D326A/Q330A/R382A/R385A ) that disrupts both groups of key residues completely eliminated the autokinase activity ( Figure 6D , lane 13 ) . The CA domain of VicK monomer B stays away from the DHp domain and represents an inactive state ( Figures 1C and S5A ) . A relatively small interface is formed mainly by van der Waals interactions between Phe383 , Ile403 , and Leu399 from the CA domain and Phe211 , Val215 , and Leu264 from the DHp domain , which are further stabilized by several hydrogen bonds between Arg382 , Arg385 , Asp271 , and Glu218 ( Figure S10 ) . The buried surface of this interface is 623 Å2 , which suggests that it may not be sufficiently stable . Phe383 and Leu399 appear to be the most important residues because they insert into helices α6 and α7 for extensive van der Waals interactions with Leu264 and Val215 . When compared with T . maritima HK853 alone , the VicK CA domain rotates ∼76° along an axis vertical to the DHp domain ( Figure S8 ) . Overall , the CA domain remains inactive by positioning itself away from the DHp domain and this interface may act as a hinge .
Our crystal structure revealed a long rod-shaped VicK holoenzyme ( Figure 1 ) . The three modules of the HAMP , PAS , and DHp/CA domains are connected through two groups of straight helices . Such an extended VicK molecule may provide sufficient surface and accessibility for potential ligands or protein partners to interact with . The long shaped molecules often have the largest radius of gyration and tend to polymerize . VicK and its homologs have one or two integrated TMs rather than being free cytosolic proteins and are localized as clusters within the membrane [26] . In B . subtilis , YycG proteins center in the division ring possibly through interactions with FtsZ , a tubulin-like protein [41] . In contrast , approximately 420 dimeric VicK holoenzymes are randomly distributed as clusters throughout the periphery of one S . pneumoniae cell [42] . This clustering characteristic indicates that its function might require direct interaction between individual molecules , which is consistent with the physical properties of rod-shaped molecules . A long-standing question is how HAMP domains serve in signal transduction because they often directly connect TMs and extracellular sensors . The HAMP domains may undergo a 26° rotation , which is derived from the unusual knobs-to-knobs packing of the solution structure of Af1503 HAMP , when they receive transmembrane signals [14] , [15] . The crystal structure of concatenated HAMP domains from Pseudomonas aeruginosa Aer-2 implicates that the conformational dynamics may also serve a role in signal transduction [20] . Consistently , a series of structure and functional experiments have shown that intrinsic thermodynamic instability is required for HAMP signaling [16] , [17] , [19] , [21] , [43] . The VicK HAMP domain appears to be a stable four-helix coiled-coil with classical knobs-into-holes interactions , wherein three pairs of hydrophobic residues from helix α2 form a central hydrophobic core with knobs-to-knobs packing , and it is unlikely that structural alternations of the HAMP domain play a central role in signal transmission ( Figures 2 and S1B ) . The VicK HAMP domain is further stabilized by Phe82 , which is conserved in the majority of streptococcal species ( Figures 2A and S11 ) . However , variable residues , including isoleucine , valine , and occasionally threonine , are present at position 82 in some VicK homologs , which suggests that the stability of HAMP domains may vary in different species . It is worth noting that the α2 helices of the HAMP domain connect with downstream PAS domains through continuous helices , part of which ( aa 86–103 ) form a fairly rigid leucine-zipper ( Figure 3C ) . This long helical structure most likely serves two purposes: ( 1 ) The HAMP domain might be further stabilized by additional interlock packing of the coiled-coil; and ( 2 ) The HAMP domain can easily transfer any conformational change down to the PAS domain . In contrast , the connection of the α6 helices between the PAS and DHp domains , particularly a short linker between α6 and β5 , is rather flexible . Thus , the VicK molecule also appears to have a potential thermodynamic property for signal transduction from the HAMP or PAS domains to the catalytic CA domains . The VicK PAS domains form a stable dimer through the short leucine-zipper ( aa 89–103 ) ( Figure 3C ) . In general , canonical PAS domains are rather flexible and readily subjected to ligand-induced conformational changes [9] . The three loops , L3–L5 , form a mobile surface that could be regulated by unknown ligands ( Figure 3B ) . Winkler et al . recently demonstrated that deletion of the PAS domain but not a triple mutation ( D133N , N136Y , and L140R ) of S . pneumoniae VicK reduced the autokinase activity and , more dramatically , phosphatase activity [33] . A similar deletion experiment also showed that the PAS domain is essential for the phosphatase activity of T . maritima ThkA [44] . Our S . mutans VicK structure shows that Leu135 ( equivalent to N136 of S . pneumoniae VicK ) stabilizes helix α4 by interactions with Ile116 and Ile139 ( equivalent to L140 of S . pneumoniae VicK ) , which contributes to the hydrophobic cavity of the PAS domain ( unpublished data ) . Therefore , it is likely that an Ile139 to arginine mutation only disrupted potential ligand binding but not dimerization . In contrast , an L100R mutation disrupted dimerization of the PAS domain , and in turn , the kinase activity of S . pneumoniae VicK [38] . In ligand-free VicK , helix α5 adapts an open conformation when compared to flavin adenine dinucleotide ( FAD ) -bound NifL , heme-bound FixL , and malate-bound DcuS ( Figure S4 ) . Thus , loop L3 is able to provide a sufficient cavity and tunnel for potential ligands to bind . Interestingly , when aligned with PhoQ , helix α4 and loop L3 of the VicK PAS domain adapt significantly different conformations and the trajectory of helix α5 is similar . Unfortunately , despite our efforts , no ligand specific to the VicK PAS domain has been experimentally identified . Only when such ligands are identified will it be possible to analyze how induced conformational changes regulate VicK catalytic activities . Therefore , this remains an interesting area for future research . The local region around the phosphoryl receptor histidine of DHp domains is highly conserved [45] . We found that this region has a low helical propensity and is subject to significant helical bending , which is consistent with its low helical propensity . The helical bending most likely helps place His217 in close proximity to the CA domain to allow hydrogen bonds to form with the γ-phosphate of ATP ( Figures 4 and 6B ) . Similarly , the DHp domain of B . subtilis DesK bends 50–54° when His188 is phosphorylated [25] . The HK853 DHp domain also bends 20° when bound to its cognate RR468 [46] . Therefore , the helical bending appears to be an intrinsic property of the DHp domain and is relevant to its function . Indeed , we showed that the combined mutations , through introducing alanines ( VSP2AAA and VSTP2AAAA ) into the DHp domain , abolished VicK autokinase activity ( Figure 4D ) . Residues Pro222 and Thr221 are conserved among VicK homologs ( Figure 4C ) . Interestingly , these residues are essential for phosphatase activity because mutations of either P222A or T221A abolished the phosphatase activity of VicK ( Figure 5 ) . Proline , which produces a kink and 18–35° bending that affects the thermodynamic stability of the α-helix [47] , plays key roles in the transmembrane signaling of several proteins , including G-protein-coupled receptors and voltage-gated potassium channels [48] . In addition to proline , threonine and serine residues are able to bend a helix 3–4° larger than alanine , which is important for the channel gating of voltage-dependent connexin32 [49] , [50] . Our data in this study demonstrate that Pro222 and Thr221 are essential for phosphatase activity in VicK ( Figure 4D ) . Glycine may also serve a similar role as proline . The glycine in the middle of an α-helix attributes unique flexibility [51] . B . subtilis DesK has a glycine instead of proline present in this region [25] . Consistently , our P222G , as opposed to the P222A mutant , had phosphatase activity similar to wt VicK ( Figure 5A , lane 6 ) . It is worth noting that the DHp domain is not significantly bent in the structure of B . subtilis Spo0B and Spo0F complex compared with T . maritima HK853 and RR468 complex , although a glycine residue is localized adjacent to the phosphoryl receptor histidine [52] . However , it is possible that the crystal structure of the Spo0B and Spo0F complex captures only one state of the DHp domain of Spo0B kinase . Our structure has shown that monomer A positions toward to its own His217 to form an active state , which is consistent with the findings using heterodimeric kinase mutants of HK853 and PhoR [46] . A flexible linker between the CA and DHp domains has been postulated to play a role in CA domain swinging [23] . In addition , the small interface created by Arg382 , Phe383 , and Arg385 provides a docking site as well as sufficient freedom for the CA domain to rotate ( Figure S10 ) . In the HK853 and RR468 complex , the CA domain swings ∼37° along the axis of the DHp domain when compared with free HK853 [46] . Crystal structures of B . subtilis DesK have shown that the CA domains could position themselves differently relative to the DHp domain [25] . The buried surface of the active monomer A is 1140 Å2 . However , this interface is mainly composed of hydrophilic and polar residues , which suggests that these contacts may only be transient ( Figure 6C ) . Our mutagenesis experiments showed that these residues are important for VicK autokinase activity ( Figure 6D ) . Interestingly , while Ile403 mediates van der Waals contacts with Asn214 and Glu218 in the active state , it also contributes to the interface in the inactive state ( Figures 6C and S10 ) . Thr221 mediates a hydrogen bond with Asn337 and van der Waals interactions with Phe295 ( Figure 6C ) . The T221A mutation eliminated phosphatase activity but did not affect the autokinase activity ( Figures 4D and 5 ) . In contrast , Winkler et al . found that a T221R mutant of S . pneumoniae VicK completely abolished its autokinase activity and greatly reduced phosphatase activity [33] . It is possible that the large arginine side chain may block the CA domain from properly accessing the DHp domain for active site formation . It is important to note that the asymmetrical positioning of the CA domains and the different conformations of each monomer of the DHp domain are captured in a unique crystal-packing environment . The two VicK dimers form an anti-parallel tetrameric packing where the active CA position is stabilized by direct interactions with HAMP and PAS domains from another dimer in the same asymmetric unit ( Figure S12 ) . The N terminus of the HAMP domain makes contacts with the inactive CA domain of the VicK dimer from another asymmetric unit through the extended N terminus of monomer B ( Nb ) ( Figure 1C ) . However , the overall structures of the HAMP and PAS domains remain symmetric . Together , coordinated helical shifts of DHp and movement of the CA domains can be combined into a model to illustrate the activation steps for VicK ( Figure 7 ) . When both CA domains are inactive , they stay relaxed and further away from the phosphoryl acceptor histidine ( I ) . Upon stimulation , one helix bends ∼25° toward the DHp central axis in coordination with the global shifts of DHp to expose the phosphoryl acceptor histidine . Consequently , one CA domain in cis rotates ∼61° to reach this histidine for initial phosphorylation ( II ) . It is likely that this activation does not happen simultaneously for both CA domains because the DHp domain allows only one helix to bend at a time ( Figure 4B ) . To fully activate both histidines ( IV ) , VicK may go through an intermediate state that is similar to the inactive state ( III ) before the second CA domain rotates . Finally , the CA domains swing back to the initial state ( IV to I ) through several steps that remain to be determined .
S . mutans VicK ( aa31–450 ) was cloned into vector pET15 ( Novagen ) . S . mutans VicR ( aa1–235 ) and S . pneumoniae VicR ( aa1–234 ) were cloned into pETHis vector . All these constructs were expressed with N terminal 6× Histidine tag in Escherichia coli BL21/DE3 Rosetta ( Novagen ) . VicK protein was purified through nickel affinity agarose ( Qiagen ) , Q sepharose , phenyl sepharose , and Superdex S200 ( GE Healthcare ) . The two VicR homologs were simply purified by nickel affinity agarose and gel filtration . Protein preps were concentrated down to 8 mg/ml in a buffer of 10 mM Tris ( pH 8 . 0 ) , 100 mM NaCl , 300 mM ammonium acetate , 2 mM DTT , and 1 mM β-mecaptoethanol ( β-ME ) . The preliminary crystals were obtained using the screen kits of JCSG Plus ( Qiagen ) and Index ( Hampton Research ) . Most crystals from these initial screening could only diffract to ∼4 . 5 Å . The later optimizations of those crystals defined the best condition of 2 . 3–2 . 8 M Sodium formate , 50 mM Tris ( pH 7 . 4–8 . 6 ) , and 4% PEG 4000 at room temperature . Selenium-methionine labeled protein was prepared following a standard protocol [53] . Bar-like crystals were grown for 2–4 d and immediately frozen in liquid nitrogen after quickly soaked with crystal growth buffer and additional 12% isopropanol as a cryo-protectant . Data were collected in BM17U , Shanghai Synchrotron Radiation facility ( SSRF ) , China and processed by HKL2000 [54] and CCP4 suite [55] . Data collection statistics are summarized in Table S2 . The selenium sites were located using SHELXD [56] . Heavy atom positions were refined and phases were calculated with PHASER [57] . The real-space constraints were applied to the electron density map in DM [58] . An initial model was then built manually using COOT [59] . The model was further refined using PHENIX with stereochemistry and secondary structure information as restraints [60] . The structure and refinement statistics are summarized in Table S2 . Model analyses were performed using a variety of programs . The structural alignments were calculated in Coot [59] . Similar folds searches were carried out using Dali server [61] . The helical bending was calculated using a program HELANAL [62] . The buried surface areas were calculated in CNS [63] . The electrostatic potential surfaces was calculated and graphed by CCP4mg [64] , while other graphics were made using Pymol ( DeLano Scientific LLC ) . The VicK protein prep at ∼4 mg/ml was first resolved on a size exclusion column ( Shodex KW-802 . 5 ) in a buffer of 50 mM HEPES ( pH 7 . 0 ) and 200 mM Na2SO4 at 25°C . Data were then collected on a DAWN HELEOS II laser photometer with an emission at 658 nm ( Wyatt , USA ) . Molecular mass was calculated using ASTRA V ( Wyatt , USA ) . All VicK mutants were generated by our modified Quikchange mutagenesis protocol [65] . All these mutants were purified as wt VicK described above . β–ME was excluded from buffer in the final protein preps . The autokinase activity of VicK was measured by using isotope γ32P-ATP ( Perkin-Elmer , NEG002Z001MC ) according to a recent protocol published by Winkler and his colleague [33] . The working solution of the hot ATP was freshly made by mixing the hot ATP with an equal volume of 3 mM cold ATP . The VicK proteins at 0 . 5–10 µM were incubated with a concentration gradient ( 0–80 µM ) of the hot ATP working solution in 10 µl reaction buffer of 50 mM Tris ( pH 7 . 5 ) , 200 mM KCl , and 10 mM MgCl2 for 0 . 25 to 8 min at room temperature . The measurements were setup at four different time points . The protein concentration and time points for each VicK protein were pre-determined to have the signals within linear ranges . The reactions were stopped by adding 3 µl 4× SDS loading buffer . The resulted mixtures were then subjected to 12% SDS-PAGE before the gels were dried and scanned in Typhoon 9410 ( GE Healthcare ) . Quantifications were carried out using a program Totallab Quant . The autokinase parameters were derived from non-linear regression of the Michaelis-Menten equation . For simple comparison of the various VicK mutants , the VicK protein preps of 2 µg were mixed with 0 . 3 µl hot ATP working solution in 15 µl reaction buffer and further incubated for 30 min at room temperature . The reactions were stopped with 4× SDS loading buffer and separated in 15% SDS-PAGE . The gels were dried before being exposed to X-ray film . S . pneumoniae VicR at concentration of 4 µM was phosphorylated in 50 mM AcP , 50 mM Tris ( pH 7 . 4 ) , 50 mM KCl , 2 mM MgCl2 , and 20% glycerol for 1 h at 37°C [66] , [67] . The phosphorylated VicR was then mixed with VicK wt and mutants at a final concentration of 4 µM for another hour at 37°C . AcP was diluted by at least 10-fold in the latter reaction . The resulted mixtures were first analyzed by PMS [68] . Briefly , the regular 8% SDS gels ( 29∶1 ) were prepared with additional 50 µM phos-tag acrylamide ( Wako ) and 100 µM MnCl2 . The gels were run at 120 V for 120 min at 4°C for the best mobility shift and stained with coomassie blue . The reactions of phosphorylated VicR were also analyzed by HPLC [67] . Briefly , additional 20% glycerol was added into reactions of VicR after treated with AcP as described above , which were further mixed with HPLC running buffers ( A: 0 . 1% trifluoric acid; B: 0 . 1% trifluoric acid and 100% acetonitrile ) to reach 40% acetonitrile . HPLC was run using a reverse-phase C8 column ( 4 . 6 mm×250 mm ) ( Agilent 1200 ) . The phosphorylation state of VicR was confirmed by PMS as described above . Homologs of the full-length VicK with >50% sequence identities were initially pooled from Genebank using a program BLAST [69] . Redundant entries of 96%–100% identity were identified using a multiple alignment program CLUSTAL [70] and subsequently removed , resulting in >50 unique homologs with 50%–96% identities ( Figure S11 ) . The DHp regions corresponding to amino acids 197–269 of these VicK homologs were used to generate the conservation logo using Weblogo server ( weblogo . berkeley . edu ) . Redundant sequences ( with 100% identity ) of the DHp domain were further removed , resulting in >40 unique sequences ( Figure S6 ) . The helicity of the DHp was analyzed by a comprehensive secondary structure prediction program Phyre , which scores each amino acid as 0–9 for a helical probability [71] . DHp engineering was carried out through reiterative process of mutations and secondary structure calculation . Those unfavorable amino acids to α-helices within the bending region of DHp domain were determined according to statistical analyses [39] . The coordinates of the structure and relevant information have been deposited into the Protein Data Bank ( 4I5S ) .
|
Two-component signal transduction systems ( TCSs ) are promising targets for new antimicrobial research because they help bacteria and fungi adapt and survive . One of the main components of TCSs is a sensor histidine kinase ( SK ) , which relays extracellular signals to intracellular pathways . Despite intensive research , a full-length structure of an SK has yet to be solved . In this study , we report the first crystal structure of the complete cytoplasmic region of VicK , an important SK in the tooth decay pathogen S . mutans . VicK is composed of several domains ( HAMP , PAS , DHp , and catalytic and ATP binding domain [CA] ) in addition to a short transmembrane domain . We find that the dimeric VicK protein has an elegant rod-shaped structure with the domains linearly connected like beads on a string . The structure suggests that VicK kinase activates itself by helical bending of the DHp domain and coordinated swinging around of the catalytic CA domain to engage with the target histidine . Structure-based mutagenesis experiments also helped us to identify key residues that are required for VicK's opposing phosphatase activity . Our studies of the multi-modular VicK protein suggest a sequential kinase activation model that may involve helical bending of the DHp domain and repositioning of the CA domains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"enzyme",
"structure",
"enzymes",
"enzyme",
"regulation",
"macromolecular",
"assemblies",
"biology",
"enzyme",
"kinetics",
"biophysics"
] |
2013
|
Mechanistic Insights Revealed by the Crystal Structure of a Histidine Kinase with Signal Transducer and Sensor Domains
|
The position of genes in the interphase nucleus and their association with functional landmarks correlate with active and/or silent states of expression . Gene activation can induce chromatin looping from chromosome territories ( CTs ) and is thought to require de novo association with transcription factories . We identify two types of factory: “poised transcription factories , ” containing RNA polymerase II phosphorylated on Ser5 , but not Ser2 , residues , which differ from “active factories” associated with phosphorylation on both residues . Using the urokinase-type plasminogen activator ( uPA ) gene as a model system , we find that this inducible gene is predominantly associated with poised ( S5p+S2p− ) factories prior to activation and localized at the CT interior . Shortly after induction , the uPA locus is found associated with active ( S5p+S2p+ ) factories and loops out from its CT . However , the levels of gene association with poised or active transcription factories , before and after activation , are independent of locus positioning relative to its CT . RNA-FISH analyses show that , after activation , the uPA gene is transcribed with the same frequency at each CT position . Unexpectedly , prior to activation , the uPA loci internal to the CT are seldom transcriptionally active , while the smaller number of uPA loci found outside their CT are transcribed as frequently as after induction . The association of inducible genes with poised transcription factories prior to activation is likely to contribute to the rapid and robust induction of gene expression in response to external stimuli , whereas gene positioning at the CT interior may be important to reinforce silencing mechanisms prior to induction .
The spatial folding of chromatin within the mammalian cell nucleus , from the level of whole chromosomes down to single genomic regions , is thought to contribute to the expression status of genes [1]–[3] . Mammalian chromosomes occupy discrete domains called chromosome territories ( CTs ) and have preferred spatial arrangements within the nuclear landscape in specific cell types , which are conserved through evolution [1]–[3] . Subchromosomal regions containing inducible genes , such as the MHC type II or Hox gene clusters , relocate outside their CTs upon transcriptional activation or when constitutively expressed [4] , [5] . Genes can preferentially associate with specific nuclear domains according to their expression status . Most noteworthy , gene associations with the nuclear lamina largely correlate with silencing [6]–[9] , whereas gene associations with transcription factories , discrete clusters containing many RNA polymerase II ( RNAP ) enzymes , have been observed only when genes are actively transcribed , but not during the intervening periods of inactivity [2] . Although CTs do not represent general barriers to the transcriptional machinery [10] , [11] and transcription can occur inside CTs [3] , [12]–[14] , the large-scale movements of chromatin , observed in response to gene induction , have often been interpreted as favouring gene associations with compartments permissive for transcription [15]–[17] . However , inducible genes frequently display an active chromatin configuration and are primed by initiation-competent RNAP complexes prior to induction [18]–[21] . Complex phosphorylation events at the C-terminal domain ( CTD ) of the largest subunit of RNAP correlate with initiation and elongation steps of the transcription cycle and are crucial for chromatin remodelling and RNA processing [22] , [23] . The mammalian CTD is composed of 52 repeats of an heptad consensus sequence Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7 , and phosphorylation on Ser5 residues ( S5p ) is associated with transcription initiation and priming , whereas phosphorylation on Ser2 ( S2p ) correlates with transcriptional elongation [22] , [23] . To investigate whether primed genes are associated with discrete RNAP sites enriched in RNAP-S5p and the functional relevance of large-scale gene repositioning in promoting associations with the transcription machinery during gene activation , we investigated the expression levels , epigenetic status , nuclear position , and association with RNAP factories of an inducible gene , the urokinase-type plasminogen activator ( uPA or PLAU; GeneID 5328 ) , before and after activation . We use antibodies that specifically detect different phosphorylated forms of RNAP to investigate the association of the inducible uPA gene with transcription factories . Prior to induction , most uPA alleles are positioned inside their CT and extensively associated with RNAP sites marked by S5p . Transcriptional activation leads to looping out of the uPA locus from its CT , and increased association with active transcription factories marked by both S5p and S2p . However , the extent of gene association with factories , before and after activation , is independent of the uPA position relative to its CT . Unexpectedly , we find that the majority of uPA genes which are positioned at the CT interior prior to activation are seldom transcribed , in comparison with the few uPA genes located outside the CT which are active with the same frequency as the fully induced uPA genes .
The uPA gene encodes a serine protease that promotes cell motility , and its overexpression is known to correlate with cancer malignancies and tumor invasion [24]–[26] . It is a 6 . 4 kb gene with 10 introns , and its regulatory regions have been extensively characterized [24] . uPA is located on human chromosome 10 , separated from upstream and downstream flanking genes CAMK2G ( calcium calmodulin-dependent protein kinase II gamma; GeneID 818 ) and VCL ( vinculin; GeneID 7414 ) by ∼40 and ∼80 kb , respectively ( Figure 1A ) . In HepG2 cells , where the uPA gene is present as a single copy , its transcription can be induced through various stimuli , including treatment with phorbol esters [27] . Tetradecanoyl phorbol acetate ( TPA ) induces its expression by ∼100-fold in HepG2 cells after 3 h of treatment ( Figure 1B ) . The induction of the uPA gene within this short time of activation occurs in all cells of the population , as shown by immunofluorescence detection of uPA protein in single cells ( Figure 1C ) ; low levels of uPA protein are detected in a small proportion ( 7% ) of the cell population prior to activation . We first investigated whether transcriptional induction of the uPA gene was associated with large-scale repositioning relative to its CT , using a whole chromosome 10 probe together with a BAC probe containing the uPA locus ( Figure 1A ) . We performed fluorescence in situ hybridization on ultrathin ( ∼150 nm ) cryosections ( cryoFISH ) , a method that preserves chromatin structure and organisation of transcription factories . Cells are fixed using improved formaldehyde fixation in comparison with standard 3D-FISH , which is particularly important for the preservation of chromatin structure and RNAP distribution [14] , [28] . CryoFISH also provides sensitivity of detection and high spatial resolution , especially in the z axis [14] , [29] , [30] . We find that , in the inactive state , the uPA locus is preferentially localized at the CT interior ( 60% loci inside or at the inner-edge , n = 166 loci ) and relocates to the exterior upon activation ( 55% loci at outer-edge or outside , n = 208 loci; χ2 test , p<0 . 0001; Figure 1D ) , concomitant with the 100-fold induction of mRNA levels determined by qRT-PCR ( Figure 1B ) . Thus , we observed a striking change in the position of the uPA locus relative to its CT upon TPA activation , which correlates with a major increase in mRNA and protein expression across the whole population of cells . Chromatin repositioning in response to gene activation has often been associated with changes in chromatin structure and degree of condensation [15] , [17] . To establish whether the large-scale relocation of the uPA locus during its transcriptional activation was accompanied by changes between closed and open chromatin conformations , we next assessed the chromatin structure of the uPA gene before and after TPA treatment ( Figure 2 ) . Micrococcal nuclease ( MN ) digestion of crosslinked , sonicated chromatin yields a decreasing nucleosomal ladder before and after TPA activation ( Figure 2A and images unpublished , respectively; [31] ) . Systematic PCR amplification at and around the uPA regulatory regions revealed two populations of genomic DNA fragments that resist processive cleavage at high digestion time points ( 50 min; see also [31] ) . At the enhancer , the size of fragments is typically mononucleosomal ( ∼150 bp; fragment E1; Figure 2B , 2C ) . At the promoter , the protected fragments have larger sizes ( >300 bp; fragments P and Px; Figure 2B , 2C ) . This feature is consistent with the presence of RNAP-containing complexes at the promoter , which was previously observed at the transcriptionally active uPA gene in constitutively expressing cells , but absent after α-amanitin treatment [31] . The same population of larger promoter fragments was also detected in uninduced cells ( Figure 2C ) , showing that the uPA gene displays transcription-associated features before activation . This was supported by an investigation of the epigenetic status of chromatin before and after activation . High resolution , MN-coupled chromatin immunoprecipitation ( MN-ChIP; [31] ) using antibodies specific for histone modifications associated with close ( H3K9me2 ) or open ( H3K4me2 , H3K9ac , H3K14ac ) chromatin [32] showed the presence of active , but not silent , chromatin marks at the promoter and enhancer of the uPA gene , both before and after TPA induction ( Figure 2D ) . Positive detection of H3K9me2 was confirmed at the imprinted H19 gene ( GeneID 283120; Figure S1 ) . Upon activation , the larger promoter fragment ( P ) is no longer detected with H3K14ac antibodies , although this mark is still present at the smaller promoter fragments ( uP and dP ) , and an enrichment of H3K9ac at the E5 fragment on the enhancer was also detected ( Figure 2D ) . These changes are likely to reflect the presence of different populations of resistant fragments at the uPA regulatory regions upon induction . Taken together , these findings show that the uPA gene adopts an open chromatin state before transcriptional activation , which is maintained after induction . The large-scale chromatin repositioning of the uPA locus relative to its CT ( Figure 1D ) cannot therefore be explained by changes from closed to open chromatin conformation . The presence of RNAP phosphorylated on Ser5 residues at promoter regions of silent genes defines them as paused or poised genes [21]–[23] . To determine whether the inactive uPA gene was associated with RNAP prior to induction , we used MN-ChIP and antibodies specific for phosphorylated forms of RNAP that can discriminate between active and paused/poised RNAP complexes [21]–[23] . Antibody specificity has been extensively characterized previously [21] , [33] and was confirmed in HepG2 cells using Western blotting and immunofluorescence ( Figure S2 ) . MN-ChIP detected the initiating ( S5p ) but not the elongating ( S2p ) form of RNAP at the promoter and enhancer regions of the uPA gene prior to induction ( Figure 3A ) , demonstrating that the uPA gene is primed with RNAP before activation . The detection of RNAP-S5p at the enhancer ( Figure 3A ) can be explained by an interaction with RNAP bound at the promoter , as previously observed in constitutively active uPA genes [31] . Previous studies describing the presence of RNAP at the promoters of paused or poised genes did not investigate a possible association with transcription factories marked specifically by the S5p modification [18] , [19] , [21] , [34] . We asked whether the association of primed uPA loci with RNAP-S5p could be detected at the single cell level and occur within specific nuclear substructures , using immuno-cryoFISH [14] . Using a BAC probe covering a genomic region centred on the uPA gene ( Figure 1A ) in combination with immunolabelling of the S5p or S2p forms of RNAP , we found that the vast majority of uPA loci were associated with sites containing RNAP-S5p prior to activation ( 87%±9% , n = 165 loci; Figure 3B , 3C ) . A significantly lower proportion was found associated with RNAP-S2p foci ( 31%±5% , n = 170 loci; χ2 test , p<0 . 0001; Figure 3C ) . The primed uPA loci are therefore preferentially associated with a subpopulation of RNAP factories that contain RNAP-S5p , but not RNAP-S2p , prior to TPA activation . We call these sites “poised , ” or S5p+S2p− , transcription factories . Scoring criteria for gene association with RNAP sites , typically used in the analyses of 3D-FISH results , often rely on proximity criteria that do not involve true physical associations , being sensitive to the limited z axis resolution ( >500 nm ) of standard confocal microscopes . This is particularly important when analysing highly abundant structures such as transcription factories which can exist at densities of 20/µm3 [35] . Although the use of ultrathin ( ∼150 nm ) cryosections mostly detects single factories [29] , we were still concerned that the extent of uPA gene association with transcription factories marked by S5p or S2p observed experimentally ( Figure 3C ) could be due to different abundance of the two modifications and might be explained , at least in part , by random processes . To assess the impact of these two constraints , we generated one simulated uPA signal , for each experimental image , with the same number of pixels as the experimental site , but positioned at random coordinates within the nucleoplasm . Next , we measured the frequency of association of the randomly positioned loci with RNAP-S5p or -S2p sites ( Figure S3B , S3C ) . We found that the association of randomly positioned BAC signals with S5p was 54%±8% ( Figure S3C; n = 68 loci ) , a significantly lower number than the experimental value of 87%±9% for the uPA locus ( Figure 3C; χ2 test , p<0 . 0001 ) . In contrast , the association of randomly positioned signals with S2p was 39%±5% ( n = 69 loci ) , similar to the experimental value of 31%±5% ( χ2 test , p = 0 . 29; Figure S3C ) . One caveat of these analyses is that the observation of similar levels of association for experimental or simulated loci with transcription factories prior to activation cannot be used to argue that this association is not specific , but simply that it is as low as it would be if loci were positioned randomly . In summary , our results show that the association of a large proportion of uPA loci with poised , S5p+S2p− factories before activation is specific , although the nuclear environment where the uPA loci are located is not devoid of active , S5p+S2p+ transcription factories , and therefore seems to be permissive for transcription . To investigate the active state of the uPA gene and the engagement of the locus with active factories following transcriptional induction , we repeated the MN-ChIP and immuno-cryoFISH analyses for TPA-treated cells ( Figure 3D–F ) . MN-ChIP showed that the enhancer and the promoter of the uPA gene are associated with the elongating ( S2p ) form of RNAP either together with RNAP-S5p ( at the promoter ) or exclusively ( at the enhancer; Figure 3D ) . The absence of RNAP-S5p at the enhancer fragments analysed , in the presence of S2p , suggests that the enhancer may maintain an association with RNAP as it moves through the coding region during elongation , where S5p is known to decrease and S2p to augment [23] . Immuno-cryoFISH after TPA induction ( Figure 3E , 3F ) showed that the activated uPA locus now becomes associated with RNAP-S2p sites ( 72%±7% loci , n = 183 ) , while maintaining an association with RNAP-S5p ( 71%±8% , n = 140 loci; χ2 test , p = 0 . 98 ) , consistent with the MN-ChIP results . The approximately 2-fold increase in association with RNAP-S2p from 31% to 72% , before and after TPA treatment , respectively , was highly statistically significant ( χ2 test , p<0 . 0001 ) . Evaluation of simulated uPA loci positioned at random coordinates in the same experimental images showed that the increased association of the uPA locus with S2p observed after activation ( 72% ) cannot be explained by random processes , as the frequency of association of simulated loci remained at 38%±2% ( n = 75 loci; χ2 test , p<0 . 0001; Figure S3B , S3C ) . An increased association with S2p sites upon activation has also recently been described for the Hoxb1 gene in mouse embryonic stem cells , albeit at lower frequency [36] . Taken together we show that the activation of the uPA gene and large-scale repositioning of the locus relative to its CT coincide with the acquisition of the S2p modification of RNAP without major changes in chromatin structure . The striking agreement between the number of uPA loci associated with RNAP factories marked by S5p and S2p upon activation and the co-existence of the two RNAP modifications detected by MN-ChIP at the promoter suggest that active factories contain both modifications , as expected from concomitant initiation and elongation events at promoter and coding regions of highly active genes . To further investigate whether poised transcription factories marked by S5p alone are distinct from the active factories marked by S2p , we compared the number of RNAP-S5p and RNAP-S2p sites in HepG2 cells , before and after induction ( Figure 4A–C ) . RNAP sites marked by S5p are significantly more abundant than RNAP sites marked by S2p ( 28% excess ) both before and after activation ( Student t test , p<0 . 0001 in both cases; Figure 4A–C ) , suggesting that a considerable number of transcription factories adopt the poised state . The excess number of sites containing S5p in the absence of S2p ( Figure 4A–C ) is consistent with recent reports identifying an abundance of primed genes [34] , [37]–[39] marked by RNAP-S5p and not RNAP-S2p [21] in embryonic stem cells or differentiated cells . To investigate to what extent S2p sites are also marked by S5p , we used an antibody-blocking assay ( [30] , [40]; Figure 4D–G ) , in which sections were first incubated with antibodies against RNAP-S5p before incubation with antibodies against RNAP-S2p . Simultaneous incubation with the two antibodies resulted in a ∼65% quenching of the detection of RNAP-S2p ( images unpublished ) , due to a more efficient binding of 4H8 , an IgG , in comparison with H5 , an IgM . The rationale of antibody blocking experiments is that the binding of the first antibody prevents binding by the second , if the two respective epitopes are located within the distance corresponding to the size of the first bound antibody complex ( Ser2 and Ser5 residues are separated by two aminoacids whereas IgGs are large proteins that measure ∼9 nm ) . After pre-incubation with the specific S5p antibody ( 4H8 ) , the overall intensity of RNAP-S2p sites was significantly reduced throughout the nucleoplasm , except in discrete interchromatin domains ( Figure 4E , 4G ) , as compared to sections not incubated with this antibody ( Figure 4D , 4G ) or to sections incubated with an unrelated ( anti-biotin ) antibody ( Figure 4F , 4G ) . A transcriptionally silent population of RNAP-S2p complexes are known to be stably accumulated in splicing speckles [33] , [41] , which are nuclear domains enriched in splicing machinery , polyA+ RNAs , and may be important for post-transcriptional splicing of complex RNAs [42] . The reverse antibody-blocking experiment also confirmed the colocalisation between S2p and S5p sites but produced lower levels of signal depletion ( unpublished ) , as expected due to the larger abundance of S5p sites ( Figure 4C ) . The results from antibody-blocking experiments suggest that most nucleoplasmic S2p sites outside interchromatin clusters also contain the S5p modification , as expected for simultaneous initiation and elongation events on the same gene during cycles of active transcription . Furthermore , S5p-containing structures are in excess of the active factories , demonstrating the presence of discrete sites marked solely by S5p , which represent poised , S5+S2p− transcription factories . We have shown large scale repositioning of the uPA locus following TPA treatment of HepG2 cells ( Figure 1D ) . The short genomic separation between uPA gene and neighbouring genes , CAMK2G and VCL ( 40 kb and 80 kb , respectively; Figure 5A ) , led us to investigate in more detail how the TPA treatment affected the transcriptional state of the three genes , by comparing the levels of unprocessed transcripts and their association with S5p and S2p factories ( Figure 5 ) . The levels of primary transcripts , produced before and after TPA treatment , were determined by qRT-PCR with primers that amplify the exon1-intron1 junction , using total RNA extracted from HepG2 cells ( Figure 5B ) ; cells were treated in parallel with α-amanitin , an inhibitor of RNAP transcription , to discriminate populations of newly made from stable transcripts . Abundant detection of primary transcripts above α-amanitin levels shows that CAMK2G and VCL are actively transcribed prior to TPA activation , whereas uPA primary transcripts are weakly transcribed ( Figure 5B; see also Figure 1B , 1C ) . The levels of CAMK2G and VCL primary transcripts decrease by 2 . 8-fold and increase by 1 . 5-fold , respectively , upon TPA treatment , whereas uPA primary transcripts increase by ∼11-fold ( Mann-Whitney U test , p = 0 . 05 for the three genes; n = 3 independent replicates ) . Analysis of spliced transcripts of CAMK2G and VCL confirms their active state prior to TPA induction and demonstrates similar effects upon activation ( unpublished ) . Interestingly , low levels of uPA primary transcripts sensitive to α-amanitin treatment are detected prior to activation ( Figure 5B ) , consistent with the detection of uPA protein in a small percentage of HepG2 cells before TPA treatment ( Figure 1C ) . The small and disparate changes in the RNA levels of the two genes flanking uPA are in line with a recent investigation of the Hoxb cluster in mouse ES cells , but occur at much shorter genomic distances , in which the Cbx1 gene , 400 kb downstream of the Hoxb cluster , does not change expression levels in spite of increased chromatin repositioning relative to the CT [36] . The behaviour of the uPA flanking genes also agrees with a broader analysis of expression changes across a whole 300 kb region , which undergoes repositioning in response to murine transgenic integration of the β-globin locus-control region , where the expression levels of many genes do not change between the two states [43] . As the levels of primary transcripts at each gene in the locus before and after TPA induction may depend on complex parameters such as the frequency and speed of RNAP elongation , the stability of unprocessed transcripts , and the rate of intron splicing , we investigated whether TPA activation influenced the levels of association of each gene with S5p and S2p sites , using fosmid probes that cover ∼42–46 kb of genomic sequence ( Figure 5A ) . Measurements of the diameters of fosmid and BAC signals yielded average values of 353 nm for the uPA fosmid , in comparison with 586 nm for the BAC probe , which demonstrates a significant improvement in spatial resolution . We find that CAMK2G and VCL are extensively associated with both S5p and S2p sites and to a similar extent , irrespective of TPA treatment ( association frequency between 62% and 82%; Figure 5C , 5D ) . Importantly , the relatively small changes in the levels of primary CAMK2G and VCL transcript upon TPA treatment ( Figure 5B ) are not reflected by detectable changes in their association with either S5p or S2p sites . This suggests that TPA activation does not influence the extent of CAMK2G and VCL association with the transcription machinery , and thus their state of activity is unlikely to have a major role in the relocation of the uPA locus from its territory . Similar analyses of uPA gene association with S5p and S2p sites using a fosmid probe ( Figure 5C , 5D ) confirms the results obtained with the larger BAC probe ( Figure 3 ) . Prior to induction , the gene is extensively associated with S5p sites ( 79%±5%; n = 91; Figure 5C ) , but not with S2p sites ( 36%±6%; n = 254; χ2 test , p<0 . 0001; Figure 5D ) . Upon activation , the uPA fosmid probe associates with S5p and S2p sites to a similar extent ( i . e . , 75%±9% and 71%±5% , n = 93 and 225 , respectively; χ2 test , p = 0 . 48; Figure 5C , 5D ) and at the same levels observed with the BAC probe ( ∼70%; Figure 3F ) . Analyses of simulated fosmid signals ( Figure S3D , S3E ) support the notion that the association of fosmid signals with S5p sites prior to induction , or with both S5p and S2p after activation , are not explained by random processes ( χ2 test comparisons between experimental and simulated association pS5p/−TPA = 0 . 0007 , pS5p/+TPA = 0 . 014 , pS2p/+TPA<0 . 0001 ) , whereas the association with S2p sites prior to induction can be ( pS2p/−TPA = 0 . 62 ) . We were surprised to find that the smaller fosmid probe associates with S2p sites prior to induction to the same extent as the larger BAC probe that also covers the active flanking genes , CAMK2G and VCL ( 36% and 31% , respectively , χ2 test , p = 0 . 26 ) . Simultaneous detection of fosmid and BAC probes in combination with S2p detection ( Figure S4B , S4C ) , confirmed that fosmid and BAC probes associate with S2p sites to a similar extent prior to activation ( 37%±9% and 28%±9% , respectively; n = 46; χ2 test , p = 0 . 37; Figure S4C ) . Unexpectedly , whilst performing these analyses , we observed that a small proportion of uPA loci detected with the fosmid probe ( 15%±3% and 20%±8% , n = 47 and 41 , respectively for − and +TPA; χ2 test , p = 0 . 57; Figure S4D ) were looped out from the signal labelled by the BAC probe independently of TPA activation , in a manner reminiscent of loci looping out from their CTs , but on a much smaller genomic length scale ( [4] , [5]; Figure 1D ) . This mechanism provides a rationale for the independent behaviour of neighbouring genes with respect to their association with specific nuclear landmarks , such as shown here for the association with specific RNAP structures . The fosmid-based analyses allowed us to confirm at higher spatial resolution that the uPA gene is preferentially associated with a subpopulation of RNAP factories , which , prior to induction , contain RNAP-S5p , but not RNAP-S2p . After induction the uPA locus is highly associated with both S5p- and S2p-containing RNAP sites , consistent with its active state . In order to investigate whether the extent of uPA gene association with active transcription factories reflects their transcriptional activity , we combined the detection of the uPA locus by DNA-FISH with the visualisation of uPA transcripts by RNA-FISH using five tagged oligoprobes mapping at introns 4 , 5 , and 10 , and exons 8 and 9 . We find that the locus is already transcriptionally active prior to activation , with 13%±8% of uPA alleles showing an association with RNA-FISH signals ( n = 200 loci; Figure 6A ) , consistent with the detection of uPA protein and transcripts before induction ( Figures 1C and 5B , respectively ) . RNase control experiments confirmed the specificity of the discrete RNA-FISH signals observed within the nucleus ( Figure S5 ) . We also show that the frequency of active alleles increases 2-fold , to 27%±10% , after TPA treatment ( n = 216; χ2 test comparison for − and +TPA , p = 0 . 0022; Figure 6A ) , in agreement with the 2-fold increase observed in the extent of uPA association with RNAP-S2p ( Figure 4B ) . The extent of uPA gene association with RNA signals after activation ( 27% ) is consistently smaller than its association with S2p sites ( 70%; Figure 5D ) . However , it must be considered that the efficiency of detection of newly made transcripts at the site of transcription depends on the abundance of RNAP loading at each single gene , the stability of newly made transcripts at the site of synthesis , and the rate of splicing , and therefore is likely to provide a lower estimate for the frequency of gene activity . Intron lengths at the uPA gene are at most ∼900 bp , and small introns can be promptly removed within seconds of synthesis . In contrast , the level of uPA gene association with S2p sites can provide a higher estimate , with the caveat that these levels of association may also reflect , in part , an indirect colocalisation of uPA loci with transcription factories associated with the flanking genes . To verify whether detection of newly synthesized uPA transcripts at the uPA locus occurred concomitantly with its association with active , S2p factories , we performed triple labelling experiments in which we simultaneously detected the uPA locus , uPA transcripts , and S2p active factories ( Figure 6B ) . We found that most uPA loci associated with an RNA signal are also associated with S2p sites both before and after TPA treatment ( 76% and 71% , n = 70 and 75 , respectively; χ2 test , p = 0 . 80; Figure 6B ) , confirming that S2p sites are active sites of transcription . As expected from the higher levels of uPA gene association with S2p than RNA-FISH sites , we find that of all the uPA loci associated with S2p sites ( ∼70% ) only half are also associated with uPA transcripts ( unpublished; see also [36] ) . This difference is likely to reflect technical limitations in the detection of transcripts of short genes containing only small introns . Our analyses of uPA gene association with different phosphorylated forms of RNAP and with newly made transcripts show that the vast majority of uPA alleles are associated with poised S5p+S2p− transcription factories prior to activation . We also identify a small population of alleles transcribed at low levels prior to activation and predominantly associated with sites that are marked by S2p ( Figures 5B and 6B ) . Upon activation , uPA alleles become associated with RNAP sites marked by both S2p and S5p . We consistently find a 2-fold increase in the association of the uPA gene association with S2p sites or uPA transcripts ( Figures 3C , 3F , 5D , and 6A ) , identifying an increased frequency of transcription upon TPA induction . We next investigated whether the increased frequency of uPA gene transcription or association with transcription factories were dependent on the locus position relative to its CT , both before and after activation , when the locus is preferentially located at the CT interior and exterior , respectively . We performed triple labelling cryoFISH experiments for chromosome 10 , the uPA locus , and transcription factories , before and after activation ( Figure 7A–C ) . Analyses were initially performed with BAC probes , but also confirmed with fosmid probes ( Figure S6 ) . As previously observed in double labelling experiments ( Figure 1D ) , the uPA locus was preferentially located at the CT interior and associated with S5p transcription factories before activation ( Figure 7B ) . TPA activation induced the relocation of most uPA loci to the CT exterior and an association with factories marked with both S5p and S2p ( Figure 7B ) . To determine whether the association of the uPA locus with poised or active factories was dependent on its position relative to the CT , we calculated the proportion of uPA locus association with RNAP at each CT position ( Figure 7C ) . For simplicity , the data for the three regions around the edge of the CT ( “inner-edge , ” “edge , ” and “outer-edge” ) were pooled into a single region , but analyses of the five regions gave similar results . Surprisingly , we found that the association of the uPA locus with S5p occurred with similar frequency at all locations relative to the CT , independently of TPA activation ( n = 90 and 134 loci , respectively; logistic regression analysis , p = 0 . 20; Figure 7C ) . This shows that the CT interior is accessible to the transcription machinery and does not preclude the interaction of a primed gene with poised , S5p+S2p− factories . In the case of S2p , we also found that the uPA locus associates with active transcription factories with similar frequencies across the different CT regions before and after TPA activation ( n = 151 and 104 , respectively; logistic regression analysis , p = 0 . 10 ) . The increase in association of uPA loci with active factories marked by S2p after TPA is statistically significant ( p<0 . 0001 ) , but the effect of TPA on the level of association is the same across all positions relative to the CT ( p = 0 . 62; Figure 7C ) . These results show that the uPA gene associates with poised or active transcription factories with similar frequencies across the different CT regions both before and after transcriptional activation . Therefore , looping of the uPA locus out of its CT is not required for the association of the uPA gene with active transcription factories . To investigate whether the large-scale chromatin movements that accompany TPA induction of the uPA gene had an effect on the association of the flanking genes , CAMK2G and VCL , with active ( S2p ) factories , we performed triple labelling cryoFISH experiments for chromosome 10 , the CAMK2G , or VCL loci detected with fosmid probes and active ( S2p ) factories ( Figure S7 ) . We find that the association of CAMK2G or VCL with S2p also occurs with similar frequency at all locations relative to the CT , both before and after TPA activation ( logistic regression analysis , p = 0 . 84 and 0 . 64 for CAMK2G and VCL , respectively; nCAMK2G/−TPA = 108 , nCAMK2G/+TPA = 128 , nVCL/−TPA = 147 , nVCL/+TPA = 134 ) . These results show that the association of the uPA , CAMK2G , and VCL genes with RNAP-S2p occurs independently of CT position . A recent analysis of gene activation induced by the insertion of a strong ( ß-globin ) enhancer in a gene rich-region also showed no effect on the frequency of locus association with active transcription factories at different positions relative to the CT [43] , although this region preferentially localises at the CT edge . In the case of the murine Hoxb locus , a small preferential association of Hoxb1 and flanking genes with active transcription factories is observed outside the CT upon retinoic acid treatment [36] . Different mechanisms of gene regulation may act on different genes and depend on the kinetics of induction over the shorter activation ( 3 h ) of the uPA gene by TPA treatment in comparison with retinoic acid treatment for several days to induce Hox genes . Finally , to investigate whether the CT position of the uPA gene has an influence on its transcriptional activity , we labelled the uPA gene , chromosome 10 , and uPA transcripts simultaneously ( Figure 7D , 7E ) . We used the fosmid uPA probe for highest spatial resolution . We find that the uPA gene is transcribed with the same frequency irrespectively of its CT position upon TPA activation ( logistic regression analysis , p = 0 . 74; n = 100 ) . These results differ from the murine Igf2bp1 and Cbx1 genes , flanking the Hoxb cluster , which are also transcriptionally active at all CT positions , independently of Hoxb induction , but are preferentially active outside the CT [36] . Difficulties in the detection of the Hoxb1 transcripts did not allow a similar analysis of allelic transcription upon induction [36] , to help establish how general the correlation is between gene positioning outside the CT and transcriptional states . Prior to activation , we unexpectedly found that the largest fraction of uPA loci , which are internal to the CT , are less likely to be transcriptionally active ( logistic regression analysis , p = 0 . 0001; n = 103 ) , whereas the smaller proportion of uPA loci not located at the preferred internal CT position is transcribed at the same frequency as upon TPA induction ( Figure 7E ) . These results suggest that the internal CT positioning has a silencing effect on the primed uPA locus prior to its induction , which helps prevent transcript elongation or interferes with transcript stability , revealing unexpected properties of locus positioning within the nuclear landscape . In summary , our analyses of the uPA gene prior to induction showed that it was ( a ) preferentially positioned at the interior of its CT; ( b ) in a poised state , characterized by open chromatin configuration and the presence of RNAP-S5p at regulatory regions; and ( c ) preferentially associated with poised , S5p+S2p− transcription factories . Transcriptional activation induces large-scale relocation of the gene towards the CT exterior and a preferred association with factories containing both ( S5p and S2p ) RNAP modifications , as expected in the active state . Although the correlation between looping out of the CT and the change in RNAP configuration suggested that the external position might favour transcriptional activation , triple-labelling experiments showed that the position of the uPA locus relative to its CT and the association with poised or active transcription factories are independent events . RNA-FISH experiments confirm that after TPA induction both external and internal positions of the uPA gene , with respect to its CT , are equally competent for transcription . However , positioning of the uPA locus inside the CT , before activation , may help control the levels of transcription , as uPA genes that are found outside of the CT before TPA treatment are more likely to be transcribed ( Figure 7E ) . Our findings reinforce the idea that the interior of CTs is not repressive for the association of genes with transcription machinery , suggesting that large-scale chromatin movements are unlikely to be necessary for genes to find transcription factories , although they may influence the extent of association for specific subsets of genes . This study expands current models of gene regulation by showing that silent genes can be associated with poised transcription factories and that factory association and gene position relative to the CT can be independent factors . Our results are also compatible with the notion that poised transcription factories represent a sub-population of specialized sites that may allow primed genes to respond rapidly and efficiently to specific activation signals .
HepG2 cells were cultured in the absence or presence of 100 ng/ml TPA ( Sigma ) for the indicated times as previously described [27] . Treatment of HepG2 cells with 1 µM flavopiridol ( 1 h; Sanofi-Aventis ) was used for the inhibition of RNAP-S2p phosphorylation by CDK9 , and 75 µg/ml α-amanitin ( 5 h; Sigma ) to inhibit RNAP transcription . For the quantification of mature and unprocessed transcript levels of uPA , CAMK2G , or VCL genes , total RNA was extracted and amplified by RT-PCR . Western blotting was performed using total HepG2 protein extracts and antibodies specific to different RNAP phosphoforms . Experimental details and information about the antibodies used can be found in Text S1 . Chromatin cross-linking , MNase digestion , and immunoprecipitation were performed as described previously [31] . See Text S1 for primer sequences ( Table S1 ) , antibodies used , and experimental details . uPA protein expression was detected with specific rabbit antiserum antibodies . For high-resolution imaging using cryoFISH , ultrathin cryosections ( ∼140–150 nm thick ) were immunolabelled and/or labelled by fluorescence in situ hybridization ( FISH ) essentially as described before [14] . RNA-FISH was performed using oligonucleotide probes ( http://www . singerlab . org/protocols ) . See Text S1 for information about the antibodies and probes used , and for experimental details . Images were acquired by confocal microscopy and analysed quantitatively . Statistical analyses were performed using χ2 test , logistic regression analysis , ANOVA , Student t-test , or Mann-Whitney U test . See Text S1 for further details .
|
The spatial organization of the genome inside the cell nucleus is important in regulating gene expression and in the response to external stimuli . Examples of changing spatial organization are the repositioning of genes outside chromosome territories during the induction of gene expression , and the gathering of active genes at transcription factories ( discrete foci enriched in active RNA polymerase ) . Recent genome-wide mapping of RNA polymerase II has identified its presence at many genes poised for activation , raising the possibility that such genes might associate with poised transcription factories . Using an inducible mammalian gene , urokinase-type plasminogen activator ( uPA ) , and a system in which this gene is poised for expression , we show that uPA associates with poised transcription factories prior to activation . Gene activation induces two independent events: repositioning towards the exterior of its chromosome territory and association with active transcription factories . Surprisingly , genes inside the interior of the chromosome territory prior to activation are less likely to be actively transcribed , suggesting that positioning at the territory interior has a role in gene silencing .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/chromatin",
"structure",
"molecular",
"biology/histone",
"modification",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"cell",
"biology/nuclear",
"structure",
"and",
"function",
"genetics",
"and",
"genomics/gene",
"expression",
"molecular",
"biology/nucleolus",
"and",
"nuclear",
"bodies",
"biochemistry/chemical",
"biology",
"of",
"the",
"cell",
"molecular",
"biology/transcription",
"elongation",
"biochemistry/macromolecular",
"assemblies",
"and",
"machines",
"mathematics/statistics",
"biochemistry/transcription",
"and",
"translation",
"genetics",
"and",
"genomics/epigenetics",
"cell",
"biology/gene",
"expression"
] |
2010
|
Poised Transcription Factories Prime Silent uPA Gene Prior to Activation
|
One of the central goals of developmental neurobiology is to describe and understand the multi-tiered molecular events that control the progression of a fertilized egg to a terminally differentiated neuron . In the nematode Caenorhabditis elegans , the progression from egg to terminally differentiated neuron has been visually traced by lineage analysis . For example , the two gustatory neurons ASEL and ASER , a bilaterally symmetric neuron pair that is functionally lateralized , are generated from a fertilized egg through an invariant sequence of 11 cellular cleavages that occur stereotypically along specific cleavage planes . Molecular events that occur along this developmental pathway are only superficially understood . We take here an unbiased , genome-wide approach to identify genes that may act at any stage to ensure the correct differentiation of ASEL . Screening a genome-wide RNAi library that knocks-down 18 , 179 genes ( 94% of the genome ) , we identified 245 genes that affect the development of the ASEL neuron , such that the neuron is either not generated , its fate is converted to that of another cell , or cells from other lineage branches now adopt ASEL fate . We analyze in detail two factors that we identify from this screen: ( 1 ) the proneural gene hlh-14 , which we find to be bilaterally expressed in the ASEL/R lineages despite their asymmetric lineage origins and which we find is required to generate neurons from several lineage branches including the ASE neurons , and ( 2 ) the COMPASS histone methyltransferase complex , which we find to be a critical embryonic inducer of ASEL/R asymmetry , acting upstream of the previously identified miRNA lsy-6 . Our study represents the first comprehensive , genome-wide analysis of a single neuronal cell fate decision . The results of this analysis provide a starting point for future studies that will eventually lead to a more complete understanding of how individual neuronal cell types are generated from a single-cell embryo .
When establishing the nematode C . elegans as a genetic model system , Sydney Brenner's stated goal was the identification of genes required to build a nervous system [1] , [2] . Several decades of investigation , mainly using the classic forward screening methods established by Brenner [1] , have indeed revealed genes that are required to generate a diversity of individual neuron types [3] . Yet our understanding of nervous system development still remains spotty . The advent of RNAi technology in C . elegans has opened an alternative path to classic forward analysis to study the genetic bases of various processes [4] , [5] . However , this approach has not been undertaken in C . elegans to comprehensively study neuronal development and fate determination . In this paper we utilize the RNAi approach to screen , at a genome-wide level , for factors involved in all aspects of neuronal development . We are using the ASE gustatory neuron class , composed of two bilaterally symmetric neurons , ASEL and ASER , as a paradigm to understand how neuronal fate is specified . Each neuron is produced by two distinct descendants of the embryonic blastomere AB ( Figure 1; [6] ) called ABa ( generates ASEL ) and ABp ( generates ASER ) . There is a progressive restriction in the types of cells produced by the AB blastomere . The great-granddaughter of AB , ABalp , still produces cells from two different germ layers ( meso- and ectoderm ) , while one of its daughters , ABalpp only produces neurons and glia . ABalpp's posterior daughter , ABalppp , then becomes committed to only produce neurons , one of them the left ASE neuron . Similar progressive restrictions of fate occur along the ABp lineage that generates the right ASE neuron . Little is known about genes that control these lineage restrictions . The RNAi screen we present here has uncovered a basic helix-loop-helix ( bHLH ) transcription factor , hlh-14 , that we characterize in detail in this study . We find hlh-14 to be crucial for the specification of neurons that derive from several lineage branches , including the ASE neurons . Once a lineage is committed to only produce neurons ( ABalpppp for ASEL and ABpraaap for ASER ) , fates have to be differentially segregated to generate distinct types of neurons . At this stage , as well as at earlier stages , a binary Wnt-signaling system operates to differentially segregate fates via the anterior-posterior differential distribution of the transcriptional regulators pop-1 and sys-1 [7] . These factors presumably interact with stage- and lineage-specific transcription factors to produce distinct cell types [7] , [8] . One intriguing aspect of neuronal lineage specification in C . elegans is that even though many neurons in the nervous system come as bilaterally symmetric pairs , the lineage of the individual cells that make up a pair can be remarkably distinct [9] . In other words , cells that have limited lineage relation can produce remarkably similar neurons . The pair of ASE neurons falls into this category . As stated above , the left ASE neuron derives from ABa and the right neuron from ABp , which are asymmetrically localized in the early embryo . However , 4 cell divisions later , at the beginning of gastrulation , the ABa and ABp descendants that generate ASEL and ASER ( called ABalppp and ABpraaa ) , begin to undergo left/right symmetric cleavage patterns to generate ASEL and ASER ( Figure 1B ) . During gastrulation the descendants of these cells migrate into left/right bilaterally symmetric positions [10] . How this bilateralization process is regulated at the levels of cell fate and cell morphogenesis is not known . The correct specification of the ASE neurons involves not only the imposition of ASE neuron identity that is shared by both the left and the right ASE neuron , but also involves the retention of the distinct lineage history of the two neurons . Even though morphologically largely bilaterally symmetric , both ASE neurons express a distinct set of chemosensory receptors and respond differently to distinct sensory cues [11] . The differential , left/right asymmetric expression of these receptors depends on a Notch-mediated signal , received in the early embryo by the lineage that produces ASEL ( Figure 1B–1C; [12] ) . This Notch-mediated signal results in a differential and transient expression of a pair of paralogous T-box genes , tbx-37/38 [13] , and then , several rounds of cell division later , in the expression of the miRNA lsy-6 in the ASEL , but not the ASER neuron [12] . How the information is transmitted from Notch , via tbx-37/38 , to lsy-6 restriction is not known . The lsy-6 miRNA has been uncovered through genetic screens for mutants in which the expression of ASEL- and ASER-specific chemoreceptors is disrupted [14] . These screens have uncovered an important principle of ASEL/R laterality through the identification of so-called Class I “2-ASEL” and Class II “2-ASER” mutants [15] . In Class I mutants , both neurons adopt ASEL fate ( as assessed by expression of ASEL-specific gcy chemoreceptors ) , while losing ASER fate; in Class II mutants the opposite happens in that both neurons adopt ASER fate and lose ASEL fate [16] . The molecular basis for this bistability is a double-negative feedback loop of transcription factors and at least one miRNA , the above-mentioned lsy-6 miRNA ( Figure 1C; [16] , [17] ) . Through genetic epistasis analysis , we have found the earliest trigger of asymmetry to be the expression of the lsy-6 miRNA [16] . Yet , it is not known how its expression is restricted to ASEL . Our screen has uncovered a novel regulator of ASE asymmetry , the chromatin methlytransferase complex COMPASS . Through analysis of known and novel mutants in components of this complex we show that the COMPASS complex acts during embryogenesis , before the birth of the ASE neurons , to regulate lsy-6 expression . As such , it provides a potential molecular link between the early Notch-signal , tbx-37/38 expression and ASEL-specific lsy-6 expression in the maturing ASE neurons . Taken together , studying the development of the ASE neurons provides a window to understanding a host of questions relevant to neuronal differentiation – how are early lineage decisions made , how are symmetry and asymmetry imposed , how are individual neuronal cell fate decisions controlled ? Previous genetic mutant screens that used ASE differentiation markers as a read-out have begun to address these questions , but these genetic screens have not yet been saturated [18] . We sought to further expand the list of genes required to build an ASE neuron . To facilitate the recovery of lethal genes and to avoid the problem of mutational hotspots , we have used a genome-wide RNAi screen to reach this goal . To our knowledge , this is the first genome-wide RNAi analysis of a single neuron cell fate decision .
RNAi is known to work less efficiently in the nervous system than in other tissues [19] . Indeed , we find that dsRNA directed against gfp barely reduces gfp expression levels in transgenic animals that drive a gfp reporter in the ASEL neuron ( Figure 2A ) . Several genetic backgrounds have been described in which animals are more sensitive to RNAi . We tested three of these backgrounds , nre-1 lin-15b [20] , eri-1; lin-15b [21] and rrf-3 [22] . We find that in all of them ASE-specific gfp expression is now significantly reduced , with nre-1 lin-15b and eri-1; lin-15b mutants allowing more effective gfp knockdown than rrf-3 ( Figure 2A ) . Previous mutant analysis from our lab has identified a number of genes required for the correct terminal differentiation of the ASE neuron ( Figure 2A–2B; [18] ) . We tested whether RNAi recapitulates the mutant phenotype of 11 of these genes . We find that in a non-sensitized background , RNAi of only 3 of the genes partially recapitulates the mutant phenotype . In rrf-3 mutants , the effects of knockdown improved for only some genes , but in both nre-1 lin-15b and eri-1; lin-15b mutants , RNAi phenocopies the effect of the respective mutant in most cases examined ( Figure 2A–2B ) . Since eri-; lin-15b mutants generally seem less healthy than nre-1 lin-15b mutants , we chose the latter strain for our genome-wide screen . As the nre-1 lin-15b mutant background is relatively untested for its overall sensitivity to RNAi in general , we screened all RNAi clones on chromosome I not just for ASE neuronal phenotypes ( as we did for all chromosomes ) , but also for obviously visible phenotypes , ( sterility , embryonic or larval lethality , slow post-embryonic growth , or post-embryonic morphological defects; see Materials and Methods ) and compared those to previously reported chromosome I screens of visible phenotypes , conducted either in a wildtype or an rrf-3 mutant background ( Figure 2C; [22] , [23] ) . We found a total of 779 clones with at least one visible phenotype detected , with an overlap of 82% and 88% for the rrf-3 and wildtype Bristol N2 screens respectively . When compared to the group of clones for which phenotypes were reported in both rrf-3 and wildtype N2 screens , our screen reveals 95% overlap . When we specifically compared the phenotypic categories reported by others and us , we found a striking trend of genes being shifted into more severe phenotypic categories in the nre-1 lin-15b background . For example , of the 303 clones reported previously to produce a Gro phenotype ( slow growth ) , we found 57% to also be Gro in our screen but an additional 22% were found in more severe non-viable categories ( Figure S1 ) . Using the nre-1 lin-15b sensitized background and the ASEL-marker lim-6::gfp ( otIs114 transgene ) , we then went on to conduct a primary screen of all available clones . We were able to screen 15 , 395 RNAi clones for ASEL defects , equaling 81% of genes in the genome . In the primary screen , all clones were scored in duplicate , revealing that 748 RNAi clones affect development of the ASEL neuron , as evidenced by loss or ectopic expression of the lim-6::gfp marker . These 748 clones were re-screened 6 additional times and we set the arbitrary threshold of considering any clone that repeated at least 3/8 times as positive . These criteria identified a total of 245 genes that affect the development of the ASEL neuron ( Figure 3 ) . Examples of some of the knockdown phenotypes are shown in Figure 4 ( showing either the RNAi phenotype or mutant alleles that confirm the RNAi phenotype ) . The vast majority of these genes ( 237/245 ) cause embryonic lethality , in addition to the ASEL phenotype . Of these 245 genes , 21 genes resulted in ectopic expression of the ASEL marker and 210 genes resulted in a loss of ASEL marker expression when knocked-down . 14 genes displayed a mixed phenotype , i . e . individual animals within a population of treated animals show either no phenotype , loss or ectopic expression of the ASEL marker ( this category was not further pursued; Table S1 ) . The RNAi screen identified the hlh-14 gene as affecting overall differentiation of ASEL/R . hlh-14 RNAi animals are viable and display a loss of asymmetrically expressed and bilaterally expressed genes in ASE to varying extents ( Table 5 ) . We first set out to corroborate the RNAi phenotype using available mutant alleles of hlh-14 . We found that two different alleles of hlh-14 recapitulated the ASE differentiation phenotype observed upon RNAi and were larval lethal as previously reported [28] . Asymmetrically expressed genes are lost in hlh-14 mutants , as is the expression of che-1 , a terminal selector of ASE neuron differentiation ( Figure 5A and Table 5 ) . The defects are almost completely penetrant in hlh-14 ( tm295 ) , a deletion allele that removes the entire bHLH domain and is likely a functional null . We find that other neurons that are lineally related to ASE are also affected in hlh-14 mutants , as assessed by loss of terminal fate marker expression of the OLL and AFD neuron pairs . These broader neuronal defects were not apparent during initial RNAi screening since the dopaminergic and cholinergic neuron marker was unaffected and hlh-14 ( RNAi ) animals are viable , unlike hlh-14 mutants which are larval lethal . Intriguingly , the effect on fate appears to be more pronounced the more posterior a neuron is located in the lineage ( Figure 5A and Table 5 ) . We analyzed the hlh-14 mutant phenotype in more detail using a classic lineaging approach , in which we monitored cell position , cleavage and morphology , using 4D microscopy [10] . We find that from the AB64-cell stage onwards , during gastrulation , the ABalpppp/ABpraaap neuroblasts and their descendants migrate dorsally , towards a location usually populated by hypodermal cells [6] , rather than remaining more ventrally positioned as they do in wild-type animals . By the AB256-cell stage these descendants are both dorsally and posteriorly mislocalised ( Figure 5B ) . At this stage , the time at which hypodermal cells normally cease division and differentiate , these descendants fail to divide further , unlike wild-type cells , which undergo one or two more rounds of division before differentiating into neurons . Furthermore , in certain cases where we were able to follow the cells for a sufficient period of time , we observed that they acquired a hypodermal-like cell morphology . We observe more frequent hypodermal transformations and mis-positioning defects in more posterior descendants of the ABalpppp/ABpraaap neuroblasts ( Figure 5B ) . Additionally , we examined the hypodermal cell fate marker dpy-7 in hlh-14 ( tm295 ) mutants at the comma stage ( Figure S3 ) . Although we were able to easily observe additional cells in the posterior of the embryo , consistent with previously reported cell fate transformations [28] and novel tranformations that we describe below , we were unable to unambiguously identify extra hypodermal cells in the anterior of the embryo due to the dim expression of dpy-7 in certain anterior hypodermal cells ( we note that several anterior hypodermal cells , in addition to the hypodermal seam cells , do not appear to express dpy-7 at this stage ) . Nevertheless we were able to observe mispositioned hypodermal cells in the anterior of the hlh-14 ( tm295 ) mutants , where the transformed cells of the ABalpppp/ABpraaap reside ( Figure S3 ) . The division and dorsal mispositioning defects are more severe in the null allele of hlh-14 ( Figure 5B ) . Together , these data are consistent with a neuron-to-hypodermal transformation of these cells and suggest that , in addition to cell fate , hlh-14 regulates aspects of cell migration . To examine hlh-14 gene expression , we generated a YFP-tagged hlh-14 transgene through fosmid recombineering ( Figure 5C; [29] ) . This fosmid reporter fully rescues the lethality , division , morphogenesis and ASE cell fate defects of hlh-14 mutants ( Figure 5B and Table 5 ) . We find , using 4D-lineage analysis , that this reporter and a previously published hlh-14 translational reporter [28] show similar expression in the ASE lineage . hlh-14 is first expressed at the AB64-cell stage ( Figure 5B ) . This is just shortly after the point at which the ASE-lineages , which descend from distinct asymmetric blastomeres , become symmetric in terms of division pattern and daughter cell fates; the descendants will form exclusively neurons and glia , producing the left-right bilateral homologs of eleven pairs of neurons and two pairs of glia ( Figure 1 ) . It is also the time we begin to see morphogenesis defects and together is consistent with a role of hlh-14 being a bilateral binary switch at , or shortly after the ABalpppp/ABpraaap stage , to determine neuronal versus hypodermal fate . hlh-14 expression appears to persist longer in posterior parts of the ABalppp/praaa lineage than in anterior parts , thereby mirroring the differential phenotypic severity of hlh-14 removal ( Figure 5A–5B ) . In the ASE branch , located at the posterior part of the lineage , hlh-14 expression is maintained throughout the lineage until shortly before the terminal division of the ASE neurons , suggesting not only early neuronal versus hypodermal decision roles for hlh-14 but perhaps also a later role in differentiation . Consistent with such a notion we find that on examining ASEL , partial removal of hlh-14 , through RNAi in animals that express two distinct markers for ASEL fate simultaneously , sometimes leads to loss of one but not the other marker ( 13/28 embryos in which ASEL was still present , as assessed by a lsy-6::yfp reporter , lost ceh-36::mCherry expression ) suggesting the ASE cell is still produced but fails to differentiate correctly . In addition to its expression in the ASE lineage , we observe by 4D-lineage analysis that our hlh-14 fosmid reporter is expressed in several other neuronal lineages . In some instances this expression is bilaterally symmetric , such as in the neuroblasts that give rise to the L/R bilaterally symmetric PLM/ALN neuronal pairs , PVQ/HSN/PHB neuron pairs and the ALM/BDU neuron pairs ( Figure S4 ) . Surprisingly , in other instances this expression is L/R asymmetric , such as in neuroblasts of the C lineage ( Figure 6 ) . We observe asymmetric expression in two asymmetrically generated neuroblasts , the Caapa neuroblast that gives rise to the DVC neuron and a cell death , and the Caappv cell , which differentiates into the neuron PVR . The bilateral homologs of these hlh-14-expressing neuroblasts are non-neuronal hypodermal cells ( Figure 6C ) . In both hlh-14 ( gm34 ) and hlh-14 ( tm295 ) mutants we observe that the Caapa neuroblast divides precociously , indicative of a hypodermal transformation . In the position of Caapaa , we observe an ectopic cell expressing the dpy-7 hypodermal cell marker , corroborating that PVR has been transformed into hyp11 , the normal fate of Caappv's bilateral homolog ( Figure 6C ) . These division defects are rescued by the hlh-14FOS::yfp transgene ( Figure 6C ) . Several other lineages in which we observe hlh-14 expression also give rise mainly to hypodermal cells , the P-cells in the case of the PLM/ALN and PVQ/HSN/PHB lineages ( Figure S4B ) and the V-cells in the case of the ALM/BDU lineage ( Figure S4C ) . In the PVQ/HSN/PHB lineage we also observe precocious division of the neuroblast that gives rise to these neurons and ectopic dpy-7 expressing cells in the tail in the absence of hlh-14 ( Figure S3; data not shown ) . We suggest that hlh-14 acts as a neural/hypodermal switch in the neuroblasts of all these lineages . Intriguingly , we also observe expression in the bilaterally symmetric neuroblasts that produce a pair of pharyngeal interneurons known as I1L/I1R ( Figure S5 ) . The lineages that generate these neurons are unusual for two reasons: ( 1 ) they derive from L/R asymmetric origins and ( 2 ) they are multi-potent , giving rise to mesodermal , epithelial and neuronal cells of the pharynx . We find that in hlh-14 ( tm295 ) mutants the division of this neuroblast is again premature , more closely matching the division time of its sister , which does not express hlh-14 and normally gives rise to two myoepithelial cells , a muscle and a marginal cell ( Figure S5B ) . While it is currently unclear what fate these cells acquire in the absence of hlh-14 , it suggests that in addition to regulating neural/hypodermal fate decisions , hlh-14 regulates more general neural/non-neural cell fate decisions and that “non-clonally” derived neurons use the same basic neural specification mechanisms as clonally derived neurons . Finally , we observe later expression of hlh-14 in currently unidentified lineages , at a time in which all terminally differentiated cells present at hatching are already formed . This is consistent with hlh-14 acting during terminal cell fate decisions in other lineages . Taken together , hlh-14 acts as a proneural gene to regulate early neuroblast cell fate , morphogenesis and certain aspects of terminal differentiation in multiple L/R symmetric and L/R asymmetric neuronal lineages . In the ASE lineage , its proneural activity is graded in an anterior-to-posterior manner . Our RNAi screen revealed ASE laterality defects upon knockdown of two different components of the histone methyltransferase complex COMPASS [30] , ash-2 ( a PHD/SPRY domain protein ) and dpy-30 . A third locus , F21H12 . 1 , also generated ASE laterality defects and we find that this locus corresponds to the worm ortholog of an additional member of the COMPASS complex , the WD40 protein RbBP5 , here-on referred to as rbbp-5 ( Table 4 and Table S3; Figure 7 ) . We confirmed the ash-2 ( RNAi ) and dpy-30 ( RNAi ) ASE laterality defects observed during our RNAi screen using mutant alleles of the respective genes ( Figure 7 and Table S3 ) . In regard to rbbp-5 , we noted that it resides in the mapping interval of the previously identified but uncloned lsy-15 gene , an ASE laterality mutant retrieved from a large-scale mutagenesis screen [18] . As previously described , lsy-15 ( ot86 ) mutants show a conversion of ASEL to ASER fate [18]; this phenotype , like those of the other COMPASS members , is maternally rescued ( see below ) . Additionally , lsy-15 ( ot86 ) mutants , like other COMPASS complex mutants , display a partially penetrant dumpy phenotype , a low brood size and an egg-laying defect ( data not shown ) . The latter is likely the result of a uterine attachment defect ( Cog phenotype ) that can be observed in lsy-15 ( ot86 ) mutants along with ectopic expression of the ceh-2 vulval cell marker ( data not shown ) . Through whole genome sequencing , rescue , and analysis of deletion alleles provided by the C . elegans knockout consortia , we find that lsy-15 indeed corresponds to rbbp-5 ( Figure 8 , Table S3 and see below ) . We analyzed several other members of the COMPASS complex that were not revealed by our RNAi screen , using RNAi , mutant alleles or both . We identified a SET-type methyltransferase component , set-2 ( a SET1A&B ortholog ) and another two WD40 proteins , swd-3 . 1 and swd-2 . 2 ( the WDR5 and WDR82 orthologs ) as having ASE laterality defects similar to those observed in rbbp-5 , dpy-30 and ash-2 mutants ( Figure 7 and Table S3 ) . The ASE laterality defects of set-2 and swd-2 were only revealed in a genetically sensitized lsy-6 ( ot150 ) background . Additionally , this background enhanced the penetrance of the RNAi defects observed in several other COMPASS complex components ( Table S3 ) . Together we find that all core components of the COMPASS complex regulate ASE laterality . To determine when and where COMPASS acts to regulate ASE asymmetry we focused on rbbp-5 and generated several reporter gene and rescue constructs . A GFP-tagged fosmid array that fully rescues the ASE laterality defects of rbbp-5 ( ot86 ) mutants is expressed ubiquitously during early embryogenesis and is predominantly nuclear localized ( Figure 8D–8E ) . We observe similar expression of a translational reporter gene construct containing the intergenic region upstream of rbbp-5 ( data not shown ) . This is consistent with previously reported broad expression of several other components of the COMPASS complex [31] , [32] . At later stages , during and after the birth of the ASE neurons , rbbp-5 expression is significantly reduced throughout the embryo . We are able to observe overlapping expression with a reporter gene expressed bilaterally in the ASE neurons , however , we note that expression of the transgenic array in the ASE neurons was relatively infrequent despite the fact that array carrying animals are fully rescued ( Figure 8D–8E ) . Additionally , we also observe complete rescue of the ASE laterality defects in non-array carrying worms derived from array positive mothers suggesting that the fosmid array can maternally rescue rbbp-5 ( ot86 ) mutants . Consistent with this we observe maternal germline expression of the rbbp-5 fosmid ( Figure 8E ) and the mutants themselves are maternally rescued ( data not shown ) . These observations provide a hint that rbbp-5 and the COMPASS complex may act early during embryogenesis to regulate ASE asymmetry . To examine this possibility in more detail , we attempted to rescue the rbbp-5 mutant phenotype by driving an rbbp-5::gfp translational fusion using two heterologous promoters: ( 1 ) the che-1 promoter , which is exclusively expressed bilaterally in the mother of the ASE neurons and then in the mature ASE neurons throughout the life of the animal or ( 2 ) the ubiquitous hsp-16 heat-shock promoter . We find that ASE-specific expression of rbbp-5 only minimally rescues the rbbp-5 ( tm3463 ) mutant phenotype , despite being strongly expressed in the ASE neurons as expected ( Figure 7B and Figure S6 ) . In contrast , leaky expression from lines carrying the hsp-16 construct and maintained at room temperature strongly rescues the ASE laterality defect in a dose dependent manner , despite rare expression in the post-mitotic ASE neurons ( Figure 7B and Figure S6 ) . We next performed staged heat-shocks using those lines with minimal rescue ability of the rbbp-5 laterality defect at room temperature . We find that when the heat-shock is performed early during embryogenesis , before the birth of the ASE neurons , the ASE laterality defect is fully rescued ( Figure 7B ) . In contrast , if the heat-shock is performed at or after the birth of the ASE neurons the defect is only weakly rescued . We conclude that rbbp-5 acts before the generation of the ASE neurons . The most upstream component of the previously described network of laterality factors is the ASEL-expressed miRNA lsy-6 . We find that lsy-6 expression is lost upon abrogation of various COMPASS components ( Figure 7C and Table S3 ) . Moreover , the “2 ASEL” fate inducing activity of lsy-6 , expressed from a heterologous promoter , is epistatic to the loss of COMPASS activity ( rbbp-5 mutants ) , corroborating the notion that COMPASS acts upstream of lsy-6 ( Figure 7D and Table S3 ) . Previous genetic screens have revealed that a MYST-type histone acetyltransferase ( HAT ) complex also shows ASE laterality defects that are indistinguishable from those of the COMPASS histone methyltransferase ( HMT ) complex , in that the ASEL neurons convert into ASER neurons and that lsy-6 expression is lost in the post-mitotic ASE neurons [33] . One of the components of the MYST HAT complex was also retrieved from our RNAi screen ( the PHD/Bromodomain lin-49 locus ) . Given the phenotypic similarity between the MYST HAT and COMPASS HMT complex , we asked whether they genetically interact . This hypothesis was born out of biochemical studies that suggest that histone methylation is often a prerequisite for ensuing histone acetylation and transcriptional activation [34] . We tested for a genetic interaction by asking for synergies in gene function . We chose conditions in which RNAi against two different HMT components ( ash-2 and lsy-15 ) resulted in essentially no laterality defect ( in a non-sensitized , rather than nre-1; lin-15b RNAi hypersensitive background ) and tested the effect of RNAi against these genes either in a wildtype background or in a MYST HAT mutant background ( the lsy-12 MYST-like HAT gene and the lsy-13 ING-like gene ) . We observed strikingly synergistic phenotypes in both lsy-12 and lsy-13 mutant backgrounds ( Figure 7E ) . In contrast , ash-2 RNAi was unable to enhance the defects of rbbp-5 mutants ( Figure 7E ) and only additive or mild synergy at best was observed in ceh-36 mutants ( Table S3 ) . All together , this data demonstrates that rbbp-5 and the COMPASS complex acts early during embryogenesis , before the birth of the ASE neurons and in conjunction with the MYST complex to regulate the expression of the miRNA lsy-6 and therefore ASE laterality .
We described here the first comprehensive , genome-wide RNAi screen that monitors a single neuron-type cell fate decision . The screen has been remarkably successful in retrieving genes known to be involved in controlling ASE neuron specification . Six genes previously known to be involved in ASE neuron specification have been found in an unbiased manner ( che-1 , die-1 , lsy-2 , lin-49 , lim-6 , fozi-1 ) and only two have not ( cog-1 , nhr-67: these genes also display no laterality defects with sequence-verified RNAi clones ) . The RNAi screen also revealed a number of genes that we would have predicted to result in ASE lineage defects , including genes involved in the segregation of early patterning cues ( e . g . par and mex- genes ) and involved in a re-iteratively used binary fate decision system ( lit-1 gene ) [7] . Moreover , the RNAi screen assisted the cloning of two previously unknown lsy mutants , lsy-15/rbbp-5 ( this paper ) and lsy-22 [35] by identifying genes with laterality defects in the genetic intervals to which lsy-15 and lsy-22 had been mapped , facilitating the identification of the phenotype-inducing mutation within the whole-genome sequencing data . The screen identified an as yet elusive proneural patterning gene for the ASE lineage , the bHLH gene hlh-14 . Previous forward genetic screens for ASE differentiation mutants have failed to retrieve alleles in this gene , possibly because these screens are intrinsically biased against alleles in mutants with lethal phenotypes , illustrating a key conceptual advantage of the RNAi screening approach . Even though previous genetic screens for ASE developmental defects have successfully retrieved hypomorphic alleles of essential genes [18] , in the case of hlh-14 , alleles that separate essential functions from its proneuronal function in the ASE lineage may not exist . We have demonstrated that hlh-14 is required for the production of neurons from several distinct lineages . Besides lin-32/Atonal [36] , hlh-14 is only the second bHLH gene in C . elegans described with such a canonical neural versus hypodermal switch function . It is possible that hlh-14 may also regulate more general neural/non-neural decisions , such as the “non-clonal” production of neurons from the multi-potent pharyngeal lineages . Additionally , we have shown that the loss of hlh-14 leads to neuronal sub-type differentiation defects , suggesting that , in a similar fashion to lin-32 [37] , it may act at multiple stages of neuronal differentiation . How hlh-14 is regulated remains to be discovered . Although early pre-patterning mechanisms that regulate proneural gene expression have been well described in both vertebrates and Drosophila , few direct activators of proneural genes have been identified [38] , [39] . In C . elegans , lin-32 is activated by Hox genes and is also repressed by ref-1 , a downstream effector of Notch signaling [40] , [41] . This suggests parallels to the Notch-mediated regulation of vertebrate proneural genes [39] . Intriguingly , an ectopic ASE lineage is generated from the ABara blastomere in ref-1 mutants [12] and it will be interesting to determine whether this is due to a de-repression of hlh-14 . We observe L/R bilaterally symmetric expression of hlh-14 in neuroblasts of symmetric origin but remarkably we also observe L/R bilaterally symmetric expression of hlh-14 in lineages that derive from asymmetric lineage origins . This expression is initiated at an identical moment in time on both the left and right sides , despite the neuroblasts deriving from L/R asymmetric origins and is the case in both the ASEL/ASER lineages and the I1L/I1R lineages . In both cases , expression coincides with the point at which these lineages become bilaterally symmetric in that they undergo an identical set of divisions to generate the left-right homologs of bilateral pairs of neurons . Future identification of upstream regulators of hlh-14 and other factors that are expressed during this “symmetrization phase” will begin to address how left-right bilateral symmetry is established in the nervous system . Intriguingly , we also observe left/right asymmetric expression of hlh-14 , which is required to induce the fate of certain asymmetric neuroblasts/neurons of the C-lineage . It will be interesting to determine how this asymmetric expression is induced , how it is coordinated with bilaterally symmetric expression programs and whether , given the anterior-posterior lineage difference ( Figure 6C ) , asymmetric expression is regulated by the binary Wnt-signaling system [7] . Given that hlh-14 , like lin-32 , fulfills its proneural function in several very distinct neuronal lineages [28] ( this paper ) , it is obvious that additional co-factors must exist to determine specificity of bHLH gene action . With this thought in mind , we were surprised to find essentially no other transcriptional regulator that is involved at the neuronal versus ectodermal decision step - or any later or earlier step in controlling the generation of the ASE lineage branch . This is also surprising from the perspective of the binary , Wnt-mediated a/p decision system that works broadly throughout the developing C . elegans embryo [7] , [42] , [43] . Through mutant analysis we have previously shown that this system also operates in the ASE lineage [44] and , moreover , we have also identified in this screen the lit-1 gene , an important signal transducer of the binary system [43] . This binary specification system , whose critical output is a differential distribution of a POP-1/LEF transcription factor in dividing cells , is generally thought of as needing to pair with lineage-specific transcriptional regulators to instruct development [8] , [44] . In other words , differentially distributed POP-1/LEF-1 is thought to interact with cell-intrinsic transcriptional “codes” ( i . e . combinations of transcription factors ) to define regulatory states that are characteristic and specific for any cell at any stage of development . Since the pop-1 system presumably cooperates with different transcription factors at each of the a/p divisions that generate ASE , we expected to reveal a wealth of transcriptional regulators . Even if such factors had pleiotropic functions early in the embryo , our screen should have nevertheless revealed them as non-gfp-positive , dead embryos . Yet , with the exception of the known transcription factors ( TFs ) and hlh-14 , we identified no novel TF in the set of 801 TFs screened in our RNAi library that affect ASE specification . Moreover , we noted that only about 12% of TFs in the genome-wide TF clone library resulted in the embryonic arrest phenotype that one would expect from strong lineage patterning defects . In all these arrested embryos , the ASE neurons are generated normally , with the exception of the known embryonic patterning genes pie-1 and skn-1 . Finally , 59 TFs generated a P0 sterile phenotype preventing the analysis of F1 phenotypes in the nre-1 lin-15b background . We re-screened these genes in wildtype and rrf-3 or eri-1; lin-15b RNAi hypersensitive backgrounds , but uncovered no additional regulators of ASE fate ( data not shown ) . There are several possible and non mutually-exclusive explanations for the paucity of TF phenotypes . The simplest is a technical one , RNAi may not sufficiently knock down specific TFs . Supporting this notion , the cog-1 homeobox gene does not display an ASE laterality defect by RNAi and was not retrieved by the screen . The other , more complex and perhaps more interesting one is well exemplified with the T box TFs tbx-37 and tbx-38 [13] . We have previously shown that both are required at an early point in the lineage that generates the ASEL neuron for the correct establishment of ASE asymmetry [12] . Yet both genes act redundantly; removal of either gene alone has no effect [13] . The C . elegans genome contains a good number of closely related , paralogous TF pairs from various families and it is conceivable that particularly during early patterning , TFs may work in redundant pairs . In addition to the tbx-37/38 case , there are several other reported cases for such redundant TF pairs ( pes-1/fkh-2 [45] , med-1/med-2 [46]; end-1/end-3 [47] tbx-8/tbx-9 [48] and ref-family genes [41] ) . The early lineage determination of ASE left-right asymmetry led us to postulate the existence of either an “asymmetry mark” that can be remembered through several rounds of division to regulate expression of the miRNA lsy-6 expression or a cascade of TFs that leads from tbx-37/38 to lsy-6 [12] . Previous work from our lab has identified a MYST-type histone acetylase complex that controls ASEL/R asymmetry through regulation of the lsy-6 miRNA [33] and in this study we have shown that the histone methyltransferase complex COMPASS also regulates ASE laterality through regulation of lsy-6 ( Figure 7 ) . We have shown that COMPASS may be directly linked to the MYST HAT complex activity , as supported by phenotypic similarity as well as genetic interaction tests . Mechanistically , histone methylation may be a step directly upstream of and preceding histone acetylation , leading to transcriptional activation in several contexts ( reviewed in [49] ) . In the context of ASE asymmetry , this is supported by two components of the MYST HAT complex containing a PHD domain ( the LIN-49 protein and the LSY-13 protein ) , which is known to interact with methylated histones [34] , [50] . Indeed , biochemical studies show that the PHD finger of lsy-13 ortholog ING , recognizes the H3K4 trimethylation mark of COMPASS and serves as a link in site specific recruitment of HATs [51] , [52] . Our synergistic data corroborate this idea in an in vivo context and demonstrate that the COMPASS complex acts before the birth of the ASE neurons , providing a potential molecular link between the early Notch signal , the expression of tbx-37/38 and the ASEL specific expression of lsy-6 . Together , these histone-modifying complexes may form part of a lineage-specific “chromatin mark” that acts , in cohort with specific TFs to bias lsy-6 expression , such that it is only expressed in ASEL . What other genes and processes has our screen revealed ? Many of the genes revealed in our RNAi screen as being involved in neural development code for proteins with apparently very general cellular functions , such as general splicing factors and core components of RNA polymerases . The remarkably specific effects of some of these factors suggest that some of these proteins may not have as broad roles as previously thought or it may argue that some of the substrates ( e . g . those of splicing factors ) relevant to specific neuronal lineage decisions are sensitive to gene dosage ( and therefore more easy to functionally impair ) than other , more generally acting substrates . In addition , early C . elegans embryogenesis is characterized by very specific signaling events between specific individual blastomeres [53] . Several of the genes that we identified as having cell/lineage defects are likely a reflection of disrupting early blastomere divisions and positions resulting in aberrant signaling events and lineage misspecifications . For yet other genes - many of them with intriguing molecular identities - their function in determining ASE remains to be determined and will likely provide novel insights into the mechanisms of neural specification .
RNAi was performed using a bacterial feeding protocol [23] with the following modifications . NGM agar plates containing 6 mM IPTG and 100 µg/ml ampicillin were seeded with bacteria expressing dsRNA . L4 staged hermaphrodites were placed onto these plates and grown at 22°C . After five days , the F1 progeny of these worms were scored for neuronal and pleiotropic defects . All inserts of clones for the known ASE factors were sequenced to confirm identity of the clone . The visible phenotypes screened for chromosome I: Emb ( embryonic lethal ) , P0 Ste ( P0 sterile ) , Stp ( sterile progeny ) , Brd ( low brood size ) , Gro ( slow postembryonic growth ) , Lva ( larval arrest ) , Let ( larval lethal ) , Adl ( adult lethal ) , Bli ( blistering of cuticle ) , Bmd ( body morphological defects ) , Clr ( clear ) , Dpy ( dumpy ) , Egl ( egg- laying defective ) , Lon ( long ) , Mlt ( molt defects ) , Muv ( multivulva ) , Prz ( paralyzed ) , Pvl ( protruding vulva ) , Rol ( roller ) , Rup ( ruptured ) , Sck ( sick ) and Unc ( uncoordinated ) . Emb was defined as greater than 30% dead embryos . Each phenotype was required to be present among at least 10% of the analyzed worms for the visible pleiotropies and neuronal defects . Non-viable ( nonV ) category encompasses P0 Ste , Emb , Stp and Let phenotypes . The JA library of RNAi clones was purchased from Geneservice ( http://www . lifesciences . sourcebioscience . com/ ) and was supplemented with the ORFeome based RNAi clones [60] courtesy of John Kim [61] . Based on sequencing of several randomly picked clones from the library we estimate the faithfulness of the library to be 80–90% ( data not shown ) . Cells were identified using 4D microscopy and SIMI BioCell software as described in [10] . Briefly , wild-type ( containing the ntIs1 transgene ) , hlh-14:gfp ( gmIs20 ) , hlh-14 ( gm34 ) ; otIs114 , hlh-14 ( tm295 ) ; ntIs1; otEx4507 Ex[hlh-14FOS::YFP] gravid adults were dissected and single two-cell embryos were mounted and visualized on a Zeiss Axioplan 2 compound microscope . To lineage hlh-14 ( tm295 ) mutants , non-array carrying embryos from the hlh-14 ( tm295 ) ; ntIs1; otEx4507 Ex[hlh-14FOS::YFP] strain were examined . Nomarski stacks were taken every 35 seconds and images within a stack at ∼1 µm apart . Fluorescent images were acquired at pre-determined time points during the course of embryonic development . Movies were lineaged using the SIMI BioCell program . The positions of cells at different time-points were monitored using the 3D-feature of SIMI BioCell . All additional DIC and fluorescent images were acquired using the open source Micro-manager software [62] . Embryos were dissected from array carrying mothers , staged and then subjected to a 15 minute heat-shock at 37°C after which the embryos were placed at 15°C and allowed to develop to the L1 larval stage before scoring . Embryos were divided into two categories for heat-shocks: ‘early’ = younger than AB256-cell stage; ‘late’ = older than AB256-cell stage . The lsy-15 ( ot86 ) allele was mapped to a region of approximately 15 MB ( Figure 6 ) through classic three-factor and single-nucleotide polymorphism ( SNP ) mapping . Then , genomic DNA was prepared from a population of lsy-15 ( ot86 ) ; otIs114 animals using the Gentra GenePure DNA extraction kit . This genomic DNA ( 5 µg ) was genome sequenced as previously described [63] using an Illumina Genome Analyzer II according to the manufacturers specification . The resultant sequence data was analyzed with MAQGene [64] . The following stringency criteria were used to filter variants: quality score > = 3 , loci multiplicity < = 1 , sequencing depth > = 3 . All detected sequence variants were analyzed for whether they were present in any one of several additional genomes of different genotypes that we sequenced in the laboratory; if so , they were considered background and eliminated from further analysis .
|
The generation of a neuron from a fertilized egg requires a multi-step cascade of molecules acting from within and outside that cell to direct it towards a neuronal fate , rather than , say , a muscle cell . These cascades are not fully understood . In this study we systematically eliminate the function of almost all genes in the C . elegans genome , one by one , to determine what it takes to build a neuron . We identified 245 genes that affect the development of a specific sensory neuron pair , e . g . the neurons were not generated or the neurons were generated but the terminal fate was not correctly specified . We characterize in more detail the transcription factor hlh-14 , which we find is required to generate multiple neurons , and the COMPASS histone methyltransferase complex , which we find to have a surprisingly specific role in the specification of a molecular and functional left-right asymmetry in this sensory neuron pair . Our study represents the first genome-wide analysis of a single neuronal cell fate decision . Further characterization of the genes identified here will enhance our understanding , and thus our capacity for treatment and prevention , of human neurological disorders .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"neuroscience",
"animal",
"genetics",
"model",
"organisms",
"genetics",
"biology",
"neuroscience",
"genetics",
"and",
"genomics"
] |
2011
|
A Genome-Wide RNAi Screen for Factors Involved in Neuronal Specification in Caenorhabditis elegans
|
Bone morphogenetic protein ( BMP ) pathways control an array of developmental and homeostatic events , and must themselves be exquisitely controlled . Here , we identify Caenorhabditis elegans SMA-10 as a positive extracellular regulator of BMP–like receptor signaling . SMA-10 acts genetically in a BMP–like ( Sma/Mab ) pathway between the ligand DBL-1 and its receptors SMA-6 and DAF-4 . We cloned sma-10 and show that it has fifteen leucine-rich repeats and three immunoglobulin-like domains , hallmarks of an LRIG subfamily of transmembrane proteins . SMA-10 is required in the hypodermis , where the core Sma/Mab signaling components function . We demonstrate functional conservation of LRIGs by rescuing sma-10 ( lf ) animals with the Drosophila ortholog lambik , showing that SMA-10 physically binds the DBL-1 receptors SMA-6 and DAF-4 and enhances signaling in vitro . This interaction is evolutionarily conserved , evidenced by LRIG1 binding to vertebrate receptors . We propose a new role for LRIG family members: the positive regulation of BMP signaling by binding both Type I and Type II receptors .
Bone morphogenetic protein ( BMP ) receptor serine/threonine kinases ( BMPRs ) are pivotal signal transducers for the small , secreted BMP morphogens , members of the transforming growth factor β ( TGF- β ) superfamily ( comprising subfamilies of TGF- βs , BMPs , activins , and others ) [1] , [2] . BMP dimers released from neighboring cells are received by these receptors , which leads to an intracellular cascade of transcriptional events . Depending on the specific pathway , cell type and milieu , these events result in a diverse array of cellular processes , from dorsal-ventral specification to cell cycle control and programmed cell death [3] . Understanding how growth factor pathways are regulated may lead not only to a better understanding of their normal physiological roles , but may also lead to potential treatments for a wide range of disorders and diseases [4] , [5] . Secreted BMP dimers travel through the extracellular matrix to activate their receptors . Originally thought to be a process of simple diffusion , the movement of TGF-β superfamily members is now recognized to be highly regulated [6] . Many factors play a role in facilitating or preventing BMP ligand access to receptor . Post-translational processing and proteolysis of ligand , as well as seclusion of ligand by extracellular matrix ( ECM ) components like integrins and proteoglycans , for example , determine whether a ligand dimer can interact with its receptors [7] . Not only is the BMP's progress exquisitely controlled , but the receptors themselves are also subject to regulation [6] , [8] . Inside the cell , receptor phosphorylation is inhibited by phosphatases , and SARA and Smurf proteins target receptors for polyubiquitination and degradation [8] . Outside the cell , the receptor complex can be inactivated by the decoy type I receptor BAMBI [9] . Coreceptors betaglycan/TGFβ receptor II ( TGFβR3 ) and endoglin can bind certain BMPS and deliver them to receptors [10]–[14] . Endoglin also associates with select type I and type II receptors [13] . Pioneer studies in Caenorhabditis elegans and Drosophila melanogaster have identified components of the pathway and furthered understanding of BMP signaling [15] , [16] . These studies have identified the conserved core of the signaling pathway , including the ligand , the type I and type II receptors , and the Smads . In C . elegans , a BMP-like pathway controls body size and male tail development ( the Sma/Mab pathway ) . The receptors for the ligand DBL-1 are SMA-6 ( type I ) and DAF-4 ( type II ) [17] , [18] . Receptor signals are transduced through the Smads SMA-2 , SMA-3 , and SMA-4 [19] . As in mammals , co-transcription factors that act with the Smads have been discovered , including SMA-9/Schnurri and RNT-1/RUNX [20] , [21] . The body size phenotype of the Sma/Mab pathway is very sensitive to dose , indicating that the core pathway is tightly controlled [17] , [18] , [22] , [23] . The only identified extracellular regulator of this pathway has been LON-2 , a conserved heparan sulfate proteoglycan that binds DBL-1/BMP and attenuates pathway signaling [24] . To identify novel components of BMP signaling , we performed traditional forward genetic screens for mutations affecting the Sma/Mab signaling pathway ( [25] , unpublished results ) . From these genetic screens , we identified , cloned , and characterized sma-10 . SMA-10 is a positive regulator of Sma/Mab signaling , and is absolutely and specifically required for body size regulation . It is a member of a cell surface localized family of proteins with leucine rich repeats and immunoglobulin-like domains ( LRIG ) . The function of SMA-10 in BMP pathway signaling is conserved , as the Drosophila ortholog lambik rescues the body size defect of sma-10 ( lf ) animals . Furthermore , SMA-10 promotes BMP signaling in mammalian cells . SMA-10 binds the pathway receptors SMA-6 and DAF-4 but not the BMP DBL-1 , and a mammalian ortholog , LRIG1 ( leucine-rich and immunoglobulin-like domains-1 ) , also binds both type I and type II receptors . These studies identify a uniquely acting positive regulator of BMP signaling , SMA-10/LRIG , that directly interacts with type I and type II receptors from C . elegans to mammals .
The first small C . elegans mutants were identified in a large-scale screen for morphology and mobility mutants [26] . Their role in BMP signaling was elucidated when sma-2 , sma-3 and sma-4 were characterized [19] . In an effort to identify additional genes that act in BMP signaling , we performed two genetic screens . From the first screen , in which body size mutant F2 animals were isolated from mutagenized N2/wild type P0 animals , two sma-10 alleles , wk26 and wk66 , were identified [25] . From a lon-2 ( e678 ) suppressor screen , three additional alleles , wk88 , wk89 , and wk90 , were identified and confirmed by complementation and sequencing . The Sma/Mab pathway regulates both body size and male tail development . A reduction of dbl-1 pathway activity results in animals that are 55%–85% wild-type length [22] , [23] . sma-10 ( lf ) animals share the small body size defect , ranging from 79% to 88% the length of wild-type animals ( Figure 1A and 1B , Table 1 ) . The dbl-1 pathway also regulates the development and patterning of male tail structures [17]–[19] , [22] , [23] , with mating spicules and sensory rays 5–7 being primarily affected . We therefore asked if sma-10 is also involved in patterning the male tail . Our studies revealed that all five alleles of sma-10 , including a presumed null ( wk88 ) , have wild-type male tail rays and spicules ( data not shown ) . These data suggest that SMA-10 is specifically involved in BMP signaling to control body size but not male tail development or patterning . Because small body size is a hallmark phenotype of mutants in the Sma/Mab pathway , we asked if sma-10 genetically interacts with Sma/Mab pathway members . To determine the functional order of SMA-10 relative to Sma/Mab pathway members , we performed epistasis tests . We generated animals with mutations in sma-10 and DBL-1/BMP pathway genes that alone give opposite phenotypes ( small and long , respectively ) , and asked what the terminal phenotype was for these strains ( small or long ) . In a regulatory pathway , the terminal phenotype is a result of the loss of gene product with the most downstream effect bypassing the requirement for the second , upstream gene product . We created double mutant animals of sma-10 with lon-2 , overexpressed dbl-1 , or over expressed sma-6 . We found that the small body size phenotype of sma-10 ( lf ) was the terminal phenotype in double mutant animals with either lon-2 or over expressed dbl-1 , but not for overexpressed sma-6 ( Table 2 ) . These results suggest that SMA-10 functions in the unique position between the receptor SMA-6 and the ligand DBL-1 . To understand the molecular nature of sma-10 , we mapped and cloned the gene . sma-10 is located at LG IV: −26 . 82 . Cosmid T21D12 conferred rescue of the sma-10 ( wk66 ) small phenotype , as does a sma-10 cDNA ( Table S1 ) . sma-10 encodes an 881 amino acid protein of the LRIG family ( leucine rich repeats and immunoglobulin-like domains ) . SMA-10 is composed of an N-terminal signal sequence ( amino acids 1–20 ) , fifteen leucine-rich repeats ( LRRs ) flanked by an LRR N-terminal domain at amino acids 24–56 and an LRR C-terminal domain ending at amino acid 499 , three immunoglobulin domains ( spanning amino acids 503 to 802 ) , a transmembrane domain ( amino acids 839 to 861 ) , and a short ( 19 amino acid ) intracellular domain from amino acids 862 to 881 ( Figure 2 ) . The C-terminal tail is not conserved between C . elegans , Drosophila and mammals . This protein structure , which is largely extracellular and is transmembrane-bound , is consistent with the order of gene function , which places SMA-10 between the secreted DBL-1/BMP and its membrane-bound receptors . Sequencing DNA of mutant sma-10 strains confirmed lesions within T21D12 . 9 ( Figure 2 ) . sma-10 ( wk26 ) contains a T to C bp change at position 572 , a substitution that changes a conserved leucine to a phenylalanine in the second LRR at amino acid 102 . A deletion of 251 bps generates sma-10 ( wk66 ) ( on cosmid T21D12 from base pairs 21092 to 21342 , beginning in the gene at the end of exon 9 ) . This transcript is predicted to result in a truncated protein with 609 a . a . of SMA-10 and 10 a . a . of novel sequence before terminating , deleting sequences after the first immunoglobulin-like domain . wk88 introduces a stop codon into amino acid position 112 in the LRR2 , resulting in a severely truncated protein , and is a presumed null allele . wk89 is a 975 bp deletion from position 21301 to 22275 in T21D12 , starting in intron 9 and ending in intron 11 , deleting the protein after amino acid 611 and creating a frame shift and premature termination sequence , removing sequences after the first immunoglobulin-like domain . wk90 changes sequence encoding Trp 286 to a stop codon , deleting sequences after LRR9 . Multiple splice variants are predicted by cDNA sequencing ( Wormbase . org ) . yk352c5 , a full-length 2 . 6 kb cDNA of the longest splice variant T21D12 . 9a , driven by 1 . 2 kb of upstream sma-10 promoter sequence , is sufficient to confer rescue of sma-10 ( wk66 ) animals ( Table S1 ) . A function for the shorter splice variants ( T21D12 . 9b , T21D12 . 9c . 1 , and T21D12 . 9c . 2 ) is not known . Database searches reveal orthologs in other metazoans . The hallmark of this family is the presence of fifteen leucine-rich repeats ( LRRs ) and three immunoglobulin-like repeats ( Ig-like ) followed by a transmembrane domain . In Drosophila , there is one SMA-10 ortholog , Lambik ( www . flybase . org ) , and in vertebrates , there are three known sma-10 orthologs , LRIG1 , LRIG2 , and LRIG3 [27] . lambik corresponds to CG8434 , but no phenotypic characterization has been published . A distantly related , distinct family is composed of the insect-specific Kekkon members , which have six LRRs and one Ig–like domain . One of these , Kekkon 1 ( kek1 ) , plays a role in inhibiting the epidermal growth factor receptor ( EGFR ) [28] . Another Kekkon , Kekkon5 ( kek5 ) , interacts genetically with a BMP signaling pathway [29] . Although core Sma/Mab pathway components are expressed in many tissues , they are all required in the hypodermis for body size regulation . To determine if SMA-10 is also needed in these cells , we first asked where sma-10 is expressed . We created a functional translational fusion of SMA-10 with GFP at the C-terminus and expressed it using the sma-10 promoter . Expression of SMA-10:GFP was visible , though faint , in the hypodermis ( Figure 1C and 1D ) , consistent with SMA-10's genetically identified role as a regulator of the DBL-1 signaling pathway , which functions in the hypodermis to regulate body size . Expression in the hypodermis of other DBL-1 pathway genes is also low [24] , [30]–[33] . Bright fluorescent expression was also observed in the pharynx of animals from embryos to adults ( Figure 1E ) . Pharyngeal expression was localized to the muscle segments pm1 ( anterior cell of the procorpus ) , pm4 ( metacarpus/anterior bulb ) , the anterior part of pm5 ( isthmus ) , and pm7 ( terminal bulb ) ( Figure 1E ) . Expression of SMA-10:GFP localized to both the cell surface and to puncta within the cells ( Figure 1E ) . Expression of mRNA corresponding to the sma-10 locus has also been reported in intestine and renal gland cells in both larvae and adult animals [34] . Digestive tract expression ( in the pharynx and intestine ) of other Sma/Mab pathway members has been reported [18] , [32] , [33] , [35] , [36] , and may reflect a proposed role for this pathway in an innate immune response [37] , [38] . We then asked where SMA-10 is required for body size regulation . Because of its expression pattern and previous experiments showing that DBL-1/BMP pathway components are required in the hypodermis , we drove the expression of full-length genomic sma-10 from pharyngeal- or hypodermal-specific promoters in sma-10 ( wk66 ) animals and assayed for rescue of body size . We found that expression of sma-10 ( + ) in hypodermis ( using the rol-6 promoter ) , but not pharynx ( using the myo-2 promoter ) , was sufficient to rescue animals to wild-type length ( Table 3 ) . Lambik is a Drosophila LRIG that is orthologous to vertebrate LRIGs and to SMA-10 . Although the function of Lambik is currently unknown , we asked whether lambik could functionally substitute for sma-10 in C . elegans . We drove Drosophila lambik cDNA from the sma-10 promoter in sma-10 ( wk66 ) animals . Transgenic animals were rescued to the wild-type body size ( Table 4 ) , thereby showing functional conservation between divergent Drosophila and C . elegans LRIGS . Our genetic data demonstrate that SMA-10 acts within a nematode BMP-like pathway to promote signaling . To test whether this function is conserved , we asked if SMA-10 could regulate BMP signaling in mammalian cells . We used a standard reporter assay in a BMP-responsive human cell line ( HepG2 ) , the BMP-response element from the Smad7 gene driving luciferase [39] . BMP2 induced reporter activity significantly over the controls , whereas in the presence of SMA-10 , the response of the promoter was increased 4 . 2-fold over BMP2 alone ( Figure 3A ) . Thus , SMA-10 can directly promote mammalian BMP signaling . The genetic and molecular data strongly support a model in which SMA-10 acts on the extracellular surface of the plasma membrane , and could act via physical interactions with ligand , receptors , or ligand and receptors . To examine these possibilities and to provide mechanistic insights into SMA-10's function , we asked whether SMA-10 directly binds to ligand or to the DBL-1 receptors , SMA-6 and DAF-4 . For the first experiment , we employed affinity labeling and substituted BMP2 for DBL-1 , since DBL-1 protein is not readily available and BMP2 has been shown to physically interact with other DBL-1 pathway members [17] , [24] . SMA-10 was FLAG-tagged and BMP2 ligand was radio-iodinated with 125I . After anti-FLAG immunoprecipitation and blotting , the blot was exposed to film . However , we failed to detect any binding of BMP2 to SMA-10 ( Figure S1 ) . We next asked whether SMA-10 interacted with the receptors . FLAG-tagged SMA-10 and HA-tagged receptors were transfected into 293T cells . To isolate SMA-10 and associated proteins from cells , lysates were first immunoprecipitated with anti-FLAG antibody and blotted . The blot was then probed with anti-HA antibody to determine whether receptors were bound to SMA-10 . Under these conditions , both SMA-6 and DAF-4 receptors co-immunoprecipitated with SMA-10 ( Figure 3B ) . Thus , SMA-10 physically interacts with receptors and not with the ligand . Given that SMA-10 binds the Sma/Mab pathway receptors , we tested whether this interaction was conserved in a mammalian system . Using the mammalian ortholog LRIG1 and mammalian BMP receptors , we assayed the ability of LRIG1 to bind the receptors . FLAG-tagged LRIG1 and HA-tagged versions of BMP receptors were transfected into mammalian cells . Lysates were immunoprecipitated with anti-FLAG antibody to pull down LRIG1 , blotted , and probed with anti-HA antibody to determine whether LRIG1 binds any BMP receptors . We found that LRIG1 interacted strongly with type I receptor ALK6 and more weakly with the type I receptors ALK1 , ALK2 , ALK3 , and ActRIB ( Figure 3C ) . In addition , we detected weak interactions with type II receptors ActRII and ActRIIB ( Figure 3C ) . Thus , LRIGs interact with BMP receptors and represent a new class of BMP receptor-associated proteins .
We propose that SMA-10 acts at the hypodermal membrane surface , where the Sma/Mab receptors are located , to facilitate receptor signaling . Our genetic evidence shows that the SMA-10 protein acts between the secreted ligand and the transmembrane receptors ( Table 2 ) . The structure of SMA-10 indicates that it contains a secretion signal and a transmembrane region ( Figure 2 ) . Subcellular localization of a rescuing translational SMA-10:GFP fusion shows SMA-10 in hypodermal tissues and at cell membrane surfaces ( Figure 1C and 1E ) . The requirement for sma-10 expression in the hypodermis to rescue the sma-10 ( lf ) small body size further supports the model that SMA-10 acts at the hypodermal membrane to facilitate DBL-1 binding to its receptors ( Table 3 ) . We predict that the extracellular domain is responsible for this rescuing effect , as SMA-10's C-terminal intracellular tail is short ( 19 amino acids ) and not conserved with Lambik , which has SMA-10 function , and SMA-10 is able to rescue when tagged with GFP at the C-terminus . We suggest that this intracellular sequence is not critical for transducing BMP signals . Mutant animals for all previously characterized Sma/Mab core-signaling components have alterations in both body size and male tail defects . However , sma-10 only affects body size . This can be explained if sma-10 acts in a tissue-specific manner , namely the hypodermis , where DBL-1activated pathway signaling is required for body size control . Previous studies of targets downstream of this pathway ( lon-1 , mab-21 , and mab-23 ) and one upstream regulator ( LON-2 ) have shown that functions in the body size and male tail pathways are separable [22] , [24] , [30] , [31] , [42] . Given that sequencing reveals that one of the sma-10 alleles ( wk88 ) is a presumed null , we favor the model that SMA-10 acts in a tissue-specific manner to enhance signal strength in the cells contributing to body size but not to the male tail . Consistent with this reasoning , differences in the requirement of signal strength between the tail and the hypodermis are seen with a hypomorphic allele of sma-6 , where body size is reduced while tail morphology is normal [18] . It remains to be seen whether differential responses to Sma/Mab pathway dosage differences are sufficient to explain this tissue specificity or if there exists another male-specific factor that functions , like SMA-10 in the hypodermis , to positively regulate Sma/Mab signaling in the male tail . We note that all the sma-10 alleles result in small animals that are not as small as animals with loss-of-function mutations in previously identified core-signaling components . If tissue specificity in other organisms is a common feature , then this could explain why sma-10 orthologs were not previously identified as BMP signaling modulators . We note that mammals have three LRIGs , and mutating a single LRIG ( LRIG1 , a . k . a . LIG-1 ) results in a mild hyperplasia of epidermal cells [43] . This is a seemingly minor defect , but loss of LRIG1 function may be partially compensated by the other two functional LRIGs . Therefore , redundancy may also explain why LRIGs have eluded identification as regulators of BMP signaling in higher organisms . The LRR and Ig-like domains exist singly in many other proteins , but only in the LRIG family do they exist in the same protein . The number of protein domains in the SMA-10/LRIG family , which excludes the insect Kekkon subfamily , is invariant . These domains have been shown to be involved with protein-protein interactions [44] , [45] . In these studies , we show that SMA-10 and LRIG1 bind BMP type I and type II receptors , and our genetic evidence and studies based on overexpression in mammalian cells suggests that these proteins are positively required for BMP family signal transduction . Kekkon 1 has been shown to be a negative regulator of the EGF pathway in Drosophila and acts by binding to the receptors [28] . Given the involvement of Kekkon 1 in EGF signaling , LRIG1 was tested for EGFR regulation in mammalian tissue culture [40] . LRIG1 was shown to also bind EGFRs and enhance their degradation [40] . Signaling of EGFs through EGFRs promotes cell proliferation . LRIG1 negatively regulates cell proliferation by down-regulating EGFR responsiveness by increasing activated receptor ubiquitination [28] , [40] . LRIG1 binds the E3 ubiquitin ligase c-Cbl , bringing it into the EGFR complex . EGFR then phosphorylates c-Cbl , thereby activating it and promoting ubiquitination and degradation of both LRIG1 and EGFRs . LRIG1's amino acids 900 to 930 contain the binding site for c-Cbl [40] , within its intracellular domain that is not shared by other LRIGs , SMA-10 , or Lambik . An interaction of c-Cbl with LRIG2 or LRIG3 has not been demonstrated [46] . The ectodomain of LRIG1 alone inhibits EGFR signaling , but does so in a ubiquitin-independent fashion , showing that EGFR inhibition by LRIGs is not exclusively through ubiquitination [47] . EGFR signaling in C . elegans , mediated by a single EGFR ( LET-23 ) , directs several embryonic and larval cell fates and also ovulatory contractions in adult hermaphrodites [48] . We did not see any obvious defects associated with EGFR in sma-10 ( lf ) animals that might suggest an interaction with EGFR . LRIG1 also binds to and inhibits signaling by hepatocyte growth factor receptor ( Met ) , a tyrosine kinase that in many known cancers is mutated or misregulated to promote invasive growth , though its mechanism of inhibition is ubiquitin-independent [41] . C . elegans has no recognized Met receptor tyrosine kinase [49] . Another Kekkon , Kekkon5 , affects BMP signaling , but the model , based on genetic and structure-function analyses , proposes that Kekkon5 regulates ligand distribution or activity rather than acts directly on receptors , as we show here for both C . elegans LRIG SMA-10 and mammalian LRIG1 [29] . Although their expression appears to be universal , human LRIGs are differentially expressed in various cancer cell types , being down regulated in many types studied , but being upregulated in others [46] . Various explanations have been proposed based on the current understanding of LRIG function [46] . Our research showing LRIG interaction with BMP receptors ( BMPRs ) leads us to propose a new model , where the cell-specific levels of BMP and EGF activity , which negatively and positively regulate cell growth , respectively , determine the cell's response to LRIG exposure . The observation that SMA-10 localizes to intracellular puncta ( Figure 1C ) is reminiscent of endocytic vesicles , and suggests a model of action for SMA-10 and the Sma/Mab signaling pathway [50] . In other systems , there is evidence that TGF-β superfamily pathway signaling can be activated via receptor monoubiquitination and receptor complex endocytosis into early vesicles ( reviewed in [51] , [52] ) . SMA-10 may thus promote signaling by facilitating receptor internalization into early endosomes . This work identifies a new , conserved component of BMP signaling , SMA-10/LRIG . Mammalian members play known roles in some receptor tyrosine kinase pathways , and this work identifies a new role for this family in BMP receptor serine/threonine kinase signaling . We have shown that two members of this family physically interact with both the type I and type II BMP receptors , and C . elegans SMA-10 enhances signaling in both the nematode and in mammalian cells .
Animals were maintained according to standard protocols [26] . All mutant strains used in this study were derived from the wild-type Bristol strain N2 , and some mapping was accomplished using the wild-type Hawaii isolate CB4856 . Alleles used include sma-10 ( wk26 , wk66 , wk88 , wk89 , wk90 ) [25] , lon-2 ( e678 ) , sma-6 ( wk7 ) , ctIs40 [ZC421 ( dbl-1 ( + ) ) + pTG96 ( sur-5::gfp ) ] [23] , bxIs16 [tph1::gfp + cat-2::yfp] [42]; and nIs128 [pkd-2::gfp] [53] . Arrays made for this study are wkEx47 ( sma-10p::sma-10:gfp + pUC18 filler DNA , 50 ng/µl each ) , wkEx91 ( myo-2p::sma-10 ( + ) ) , wkEx92 ( rol-6p::sma-10 ( + ) ) , wkEx93 ( sma-10p::lambik ) , texEx190 ( sma-6p::sma-6 ( + ) :gfp ) , and texEx195 ( sma-10p::sma-10 ( yk352c5 ) . Creation of transgenic arrays was performed by standard microinjection techniques [54] . Genomic sma-10 or Drosophila lambik cDNA was cloned into nematode expression vector pPD95 . 75 with the appropriate promoter sequence . wkEx47 was made by removing the stop codon of the sma-10 genomic sequence and fusing the gfp sequence in-frame at the 3′ end . Transgenic animals were generated by germline microinjection , using constructs at 50 ng/µl ( HW480 sma-10p::lambik , CZ10 . 2 sma-10p::sma-10:gfp , and CZ9 . 1 sma-10 ( yk352c5 ) ) or 0 . 5 ng/µl ( HW469 rol-6p::sma-10 and HW477 myo-2p::sma-10 ) with the co-injection marker ttx-3p::rfp or ttx-3p::gfp at 50 ng/µl ( with HW480 and CZ9 . 1 ) or 100 ng/µl ( with HW469 and HW477 ) . ttx-3p drives expression in AIY interneurons . One representative stable line for each transgene was measured . Isolation of sma-10 ( wk26 ) and sma-10 ( wk66 ) was previously described [25] . In an effort to identify additional alleles of genes that act BMP signaling , lon-2 ( e678 ) hermaphrodites were mutagenized with 50 mM ethyl methanesulfonate ( EMS ) using standard procedures [26] . Mutagenized P0 animals were transferred to plates and allowed to segregate self-progeny . F1 animals were transferred to new plates to segregate progeny , which were then scored for a small phenotype in a quarter of the population . From about 9 , 000 mutagenized genomes screened , three additional alleles of sma-10 were isolated , wk88 , wk89 , and wk90 . These alleles , as well as the two alleles isolated in the Sma screen [25] , were outcrossed five times before further analyses were done . To measure body size , animals were picked at the L4 stage and photographed as young adults about 24 hours later . Images from individual animals were captured from a dissecting microscope using an Optronics MagnaFire CCD camera system and software ( Optronics , Goleta , CA ) . Perimeters ( Table 1 ) or lengths ( Table 2 , Table 3 , Table 4 ) of animals were determined by using Image-Pro Plus measurement software ( Media Cybernetics , Inc . , Silver Spring , MD ) . The images for Figure 1C and 1D were captured using an Axiovert 200 M microscope ( Carl Zeiss MicroImaging , Oberkochen , Germany ) equipped with a digital CCD camera ( C4742-95-12ER , Hamamatsu Photonics , Hamamatsu , Japan ) and were deconvolved with AutoDeblur software ( AutoQuant Imaging , Watervliet , NY ) . The Figure 1E image was obtained on an Olympus IX81 with a Carv Nipkow disk confocal unit ( Atto Biosciences , Rockville , MD ) and SensiCam QE camera ( Cooke Corp . , Auburn Hills , MI ) . We performed statistical analyses on these measurements . Individual measurements from each strain were averaged . We determined the ratio and 95% confidence interval of the average measurement ( mean ) of each strain to the wild-type strain mean . To verify the significance of our findings , we tested the null hypothesis that the ratios of the compared means are the same . The ratios compared were the double mutant strain mean/wild type mean to the single sma/transgenic mutant strain mean/wild type strain mean . For populations measured on the same day , the denominator ( the average of wild-type measurements ) was the same and a Student's t-test was used to test the null hypothesis . For populations measured on different days , the Welch-Satterthwaite equation was used to calculate the effective degrees of freedom for this 2-tailed t-test for two ratios . We determined the value of t with the calculated degrees of freedom and compared the t-value to the Student's t table to construct the probability ( p ) of the null hypothesis . sma-10 ( wk66 ) ; lon double mutant animals were constructed by crossing heterozygous sma-10 ( wk66 ) males with lon-2 ( e678 ) or lon-1 ( wk50 ) hermaphrodites , with animals overexpressing an integrated transgene with wild-type dbl-1 ( ctIs40 ) , or with animals overexpressing an extrachromosomal array encoding functional SMA-6 ( texEx190 ) [15] . Wild-type F1 were isolated and small and long F2 animals were picked to individual plates . The F3 generation was then examined for the presence of long animals from a small F2 parent or small animals from a long F2 parent . sma-10 ( wk66 ) was previously mapped to chromosome IV by two-factor crosses [25] . We further refined its position by standard three-factor mapping and single nucleotide polymorphism mapping [26] , [55] . We used microinjection and germline transformation rescue [54] to discover that YAC Y80C9 and cosmid T21D12 rescue the small phenotype of sma-10 ( wk66 ) animals . Each predicted gene on T21D12 , with at least 1 . 5 kb of promoter sequence , was amplified by the polymerase chain reaction ( PCR ) . The DNA product was purified and injected into sma-10 ( wk66 ) animals . Only T21D12 . 9 rescued the small phenotype . PCR confirmed altered or deleted sequences in all five sma-10 alleles . 293T cells were cultured in Dulbecco's modified Eagle's medium containing high glucose and supplemented with 10% fetal bovine serum ( FBS ) . HepG2 cells were cultured in minimal essential media supplemented with 1% non-essential amino acids and 10% FBS . Cells were transfected by calcium phosphate and lysed 48 hours after transfection in TNTE buffer containing 0 . 5% Triton-X-100 ( 150 mM NaCl , 50 mM Tris ph 7 . 4 , and 1 mM EDTA ) [56] . Immunoprecipitations were carried out using M2 anti-FLAG ( Sigma ) or 12CA5 anti-HA ( made in-house ) followed by incubation with Protein-G Sepharose beads ( Amersham Biosciences , Uppsala , Sweden ) . Immunoprecipitates were then washed four times with lysis buffer containing 0 . 1% Triton-X-100 . Proteins were separated by SDS-PAGE and immunoblotted with anti-HA ( 12CA5 ) or anti-FLAG ( M2 ) . Transcriptional assays were carried out using a previously described BMP-responsive element from the mouse Smad7 gene ( I-BRE ) driving firefly luciferase [39] . Luciferase assays were carried out as described [39] . Renilla luciferase expressed from the CMV promoter was used as an internal control for transfection efficiency . Firefly luciferase values were normalized using Renilla luciferase values . 1 . 2 kb promoter sequence 5′ of the sma-10 open reading frame driving expression of genomic sma-10 was sufficient to rescue body size defects in sma-10 ( lf ) animals , and this same promoter region was fused to Drosophila lambik/CG8434 cDNA to address conservation of LRIG family function . Tissue specific expression was determined by driving expression of sma-10 genomic sequence with myo-2 ( pharyngeal expression ) or rol-6 ( hypodermal expression ) promoters [57] . We generated GFP-tagged SMA-10 by fusing the eGFP sequence to the 3′ terminus of wild-type sma-10 genomic sequence that lacked its termination codon . The rescuing sma-6 construct was made by fusing the mCherry sequence in frame to the 3′ end of genomic sma-6 and expressing it using 1260 bp sma-6 promoter sequence . 3x FLAG SMA-10 was made by fusing the full-length sma-10 cDNA from the first Sal I site in frame to the 3x FLAG signal peptide containing vector p3xFLAG-CMV8 ( Sigma ) . Similarly , we constructed the 3x FLAG-tagged LRIG-1 by subcloning the LRIG cDNA from an endogenous Sal I restriction site to the C-terminus into p3xFLAG-CMV8 . This construct encodes a protein with the 3x FLAG tag between the N-terminal signal sequence and the first LRR of LRIG1 . SMA-6 was C-terminally tagged with the HA1 epitope of influenza virus hemagglutinin ( HA ) and was cloned into the mammalian expression vector pCMV5 ( T . Reguly and J . Wrana , unpublished work ) . DAF-4DC-HA is HA-tagged DAF-4 with a deletion of its C-terminal tail ( T . Reguly and J . Wrana , unpublished work ) . ALKIHA , ALK3HA , ALK6HA , ALK2HA , ActRIBHA , ActRIIHA , ActRIIBHA have been previously described [58]– .
|
Bone morphogenetic protein ( BMP ) family members , small secreted signaling molecules , play diverse roles in development and homeostasis . Uncontrolled BMP signaling results in a variety of disorders and diseases . BMPs signal to receiving cells through two receptor types , which act together to propagate the BMP signal within cells . To understand how BMP signaling is controlled , we used the nematode Caenorhabditis elegans to identify conserved regulators of BMP signaling . Here , we characterize SMA-10 , the first extracellular positive regulator of DBL-1/BMP receptor-mediated signaling . SMA-10 is a new member of a family with leucine rich repeats and immunoglobulin-like domains ( LRIG ) . SMA-10 physically binds the two types of DBL-1/BMP receptor . We demonstrate conservation of LRIG function by showing that a Drosophila melanogaster LRIG can functionally substitute for loss of C . elegans SMA-10/LRIG , that C . elegans SMA-10 can directly promote mammalian BMP signaling in cells , and that mammalian LRIG1 interacts with BMP receptors . Our work establishes a role for LRIGs in BMP regulation through binding both types of BMP receptor .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/molecular",
"development",
"developmental",
"biology/developmental",
"molecular",
"mechanisms",
"cell",
"biology/cell",
"signaling"
] |
2010
|
Caenorhabditis elegans SMA-10/LRIG Is a Conserved Transmembrane Protein that Enhances Bone Morphogenetic Protein Signaling
|
HEY bHLH transcription factors have been shown to regulate multiple key steps in cardiovascular development . They can be induced by activated NOTCH receptors , but other upstream stimuli mediated by TGFß and BMP receptors may elicit a similar response . While the basic and helix-loop-helix domains exhibit strong similarity , large parts of the proteins are still unique and may serve divergent functions . The striking overlap of cardiac defects in HEY2 and combined HEY1/HEYL knockout mice suggested that all three HEY genes fulfill overlapping function in target cells . We therefore sought to identify target genes for HEY proteins by microarray expression and ChIPseq analyses in HEK293 cells , cardiomyocytes , and murine hearts . HEY proteins were found to modulate expression of their target gene to a rather limited extent , but with striking functional interchangeability between HEY factors . Chromatin immunoprecipitation revealed a much greater number of potential binding sites that again largely overlap between HEY factors . Binding sites are clustered in the proximal promoter region especially of transcriptional regulators or developmental control genes . Multiple lines of evidence suggest that HEY proteins primarily act as direct transcriptional repressors , while gene activation seems to be due to secondary or indirect effects . Mutagenesis of putative DNA binding residues supports the notion of direct DNA binding . While class B E-box sequences ( CACGYG ) clearly represent preferred target sequences , there must be additional and more loosely defined modes of DNA binding since many of the target promoters that are efficiently bound by HEY proteins do not contain an E-box motif . These data clearly establish the three HEY bHLH factors as highly redundant transcriptional repressors in vitro and in vivo , which explains the combinatorial action observed in different tissues with overlapping expression .
NOTCH signaling is a key regulatory pathway for cardiovascular development and homeostasis [1] . Its receptors mainly act through transcriptional activation of target genes by a complex of the NOTCH intracellular domain , released by gamma-secretase , the transcription factor CBF1 ( RBP-Jk ) and the Mastermind co-activator proteins ( Maml1-3 ) . Without NOTCH binding CBF1 has a repressive function and associates with additional co-repressor proteins . Upon activation different and in part cell type specific target genes are induced , the most prominent ones encoding members of the HEY and HES family of bHLH repressor proteins . There are three HEY genes ( HEY1 , HEY2 and HEYL ) and several HES genes , with HES1 being the closest relative . All are related to the Drosophila hairy and Enhancer-of-split genes , which are well known transcriptional repressor proteins . HEY and HES proteins have a similar domain architecture with a DNA binding and dimer-forming bHLH ( basic helix-loop-helix ) region , an Orange domain that may also participate in dimerization and conserved C-terminal WRPW ( HES ) or YRPW ( HEY ) motifs . The WRPW peptide mediates interactions with groucho-type co-repressor proteins , but YRPW interaction partners for HEY proteins are still unknown . Mouse knockout studies have revealed a striking overlap in phenotypes between NOTCH and HES or HEY mutants suggesting that these bHLH factors convey a significant fraction of the known biological responses [1] , [2] . Loss of HEY2 or HEY1/HEYL leads to identical cardiac phenotypes with ventricular septum defects ( VSD ) and valve anomalies that appear to be due to an impaired EMT process of endocardial cells in the atrioventricular canal [3] . Since HEY1 and HEYL single knockouts do not show evidence of cardiac developmental defects these genes are obviously less critical in this process . Nevertheless , the overlap in endocardial expression and the overlap in phenotypes clearly argue for a combined and partially redundant action of all three HEY genes . Interestingly , there is also a cooperation of HEY1 and HEYL in skeletal muscle since double knockout mice lack quiescent satellite cells , which are essential for regeneration [4] . When HEY1 and HEY2 are deleted together a much earlier embryonic vascular defect is observed with failure of angiogenic remodeling and a lack of arterial differentiation [5] , [6] . Additional critical sites for HEY functions are the inner ear [7] , brain [8] and bone [9] . The apparent redundancy of HEY factors in many sites and their high degree of sequence identity in the bHLH region suggest that they may be functionally interchangeable , but there are also claims for a completely different function of HEYL e . g . in neurogenesis [10] . Evidence for this scenario is limited , however . Despite extensive analyses of mouse phenotypes surprisingly little is known about the direct targets of HEY or HES genes [2] . Microarray analyses of overexpressing cells or tissues from knockout animals have provided evidence for HEY dependent changes in gene expression in several settings [11]–[14] . There is very little overlap between target lists , however , and evidence for direct regulation of these genes by HEY proteins is largely lacking . To better characterize the network of genes that mediate NOTCH-HEY signaling effects in target cells we generated HEK293 cells that express HEY1 , HEY2 or HEYL in a highly regulatable fashion . These cells were used to search for HEY-dependent changes in transcript levels by microarray analysis and to identify direct binding sites of all three HEY factors in target genes . We could define putative binding motifs and validate DNA targets by promoter analysis . Analysis of cardiac tissue from knockout mice validates a number of these genes as direct in vivo targets .
To identify target genes of HEY factors we employed HEK293 cells with tightly regulated HEY transgene expression . HEK293 cells were chosen since they express endogenous HEY genes at significant levels ensuring that these cells are capable of responding appropriately . According to transcript profiling HEY1 ranks as number 525 of all expressed genes , while HEY2 and HEYL are expressed at lower levels with a rank of around 10 . 000 [15] . To generate a system with tunable HEY gene expression , cells were first transfected with pWHE134 [16] , encoding a reverse tetracycline transactivator ( rtTA ) plus a tetracycline-dependent repressor ( tetR-KRAB ) driven by a CMV promoter ( 293tet cells ) . Flag-HA- ( FTH ) -tagged HEY1 and Flag-HEY2 sequences under the control of a tetracycline-responsive promoter were subsequently introduced via lentiviral vectors ( for details see Materials and Methods and Figure S1A–S1D ) . For some of the experiments Flag-Strep- ( FS ) -tagged constructs were employed with similar results using transposon-mediated insertion ( FS-HEY1 , FS-HEY2 , and FS-HEYL ) . The N-terminal epitope tags do not affect localization or transcriptional activity of the proteins in vitro . Western Blot analysis and quantitative RT-PCR of individual clones confirmed tightly regulated doxycycline ( Dox ) -dependent expression ( Figure S1E ) . Experiments were either conducted with 30–50 ng/ml Dox for low level expression ( e . g . raising endogenous HEY1 mRNA level by 2–10 fold ) or with 1–2 µg/ml Dox for stronger overexpression . HEY regulated genes were identified after strong induction for 48 h ( 293tet-FTH-Hey1 ) or 72 h ( 293tet-Flag-Hey2 ) , respectively , using Affymetrix microarrays . Under more stringent cut-off values only a small number of genes appeared regulated . With relaxed criteria of ≥1 . 3× similar numbers of up- and down-regulated genes were identified ( Figure 1A and Table S1 ) . The number of target genes and the extent of regulation were greater for HEY2 , which may result from differences in protein levels due to longer induction , differential potency of the bHLH protein or cell-intrinsic mechanisms . Comparison of gene lists identified a strong overlap , but in all cases the span of regulation is small . Validation of microarray results was done by quantitative real-time RT-PCR ( qRT-PCR ) on a subset of genes ( Figure 1B ) . Repression could generally be confirmed and the extent of regulation tended to be higher , in the range of 2–6-fold . For upregulated genes validation was also successful in most cases , but the extent of regulation was more limited . Importantly , values obtained for HEY1 and HEY2 were more similar now , likely due to the same 72 h induction period . Especially for repressed genes the longer induction time may lead to larger changes since the half-life of target mRNAs becomes less of a problem . Expression of the endogenous HEY1 as well as HEY2 and the related HES1 gene was repressed , pointing to a negative feedback loop for these factors . These experiments were repeated for HEK293 cells with a regulated expression of Flag-Strep-tagged HEYL and all the genes tested exhibited very similar direction and extent of regulation . Thus , the three HEY factors appear functionally interchangeable , at least in HEK293 cells . Since HEK293 cells express endogenous HEY1 this may already lead to a repression at baseline . We therefore tested expression of target genes in a HEY1 knockdown situation . Using stably expressed shRNA we managed to reduce HEY1 RNA expression by 75% . Even this rather limited reduction had a clear impact on target genes expression ( Figure 1B ) . Several HEY-repressed genes were up-regulated up to 3 . 4-fold ( BMP2 ) , while at least some of the genes induced upon HEY overexpression tended to be repressed by HEY1 knock-down , further confirming the validity of our target genes . Gene ontology analysis of regulated genes identified a striking overrepresentation of genes related to transcriptional control as well as development and differentiation ( Figure 1C ) . The prevalence of transcriptional control genes suggests that HEY genes are positioned higher up in the hierarchy of signaling cascades and modulate other transcription factors rather than effector genes . The terms identified for morphogenetic processes include limb/skeletal development , neurogenesis , organogenesis of branching organs ( kidney , lung ) , cardiac and vascular development , which agrees with the dynamic spatio-temporal HEY expression patterns in embryos and implicates HEY genes in a broad spectrum of developmental decisions . For HEY1 , apoptosis-related genes are over-represented especially among upregulated genes . However , most of the strongly enriched GO terms are preferentially found for down-regulated genes , indicating that they form a more focused group . These also tend to exhibit higher ratios of expression changes , which is in agreement with the primarily repressive nature expected for HEY bHLH factors . The mode of HEY action has been questioned by publications implicating indirect mechanisms of transcriptional control despite the presence of a classical bHLH domain [summarized in 2] . Especially the lack of E-box target sequences in some of the few known HEY-repressed genes has cast doubt on direct DNA binding as the mode of action . We therefore tested four strong target genes ( HEY1 , KLF10 , BMP2 and FOXC1 ) in HEK293 cells for direct HEY binding by chromatin immunoprecipitation ( ChIP ) . 293tet-FTH-Hey1 cells were induced at low level to avoid oversaturation and HEY-bound DNA was captured using a Flag-antibody . In each case sequences from the proximal promoter region ( within 2 kb of the transcriptional start site ( TSS ) ) were efficiently enriched ( 10- to 60-fold ) in Dox-induced cells ( see also Figure 2D ) . Controls with non-induced cells or immunoprecipitations using unspecific IgG antibodies were both negative , demonstrating high specificity . Other conserved sequences further upstream ( −1 . 4 to −6 . 5 kb from TSS ) or intronic regions were not enriched . Experiments with HEY2 and HEYL ( not shown ) generated essentially the same results , indicating that HEY proteins bind to the proximal promoter regions of target genes in a similar , if not identical fashion . Further support for direct DNA binding came from experiments with a subtle HEY1 mutant , where conservative point mutations were introduced at three sites in the basic domain , which alter presumptive DNA contacting amino acid residues ( R50K , R54K , R62K; HEY1-RK3 ) ( Figure 2 ) . The mutant protein was expressed at similar levels upon Dox induction , it exhibits nuclear localization and it efficiently dimerizes with wild-type HEY1 ( not shown ) . Expression analysis of the target genes HEY1 , KLF10 , BMP2 and FOXC1 revealed that only wild-type HEY1 , but not HEY1-RK3 is capable of repression ( Figure 2C ) . In ChIP analysis HEY1-RK3 did not bind to the corresponding target promoters ( Figure 2D ) . Thus , the basic domain and its presumptive DNA contacting side chains are essential for the transcriptional activity of HEY1 . Since HEY proteins can directly bind to promoters of target genes we sought to identify the complete repertoire of potential HEY regulated genes through next-generation sequencing of ChIP-enriched DNA fragments ( ChIPseq ) . Non-induced cells were used as a reference . A total of 13–14 million reads were generated for HEY1 and HEY2 and around 90% of these could be mapped back to the human genome ( Tables S2 , S3 , S4 ) . In both cases approximately 10 , 000 high confidence binding sites could be identified ( criteria being a p-value of <10−5 and a peak height of ≥10 ) . To validate candidate genes of HEY1 and HEY2 , we tested peaks from 23 genes with different height ( 11 to 380 ) individually by quantitative PCR ( Figure S2 ) . Each binding site could be validated and the same DNA regions were also found to be targets of HEY2 ( not shown ) . HEY1 and HEY2 exhibit a remarkable similarity of binding profiles and in most cases peaks of ChIP-enrichment are superimposable ( Figure 3A ) . When binding sites are ranked , 59% of the top 1000 sites are shared between HEY1 and HEY2 . A further 37% of these sites are still among the top 5000 binding sites of the other factor , respectively ( Figure 3B ) . Thus , only a small minority of binding sites ( ≈4% ) may be divergent between HEY1 and HEY2 and upon manual inspection most of these are small peaks or the divergence is only of technical nature . The strong similarity of binding is also evident from the heat map generated for all HEY peaks , where very similar distributions of peaks are evident and potential differences seem to be limited to low-scoring sites ( Figure 3C ) . Binding sites are preferentially located in the proximal promoter region of genes or within exon 1: 55–62% of all peaks are within 500 bp of transcriptional start sites ( TSS ) and 66–76% fall within +/−2 kb ( Figure 4A ) . When the strictly intragenic peaks were counted more than one third each is located in exon 1 or in intron 1 , respectively ( Figure 4B ) . This suggests that HEY proteins likely act directly on promoter associated protein complexes and not through long range enhancer or silencer type mechanisms . The vast majority of binding sites ( >90% ) are located within CpG islands . This is especially true for peaks within +/−2 kb of the TSS ( 98% ) and to a lesser extent for more distal peaks ( 75% ) . HEY binding sites are located preferentially at active or poised promoters exhibiting H3K4me3 histone marks . In HEK293 cells approximately 20 . 000 promoters are characterized by the presence of Pol II [15] and the histone mark H3K4me3 [17] . Around one third of these sites is also bound by HEY1 or HEY2 , representing around 70% of all Hey peaks ( Figure 4C ) . In contrast , there is no evidence at all for HEY binding at silent promoters that lack Pol II/H3K4me3 marks . HEY bound active promoters have somewhat reduced average H3K4me3 values , which may correspond to the repressive capacity of HEY proteins . Gene Ontology analysis of the top 1000 peaks revealed that the promoters bound by HEY proteins are strongly biased towards transcriptional control and embryonic development genes ( Figure 4D ) . This corresponds well to the data obtained from the microarray analyses described above . The striking similarity of HEY1 and HEY2 binding patterns posed the question whether HEYL has the same preferences . This is clearly the case for genes used for ChIPseq validation , listed in Figure S2 ( data not shown ) . Preliminary analysis of ChIPseq data from induced 293tet-FS-HEYL cells revealed that the vast majority of HEY1/2 bound sequences are again bound by HEYL ( 95% of Hey1/2 peaks , Table S5 ) . We also identified a large number of additional binding sites that tend to be barely present and/or not significant in the analysis of HEY1 and HEY2 ( Table S5 ) . HeyL was expressed at somewhat higher levels compared to Hey1/2 , which may contribute to the detection of additional , previously not significant peaks . On the other hand , we have little evidence to major changes in Hey binding when cells were induced at lower or higher levels or even transiently transfected . Therefore the basis for the increase in binding sites for HeyL will have to be clarified in future studies before final conclusions can be drawn . Nevertheless , all three HEY proteins appear to bind to the same core of genomic sites with very similar preferences . To identify potential DNA binding motifs for HEY1 or HEY2 we searched the top 300 target sites ( +/−100 bp of peak location ) using bioinformatic tools . Sequence motifs that are overrepresented tend to be highly GC-rich since the average GC content in HEY peak regions is around 85% . To reduce the influence of this bias we carefully selected control regions from a set of promoters that are not bound by HEY factors , but display very similar GC profiles . Using the motifRG package we identified two motifs that resemble E-box sequences ( Figure 5A ) . Through binding site selection we had previously identified a class B E-box motif ( CACGTG/CACGCG ) as a preferred HEY binding site [18] , which turned out to be one of the two sequences in our list . One other sequence ( GCGCGC ) reached a similar score , but its relevance remains unclear . Electrophoretic mobility shift assays with purified HEY1 protein expressed in E . coli showed strong E-box binding ( CACGTG ) and efficient competition by the unlabeled oligonucleotide ( Figure 5B ) . The related CACGCG and CGCGCG sequences were much poorer competitors and their own binding to HEY1 could easily be competed by an excess of the prototypic class B site . Nevertheless , only a fraction of Hey peaks contain the CACGYG E-box sequence , suggesting that in vivo binding may employ an even more relaxed consensus or depend on additional interacting proteins . Promoter analysis with luciferase reporter assays validated HEY-dependent repression in vitro . In transient co-transfections several promoters like HEY1 , JAG1 , BMPR1A and NGN3 were efficiently repressed by cotransfection of HEY1 ( Figure 6 ) . Transfection of an activator construct encoding a fusion of the HEY1 bHLH-Orange sequences with the VP16 activation domain ( VP16-HEY1 ) in turn induced luciferase expression from the same reporter . The HEY1-RK3 protein with its impaired DNA binding capacity was incapable of efficient repression or activation . Each promoter contains at least one sequence motif that could serve as a HEY binding sequence . In the case of JAG1 a targeted mutation of the putative E-box motif ( gggCACGCGtca to gggCAtca ) fully abrogated responsiveness to HEY1 or VP16-HEY1 . This again demonstrates that HEY proteins directly bind DNA through E-box motifs and mediate repression of their target genes . Comparison of target lists for gene regulation and DNA binding further supports the concept of HEY proteins as direct repressors . Especially the genes with stronger repression on mRNA level frequently had ChIP peaks close by and peak height was much higher ( median 27 and 22 ) ( Figure 7 ) . Importantly , genes that were induced upon HEY expression did not have significant associated ChIP peaks ( median peak height 0 ) . This underscores the notion that repression of transcription appears to be a direct effect of HEY proteins on the corresponding promoters , while gene induction rather tends to be a secondary and indirect phenomenon . Hey genes are important for development of several organ systems including the heart as reported in numerous knockout studies . We therefore aimed to validate our HEY target genes in the mouse heart . To confirm the presence of HEY binding sites at corresponding genomic locations we repeated our ChIP experiments in HL-1 cells , a murine cardiomyocyte cell line , which maintains cardiac morphology and biochemical and electrophysiological properties in cell culture [19] . We were able to confirm 16 out of 18 HEY binding sites ( Figure 8A ) , indicating that the majority of HEY binding sites detected in HEK293 cells are also present in murine cardiomyocytes . Hey2 and Hey1/L knockout mice exhibit membranous VSDs and valve defects and overlapping expression in the critical endocardial cells of the AV canal [3] , [20] . Hey2 knockout hearts in addition show evidence of cardiomyopathy in the ventricles , which corresponds well to the fact that ventricular cardiomyocytes express only Hey2 , but not Hey1 or HeyL . We therefore tested dissected ventricles of Hey2−/− embryos at E14 . 5 for deregulation of Hey target genes . A series of genes tested exhibited a clear and highly significant up-regulation in knockout embryos by quantitative real-time RT-PCR ( Figure 8B ) . In contrast , Hey1 and HeyL are not expressed in the ventricles and in the knockout situation there is only limited deregulation of the same set of genes where only induction of Sema6d reaches statistical significance . To extend these findings we also tested ventricles from animals with a global Hey1 over-expression [9] . In this case , most of the genes up-regulated in Hey2−/− mice were down-regulated ( Figure 8B ) with the lower amplitude likely being due to endogenous Hey2 already being present . This clearly documents that Hey repression of target genes is functional in the mouse in vivo with an induction of these genes in the knockout situation .
HEY genes have been described as repressors of a small number of individually tested target genes that had been identified serendipitously by various means [summarized in 2] . To search for additional HEY regulated genes we chose HEK293 cells as these are easy to manipulate and they express endogenous HEY genes suggesting that they can react to altered HEY protein levels in a physiologically relevant manner . We obtained very similar patterns of expression changes for HEY1 and HEY2 , both on microarrays as well as in confirmatory real-time RT-PCR . Even the more divergent family member HEYL led to concordant regulation of the target genes tested . Surprisingly , the level of regulation was rather limited in all cases . HEY1 itself is the strongest down-regulated gene ( 3–6-fold ) , indicative of an important negative feedback loop . For HEY2 and HEYL the repression was also seen , but less pronounced . This negative feedback loop may be similar to the ones described for Hes1 and Hes7 that are important in somitogenesis and neural stem cell biology [21] . The generally modest expression changes suggest that HEY genes rather modulate existing gene transcription instead of completely switching expression states . On the other hand , preexisting HEY1 mRNA and protein in HEK293 cells may already induce a level of repression that can be further enhanced to a limited extent only and this is supported by our experiments with Hey1 shRNA , where an induction of several target genes could be seen . The study by Xin et al . [13] likewise reported a small number of HEY2 regulated genes , where only three structural genes showed regulation in the range of 5–9-fold , which is in line with our data . Gene regulation on mRNA level could be due to direct or indirect effects of HEY proteins on target promoters . This distinction became more relevant as HEY proteins led to induction and repression of comparable numbers of transcripts . ChIP analyses are an excellent tool to generate additional evidence for a direct mode of action . In these experiments we relied on a rather limited overexpression of HEY genes in order to still mimic a physiological situation . Nevertheless , we identified a very large number of around 10 , 000 target sites in HEK293 cells with almost identical profiles for HEY1 and HEY2 . Differences are mostly restricted to less enriched target sites . This translates to a Pearson's correlation of r = 0 . 75 between HEY1 and HEY2 , which is close to the value of r = 0 . 83 obtained for biological replicas in other studies [22] , indicating that HEY1 and HEY2 regulate the same targets . For HEYL an even larger number of peaks was identified . While the reason for the increased number of binding sites is still unclear , the vast majority of HEY1 and HEY2 peaks were again seen in our HEYL dataset , supporting the idea of strongly overlapping functions . There is a striking discrepancy , however , between the large number of ChIP peaks and the much smaller number of genes regulated by HEY proteins . The vast majority of binding sites observed may thus not contribute to gene regulation , or else endogenous HEY proteins may have already exhausted the regulatory potential at some of these sites . On the other hand , an overabundance of bound DNA sequences has been observed for other transcription factors before , like the bHLH factors MYC or MYOD that yielded comparable results [23] , [24] . Given the probably limited protein concentration it even appears questionable if all sites will be occupied simultaneously in any given cell and rather points to a high turnover rate . For c-MYC , another E-box binding protein , a two-step model of initial binding to open chromatin followed by more relevant sequence specific binding has been put forward [25] . Functionally active binding sites may also emerge only through additional modifications or concomitant binding of additional factors to form fully functional complexes . A possible scenario to explain HEY functions might therefore include a general preference of HEY factors for genes with an open chromatin configuration , where the actual transcriptional change then depends on circumstances like cell type and differentiation status . It remains to be established if and how HEY functions can be described by such models . The mode of regulation by HEY proteins appears to be rather uniform . The vast majority of binding sites were found in close proximity to transcriptional start sites . This rather implicates HEY proteins in direct interactions with the basal transcriptional machinery or local chromatin at promoters as opposed to long range enhancer type mechanisms . The majority of HEY-repressed genes appears to be direct targets since they contain strong HEY binding sites within the promoter or 5′ UTR regions . On the other hand , genes activated by HEY proteins are likely regulated in an indirect manner: more than half of them do not contain relevant peaks at all and peak height was generally rather small , suggestive of HEY expression leading to a reduction in other critical transcriptional activators for those genes . Direct repression of target promoters could also be verified in vitro by luciferase reporter assays . As reported in earlier studies by us and others , HEY1/2/L can repress target promoters up to ten-fold [14] , [26] . Our current experiments provide important additional evidence for a HEY function as direct transcriptional repressor: Firstly , the bHLH-Orange domain can be turned into an activator of transcription when fused to the strong VP16 activation domain . Furthermore , changing only three arginine residues that presumably contact DNA into lysine completely abolished DNA binding and transcriptional response of this mutant . This clearly establishes HEY proteins as direct DNA binding transcriptional repressors , while gene activation by HEY proteins appears to be indirect as the promoters are largely devoid of HEY target sites . A putative DNA binding motif for HEY proteins of tggCACGYGcca has previously been defined by in vitro oligonucleotide selection [18] . However , in most studies the core consensus E-box site CACGYG was either not present in the small number of putative target promoters analyzed previously , or deletion of related E-box sites did not alter expression of luciferase reporter constructs [summarized in 2] , leading to proposals of indirect HEY functions . Here we could show that deletion of an E-box site in the JAG1 promoter abolishes HEY regulation in luciferase assays . Related findings have recently been published for the IDE promoter [27] . This shows that at least for some HEY target genes the E-box motif is required for Hey regulation . De novo motif discovery in our ChIPseq data set also led to the identification of an E-box motif of CACGYG as one of the two top candidates . Finally , a search of all known DNA binding motifs likewise recovered myc-type E-box sequences as being highly enriched ( not shown ) . While these data clearly demonstrated that E-box sequences can be bound by HEY proteins in vivo , this does not fully explain the genomic binding patterns observed since many of the bound regions do not contain such motifs . HEY proteins may either use less stringent criteria for DNA binding in vivo or they might also bind in a sequential manner that initially does not fully rely on sequence specificity as suggested by Perna et al . [25] . Another possibility would be the need for additional cooperating factors that bind in the vicinity or form ternary complexes to ultimately affect gene expression , but this will depend on the characterization of novel HEY binding partners . The observed co-occurrence of binding sites for factors like SP1 , E2F , AP2 , NRF and EGR is expected at promoter-proximal regions , but may also hint at potential interactions of HEY proteins with some of these factors . The rather limited extent of gene repression by HEY proteins is also reflected in the chromatin signature of the corresponding promoters . There is a striking overlap of HEY bound sequences with the presence of polymerase II ( Pol II ) and the active chromatin mark H3K4me3 , which are preferentially found at active and poised promoters . This again argues in favor of a modulatory role of HEY proteins with just limited alterations in gene expression . In human ES cells Pol II and H3K4me3 marks have been identified at silent genes as well , however , and it has been suggested that the critical step lies in transcription elongation . Interestingly many developmental regulators fall within this group of genes [28] , [29] , to which a significant fraction of HEY target genes belongs as well . The location of a large number of HEY binding sites just downstream of the transcriptional start site would ideally position HEY to influence the pausing vs . elongation switch of Pol II . Previous studies have suggested redundancies between HEY1 , HEY2 and HEYL that manifest in distinct phenotypes in single and combined KO mice due to partly overlapping expression profiles [3] , [5] , [6] . The striking overlap in gene regulation and the highly related patterns of ChIPseq peaks indicates that all three HEY proteins indeed elicit very similar responses in a given cell type . This is consistent with the idea that the expression patterns of HEY factors largely define the outcome of knockout studies , whereby no individual , intrinsic functional properties , but overall and cumulative Hey expression levels would be critical . On the other hand , the substantial divergence in the poorly conserved C-terminal half of the proteins is suggestive of a significant potential for paralog-specific functions that may yet have to be uncovered . The identification of either fully shared or paralog-specific protein interaction partners of HEY factors may help to shed light on this important issue .
HEK293 cells were cultured in DMEM medium ( PAN Biotech , Aidenbach , Germany ) containing 10% FCS , 50 U Penicillin and 50 µg/ml Streptomycin . 293tet cells were generated by transfection with PvuII linearized pWHE134 plasmid [16] using polyethylenimine ( 3 µg DNA , 6 µl PEI per 6-well plate for 8 h ) followed by selection with 0 . 5 mg/ml G418 . HEY expressing cells were produced by lentiviral transduction of 293tet cells with p199-FTH-hHey1-iEP , p199-Flag-mHey2-iEP constructs based on p199 plasmids [30] ( for maps see Figure S1 ) . For regulated HEYL expression pTol2-FS-mHeyL-iEins-WHE carrying insulator sequences ( HS4ins ) and the complete tet-regulatory module from pWHE459 [31] was introduced into HEK293 cells by Tol2-mediated transposition with pKate-N/Tol2 [32] followed by puromycin selection ( 1 µg/ml ) . The HEY1 knockdown was generated by lentiviral transduction of HEK293 cell with shRNA vectors ( Open Biosystems clone ID V3LHS_404238 ) . In all cases individual colonies were picked and validated separately . The HEY1-RK3 mutant was generated by PCR-mediated mutagenesis using primers spanning the altered sites . All constructs were verified by sequencing . HL-1 cells were cultured in Claycomb medium ( Sigma-Aldrich , Munich , Germany ) containing 10% FCS , 100 µM norepinephrine , 4 mM L-glutamine , 50 U Penicillin and 50 µg/ml Streptomycin . For ChIP 5*106 HL-1 cells were transiently transfected with 8 µg plasmid DNA using 100 µl Ingenio Buffer ( Mirrus , Madison , USA ) and the Amaxa Nucleofector II electroporator ( program T-20 , Lonza , Basel , Switzerland ) . After 48 h of culture , cells were used for ChIP . Hey1 , Hey2 and HeyL knockout lines have been described before [3] , [5] . The Act-Hey1 transgenic line expressing Hey1 under the control of the ß-actin promoter was obtained from M . Susa ( NIBR , Basel ) [9] . Total RNA was extracted either from cells or tissue samples using TriFast ( peqGOLD , Peqlab , Germany , Erlangen ) according to the manufacturer's protocol and quantified by OD260 nm measurements using a spectrophotometer ( NanoDrop ND 1000 , Peqlab ) . Total RNA of control and dox-induced cells ( 1–2 µg/ml doxycycline for 48–72 h ) was used for microarray analysis on Human Genome U133 Plus 2 . 0 Gene Arrays ( Affymetrix , Santa Clara , CA ) . Labeling and washing were performed according to the standard Affymetrix protocol . The arrays were scanned using a GeneChip Scanner 3000 ( Affymetrix ) . Data analysis and quality control was done using different R packages from the Bioconductor project ( www . bioconductor . org ) . Probe sets were summarized using the RMA algorithm and resulting signal intensities were normalized by variance stabilization normalization [33] . 2 µg RNA were reverse transcribed using the Revert Aid First-Strand cDNA synthesis Kit ( Fermentas , Lithuania , Vilnius ) with oligo ( dT ) primers . qRT-PCR was performed with an iCycler iQ5™ Real-Time PCR Detection System ( BioRad , USA , Hercules ) . Primer sequences are listed in Table S6 . Reactions contained 1/50 of the cDNA reaction and PCR was performed with annealing at 60°C and SybrGreen quantification . PCR products were confirmed by melting curve analysis and agarose gel electrophoresis . The housekeeping gene HPRT was used to normalize expression levels . All measurements were performed at least twice and mean values were calculated . 5*106 HEK293 cells were induced with 50 ng/ml doxycycline for 48 h to obtain low level overexpression of HEY proteins . The same amount of cells was kept uninduced as control . HL-1 cells were transiently transfected with pCS2p-Flag-Hey1 , pCS2p-Flag-Hey2 [14] and pll3 . 7 , which was used as control . Cells were harvested as described earlier [34] . Briefly , the cells were fixed with 1% paraformaldehyde for 10 min at room temperature . Fixation was stopped by adding glycine to 0 . 2 M and cells were washed three times with ice-cold PBS and harvested . All subsequent steps were done at 4°C . The cells were lysed in cell lysis buffer ( 50 mM Hepes-KOH pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% TritonX-100 , 0 . 1% Deoxycholate , 0 . 1% SDS ) and spun down . The resulting pellet of nuclei was lysed in nuclei lysis buffer ( cell lysis buffer containing 1% SDS ) and sonicated using a Digital Sonifier W-250 D ( Branson Ultrasonics , USA , Danbury ) . Debris was removed by centrifugation . For immunoprecipitation , Chromatin was diluted five-fold with ChIP buffer ( 0 . 01% SDS , 1 . 1% TritonX-100 , 1 . 1 mM EDTA , 20 mM Tris pH 8 . 0 , 167 mM NaCl ) and 550 µl of diluted chromatin was mixed with 45 µl 1∶1 protein G agarose slurry ( in cell lysis buffer ) and 4 µg antibody ( αFlag-M2 or rabbit IgG , Sigma-Aldrich ) and incubated overnight . Then , the agarose beads were washed three times with cell lysis buffer , once with washing buffer ( 50 mM Hepes-KOH pH 7 . 5 , 350 mM NaCl , 1 mM EDTA , 1% TritonX-100 , 0 . 1% Deoxycholat , 0 . 1% SDS ) and once with LiCl washing buffer ( 10 mM Tris-HCl pH 8 . 0 , 250 mM LiCl , 1 mM EDTA , 0 . 5% Nonidet P-40 , 0 . 5% SDS ) . Elution was performed with elution buffer ( 50 mM Tris pH 8 . 0 , 10 mM EDTA , 1% SDS ) at 68°C for 30 minutes . The eluted chromatin was incubated with 0 . 8 mg/ml Proteinase K and PFA fixation was reversed by incubation at 68°C overnight . The DNA was then purified using phenol-chloroform extraction , precipitated and quantified using PicoGreen ( Invitrogen , USA , San Diego ) . For ChIPseq the same protocol as for ChIP was used in principle , but 2 . 5*108 cells were employed and after lysis of nuclei , chromatin was spun down at 20 . 000 rpm ( SW 41 TI rotor , Beckman , USA ) , washed twice with cell lysis buffer and sonicated in 1 ml cell lysis buffer per 100 µl chromatin pellet . For ChIP 10 µg antibody was used . 7–12 ng of ChIP DNA was subjected to sample preparation using the NEBNext ChIPseq sample preparation kit ( New England Biolabs , Ipswich , USA ) according to the manufacturer's instruction . Briefly , DNA was end-polished with T4 DNA polymerase and kinase . After column purification , Illumina adaptors were ligated to the ChIP DNA fragments . For HEY1 and HEY2 fragments were subjected to 15 cycles of PCR amplification and DNA with a length of 200–350 bp was excised from an agarose gel using Qiagen gel extraction kit . For HEYL 175–225 bp fragments were first excised and then amplified by 18 cycles of PCR . The DNA fragments were sequenced on an Illumina GAIIx platform ( Illumina , USA , San Diego ) . 36 bp sequences were generated and mapped to the hg19 genome by bowtie 0 . 12 . 7 [35] with standard parameters . These raw sequencing data were further analyzed using the peak finding algorithm MACS 1 . 4 . 1 [36] using sequences from uninduced cells as control to identify the putative binding sites . All peaks with a minimum p-value of 10−5 and a minimum height of 10 were included . The uniquely mapping locations for each factor were used to generate the genome-wide intensity profiles , which were visualized using the USCS genome browser . PeakAnalyzer [37] was used to annotate peaks and to calculate overlaps between different bed files . Heat maps were generated using seqMiner 1 . 2 . 1 [38] with K-means raw clustering . GO terms analysis was performed with DAVID 6 . 7 [39] using the functional annotation clustering method and allowing only biological processes . Clusters were named based on interpretation of enriched GO annotations . The R-package motifRG ( Bioconductor package motifRG , Zizhen Yao , manuscript in preparation ) was used to identify binding motifs , using sequences +/−100 bp around the summit of the top 300 highest ranking peaks . Unrelated sequences with a similar distance towards transcription start sites of genes lacking ChIPseq peaks and with similar GC distribution were selected and used as control/background . The JAG1 luciferase construct containing the potential Hey binding sequence tgaCGCGTGccc was mutated by cutting with MluI ( ACGCGT , Fermentas ) , followed by Mung Bean Nuclease ( Fermentas ) treatment and religation using T4 DNA ligase ( Fermentas ) . This results in a four base pair deletion . For luciferase assays approximately 104 HEK293 cells were transiently transfected with 250 ng of the luciferase promoter construct and 50 ng of the regulatory HEY construct in a 24-well format . Cells were harvested after 48 h and lysed in 150 µl lysis buffer ( 25 mM Glycyl-Glycine pH 7 . 8 , 15 mM MgSO4 , 15 mM KPi , 4 mM EGTA , 1 mM DTT , 1% Trition-X100 ) . After incubating for 10 min at room temperature cells were pelleted and 50 µl of the supernatant were measured in a GLOMAX 96 microplate luminometer ( Promega , USA , Madison ) using 150 µl assay buffer ( lysis buffer with , 1 mM ATP , 0 . 1 µg/µl D-Luciferin ) . All measurements were done in triplicates . EMSA was performed using binding buffer ( 20 mM Tris pH 7 . 6 , 100 mM KCl , 0 . 5 mM EDTA , 0 . 1% Nonidet P-40 , 1 mM MgCl2 , 1 mM DTT , 10% glycerol ) and 1 ng recombinant MBP-HEY1 protein , 1 µg poly-dAdT , 5 ng biotin-labeled probe and either 0 , 25 , 75 or 250 ng competing unlabeled probe in a total volume of 10 µl . After incubating on ice for 30 min , samples were loaded on a 6% polyacrylamide gel and later blotted onto Amersham Hybond N+ membranes ( Amersham , UK ) . Detection was done using the PIERCE LightShift Chemiluminescent EMSA kit according to the manufacturer's recommendations ( PIERCE , USA , Rockford ) . HEK293T cells were transfected with either HEY1 or HEY1-RK3 expression plasmids 24 hours prior to fixation with 4% PFA . After blocking with 5% goat serum/0 . 3% Triton X-100/PBS , the Flag antibody ( rabbit; Cell Signaling , Danvers , Massachusetts ) was added ( 1∶800; o/n , 4°C ) . After washing in PBS at RT the secondary antibody Alexa488-rabbit ( Bio-Rad ) was used ( 1∶2000; 1 h , room temperature ) . Nuclei were stained using Hoechst33342 ( 1∶10000; Roth , Karlsruhe , Germany ) and subsequently cover slides were mounted in Mowiol . Pictures were taken using a Leica AF6000 fluorescence microscope . The ENCODE ChIPseq data for H3k4me3 was downloaded from “ftp://encodeftp . cse . ucsc . edu/pipeline/hg19/wgEncodeUwHistone/” ( Producer: University of Washington ) [17] and the ChIPseq data for PolII was downloaded from “http://www . ncbi . nlm . nih . gov/gds ? term=GSE11892” [15] .
|
NOTCH signaling is a central developmental pathway that influences a multitude of cell fate decisions and differentiation steps as well as later tissue homeostasis and regeneration . The three HEY genes encode basic helix-loop-helix transcription factors that are critical effectors to convey signaling by NOTCH receptors and similar signaling systems . This is underscored by the multitude of developmental defects observed in HEY single- and double-mutant mice . The mode of action of HEY proteins remained largely unexplored , however . By gene expression analysis and chromatin immunoprecipitation we have now identified a large set of HEY target genes . While only 500–2 , 000 mRNAs are regulated by HEY1 or HEY2 , there are around 10 , 000 binding sites in the genome . HEY proteins act as transcriptional repressors that bind close to transcriptional start sites in all cases tested . In contrast , gene activation seems to be mediated by indirect/secondary mechanisms . The extent of regulation is rather limited , implicating HEY genes in modulating rather than switching on or off target gene expression . All our in vitro and in vivo data point to a high degree of redundancy between the three HEY genes , suggesting that tissue specific patterns and expression levels determine the final outcome of HEY induced cellular responses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"genome",
"expression",
"analysis",
"functional",
"genomics",
"gene",
"regulation",
"dna",
"transcription",
"gene",
"function",
"cardiovascular",
"genome",
"sequencing",
"developmental",
"biology",
"organism",
"development",
"molecular",
"development",
"molecular",
"genetics",
"epigenetics",
"chromatin",
"sequence",
"analysis",
"gene",
"expression",
"organogenesis",
"biology",
"molecular",
"biology",
"congenital",
"heart",
"disease",
"microarrays",
"signal",
"transduction",
"nucleic",
"acids",
"signaling",
"genetics",
"vascular",
"biology",
"genomics",
"molecular",
"cell",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics",
"cell",
"fate",
"determination"
] |
2012
|
Target Gene Analysis by Microarrays and Chromatin Immunoprecipitation Identifies HEY Proteins as Highly Redundant bHLH Repressors
|
Male and female tsetse flies feed exclusively on vertebrate blood . While doing so they can transmit the diseases of sleeping sickness in humans and nagana in domestic stock . Knowledge of the host-orientated behavior of tsetse is important in designing bait methods of sampling and controlling the flies , and in understanding the epidemiology of the diseases . For this we must explain several puzzling distinctions in the behavior of the different sexes and species of tsetse . For example , why is it that the species occupying savannahs , unlike those of riverine habitats , appear strongly responsive to odor , rely mainly on large hosts , are repelled by humans , and are often shy of alighting on baits ? A deterministic model that simulated fly mobility and host-finding success suggested that the behavioral distinctions between riverine , savannah and forest tsetse are due largely to habitat size and shape , and the extent to which dense bushes limit occupiable space within the habitats . These factors seemed effective primarily because they affect the daily displacement of tsetse , reducing it by up to ∼70% . Sex differences in behavior are explicable by females being larger and more mobile than males . Habitat geometry and fly size provide a framework that can unify much of the behavior of all sexes and species of tsetse everywhere . The general expectation is that relatively immobile insects in restricted habitats tend to be less responsive to host odors and more catholic in their diet . This has profound implications for the optimization of bait technology for tsetse , mosquitoes , black flies and tabanids , and for the epidemiology of the diseases they transmit .
An Excel spreadsheet was provided with a series of square “maps” , composed of 200×200 cells representing a total 2×2 km . If flies had to be allowed to move off the maps , each map was assumed to adjoin mirror-image maps on all four sides , so that the number of flies leaving the map at any point was equal to the number entering there . If very long bands of habitat had to be considered , the bands were fitted into the maps by making the bands take a right angle bend at intervals of nearly 2 km . Each cell had a formula which displayed a number indicating the number of flies associated with events during a step period . Starting with a map at the top of the spreadsheet , and working down through other maps below , the following stages of calculation were performed , some of which required several maps . Calculations were controlled by the Visual Basic for Applications facilities associated with Excel and which set Excel to iterate for a number of times equal to the number of step periods required . At each iteration the calculations passed down the spreadsheet , performing stages 1–7 in succession .
Tsetse flies ( Glossina spp . ) occupy about ten million square kilometers of sub-Saharan Africa [1] . They feed exclusively on vertebrate blood and , in so doing , transmit those trypanosomes , namely Trypanosoma brucei rhodesiense and T . b . gambiense , that cause sleeping sickness in humans . These trypanosomes , together with others such as T . vivax , and T . congolense cause the disease of nagana in domestic animals . Host location by tsetse [2] , [3] is thus a key aspect of disease dynamics . Moreover , understanding the host-orientated behavior of tsetse has led to several cost-effective means of attacking the flies [1] , [4] , [5] , and could have implications for current and prospective methods of controlling mosquitoes , such as the use of bed-nets [6] , insecticide-treated livestock [7] , odor-baited traps [8] and genetically-modified vectors [9] . The various species of tsetse divide into the so called “forest” , “riverine” and “savannah” groups , of which only the latter two groups are epidemiologically important . The savannah species occupy extensive blocks of deciduous woodland and transmit mostly nagana [1] . whereas the riverine species are important vectors of both nagana and sleeping sickness and typically occur in evergreen woodland near water bodies The two groups of main vectors differ in at least four important ways: ( i ) savannah flies displace by an average of about 1 km/day [10] , while riverine flies displace only about a third as much [11]; ( ii ) savannah tsetse commonly feed on large hosts such as warthog , kudu and elephant , while small animals such as lizards form much of the diet of riverine tsetse [12]; ( iii ) the response of savannah tsetse to odor is several times greater than for riverine tsetse [13]; ( iv ) savannah tsetse are strongly repelled by humans [2] , whereas riverine flies are not [14] , [15] , [16] . These contrasts have led to marked differences between the designs of insecticide-treated screens , called targets , used to control each group [16] . For savannah tsetse the targets are 1–2 m2 and baited with artificial ox odor [17]; for riverine tsetse the targets are as small as 0 . 06 m2 and used without odor [18] . The distinctions between the behavior of riverine and savannah tsetse seem anomalous . For example , the avoidance of humans by savannah flies is usually attributed to the high risks of feeding on a type of host adept at killing probing insects [2] , but the risks should be high for riverine flies too , so why are riverine flies not equally averse to humans ? If savannah tsetse rely heavily on odor attraction , why do riverine flies not do so ? Moreover , since riverine tsetse feed off small animals and land on tiny targets , why do savannah tsetse disregard such baits [19] . To explain these anomalies we hypothesized that the distinctive responses of riverine and savannah tsetse to baits is associated directly with the way that the overall size and shape of different habitats affect fly mobility , devoid of any distinctions in the innate behavior of the two groups of tsetse . This hypothesis is an extension of the experimental and theoretical evidence that various arrangements of dense bushes inside the habitat restrict the movement of tsetse and so alter the catches at baits [20] , [21] . It resonates with indications from studies with other creatures that habitat geometry can be important in a variety of matters such as speciation [22] , species coexistence in predator-prey relationships [23] , the dynamics of such relationships [24] , and population abundance [25] . While much of the behavioral impact of dense bushes within tsetse habitat has been established by experiments in the real world , involving small-scale manipulations of bush arrangements [20] , [21] , manipulations on a much larger and impractical scale would be required for field tests of the hypothesis that the behavior of tsetse is governed also by the overall size and shape of the habitat . Hence , we used a deterministic model to simulate within a Microsoft Excel spreadsheet the impact that the overall shape and size of habitats , together with the arrangement of bushes within them , has on tsetse displacement , catches at experimental baits , feeding success , host selection , and the efficacy of various types of target .
There were no ethical issues since all work was theoretical . The spirit of the modelling was that a cohort of flies that had started its feeding cycle moved about the habitat , encountering visual and/or odor cues from various natural or artificial baits and then fed on the baits , or was killed by them , with a probability appropriate for each bait type . Flies fed or killed at various times during the cycle were accumulated and removed from the simulation .
The ability to find stationary baits depends largely on displacement rate [19] . The principles applying to this rate were elucidated by seeding flies in the central cell of a band or block of good habitat and allowing them to execute the average daily allocation of 1000 steps , in the absence of natural death or removal by baits . Blocks were in a checker-board arrangement with poor habitat so that flies could diffuse between blocks of good habitat , albeit slowly . Bands were flanked by no-go areas to focus only on movement within the band . The results with different widths of blocks and bands indicate that at widths of 10 m the displacement was only 43–64% of the displacement in homogeneous habitat ( Fig . 2 ) . The figures increased with increasing widths , but were still only 76–85% at widths of 450 m . At any given width , the displacement in a block was less than in a band . The complex curve for blocks was associated with the change in the ratio of perimeter to area , and hence the proportion of flies located where they could step out of the block . To assess the effect of heterogeneity within the overall shapes of habitats , cells of no-go vegetation simulating impenetrable bushes [20] , [21] , were located within habitats of various shape . Findings from simulations with a variety of bush arrangements are exemplified ( Fig . 3 ) by data for a 50 m-wide band with either no bushes , or each of four different bush arrangements , and for a large block composed of such bands placed parallel and adjacent to each other , with the adjoining parts of each band being mirror images . The rate of displacement tended to decline as: ( i ) numbers of bushes increased , ( ii ) flight paths between dense vegetation became more tortuous , and ( iii ) the abundance of dead-ends rose , so that the flies expended much flight on retracing their steps . Although real bushes in the field are unlikely to show the sort of serially repeated arrangements modelled above , the overall effects are likely to be similar . Allowing that riverine habitat occurs in bands or small blocks , and is often more densely bushed than savannah , the above results match field observations that tsetse displacement is greatest with savannah tsetse [10] , [11] , [39] . For simplicity , subsequent modelling assumed that all habitats contained no dense bushes . With that assumption the differences found between the efficacy of baits in riverine habitats and large blocks of savannah tend to be conservative indications of real differences . The relative importance of visual and olfactory stimuli is commonly estimated in the field by comparing catches from a host animal with those from an odorless model animal of the same size [2] , [40] . In simulating such experiments , the two types of bait were operated for six days in a crossover design , alternating between sites that were sufficiently far apart to ensure that the baits there did not compete with each other . The baits were present for half of the daily step periods each day , consistent with the fact that field catches of tsetse are often made in the afternoon only [2] . The simulated catch with each bait was expressed as a percent of the initial abundance of tsetse per square kilometer of the good habitat , and the efficacy of odor relative to visual stimuli was taken as the percent by which the addition of odor increased the catch above that with visual stimuli alone . As expected , catches and odor efficacy increased with bait mass ( Table 2 ) . Intriguingly , catches declined markedly on going from the large block of habitat to the bands , but the decline was greatest with the large baits and when odor was used . Consequently , bait size was relatively unimportant in the bands , and the percent efficacy of odor in the narrowest band was around a quarter of the efficacy in the large block . Similar indications were produced when the baits were operated in habitat restricted to small blocks . For example , when the block consisted of just one cell , the catch with the lizard was >99 . 9% of the catch with the elephant and percent efficacy of odor was <0 . 1% with either animal . Outputs for the percent efficacy of odor in the large block accord well with field data for savannah flies . For example , for G . m . morsitans and G . pallidipes in the field , the relative efficacies of odor with an ox ( 454 kg ) , donkey ( 204 kg ) kudu ( 136 kg ) warthog ( 82 kg ) and bushpig ( 73 kg ) averaged 435% , 175% , 89% , 56% and 73% , respectively [2] . More remarkably , outputs for the bands or small blocks accord well with the limited field efficacy of odor against riverine tsetse [13] , despite the model's provisions that the innate responsiveness and mobility of flies in the bands was exactly the same as in the large block . Hence , habitat geometry , irrespective of any innate behavioral distinctions , can account for most differences between patterns of field catches of savannah and riverine tsetse . Simulations were made with various densities of large and tiny targets ( Fig . 1 ) operated continuously in a large block or 10 m-wide band . As in field campaigns against riverine tsetse , tiny targets were used without odor , but large targets were modelled with and without artificial ox odor , according with the field use of large targets against savannah and riverine flies , respectively . In keeping with field catches at targets [17]–[19] , the numbers of targets required to achieve a given rate of kill differed greatly between the large block and the band ( Fig . 4 ) . To interpret the outputs it can be taken that an imposed death rate of about 4% per day , or 12% per feeding cycle , reduces field populations of tsetse by 99 . 99% per year , leading to population elimination in the absence of invasion [16] . On that basis , outputs accord with field indications for the numbers of various sizes of target needed to control savannah [41] and riverine [42] tsetse , and for the efficacy of odor with targets in savannah [2] and riverine [43] habitats . Hence , the results offer further support for the hypothesis that habitat geometry , not differences in innate behavior , determines much of the distinctive availabilities of riverine and savannah tsetse . To explore the abilities of various sizes and population densities of hosts to support the tsetse population , it was assumed that flies fed only on those stationary hosts that the model introduced , so no allowance was made for feeding on any other animals . Feeding success was scored after four days when fed flies had replenished their food reserves after an average of around three days , i . e . , the normal length of the hunger cycle . It was also scored after six days , when flies were about to die of starvation . Since some flies died of causes other than starvation , percent feeding success could not reach a full 100% . As expected from the above work with targets and simulated field catches , the host numbers required to allow a given level of feeding success were much greater in a narrow band than in the large block , and the efficacies of the various hosts differed greatly in the block but relatively little in the band ( Fig . 5 ) . Thus , in the large block , about 15–30 lizards led to the same feeding success as one elephant , but in the band only about 2–3 lizards were required . As in other modelling [44] , the number of flies discovering hosts decreased substantially when hosts were grouped instead of being singly and evenly distributed . Consider , for example , a population of lizards at an overall density of 100/km2 in a band of habitat 10 m wide . When the lizards were distributed singly and evenly the 4-day feeding success was 25% , but dropped to only 2% when the lizards occurred in evenly distributed groups of four , with each group involving a lizard in each cell of a line of four cells along the axis of the band of habitat . In a large block of habitat the comparable figures for feeding success were 65% for lizards distributed singly , as against only 11% for the grouped lizards . The outputs ( Fig . 5 ) are consistent with the abilities of known host populations to support tsetse . Thus , savannah tsetse at Sengwa , Zimbabwe , were maintained by a mixed population of hosts comprising an average of ten warhogs , plus two elephants and several kudu and other bovids per square kilometre [45] . Moreover , the model's indications that tsetse in restricted habitats can be supported largely by small hosts such as lizards , with population densities of around 50–100/km2 [46] , agree with the frequency of lizards and other small creatures in the blood-meal identifications of riverine tsetse [12] . Mobility has thus far been assumed to be the same for all flies . However , female tsetse displace at a greater rate than males [10]; young flies with poorly developed flight muscles [47] and old flies with damaged wings displace relatively little , and daily flight times can double or halve according to seasonal temperature [26] . To simulate this variability , the daily number of flight steps was increased or decreased threefold . As expected , the greater the mobility of flies the sooner they fed . However , it was more instructive to consider what this implied about the extent to which flies could afford to be selective about feeding on hosts they encountered . To explore this , the model's map was provided with an even spread of hosts . At different points in the feeding cycle , calculations were then made of the probability that flies that did not feed at that point would die of starvation . In any given habitat , and with any given size and abundance of host , this probability increased with the number of host-searching days completed . It increased also with a reduction in the number of step periods allowed per day and was greater in the narrow band than in the large block . The latter phenomena are illustrated by considering outputs with kudu at 16/km2 , which represents roughly the abundance and mean size of the main hosts , i . e . , warthogs , elephants and kudu , that sustained the tsetse population in the savannah at Sengwa [45] , discussed above . Simulations were also made with host populations consisting of lizards at 100/km2 , to be closer to a host situation more typical of riverine habitats [46] . The results show that tsetse in large blocks of habitat can afford to feed much more selectively than when they are in a restricted habitat carrying the same types and abundance of hosts ( Table 3 ) . The comparison between real riverine and savannah areas will depend crucially on the numbers and sizes of hosts present in each situation , and on the intrinsic mobility of the tsetse present . However , the principles are established that a reduction in the innate mobility of tsetse , and the limits that restricted habitats impose on host location , can greatly favor a strategy of feeding on any host encountered .
Our results suggest the possibility of reducing the wide variety of host-orientated behavior to a unifying framework applicable to both sexes and all species of tsetse in all habitats , including the many forest-group species not modelled here . The development of such a framework requires further theoretical and experimental attention . Nevertheless , host location must depend largely on displacement rates which affect: ( i ) effectiveness of odor attraction , ( ii ) reliance on small , abundant and solitary hosts , ( iii ) performance of small targets relative to large , ( iv ) repellence of humans , ( v ) importance of stationary as against mobile baits , and , ( vi ) persistence near hosts and the strength of alighting responses . The magnitude of each of these phenomena is expected to be governed by ( i ) the width and length of the overall habitat , ( ii ) proportion of habitat that allows free flight , ( iii ) fly size , since innate displacement potential increases with size and ( iv ) proportion of the fly's energy available for flight [47] . Host-finding is likely to be influenced also by parameters other than those governing displacement . For example , changes in vegetation affect the length and structure of odor plumes [50] , [51] . Nonetheless , the above four parameters , among which habitat geometry seems very important , could go far towards rationalizing much of the apparent variety of tsetse behavior . Empirical support for a unifying framework is provided by results from three sources . First , some of the most comprehensive data for savannah tsetse come from Rekomitjie , Zimbabwe . The biggest fly present , female G . pallidipes , is twice the size of the smallest , male G . m . morsitans . In accord with expectation , the larger flies are the most mobile [10] , the most available to stationary odor baits , the most repelled by humans [2] , the least available to tiny , as against large , targets [19] , the least persistent and the least likely to alight [2] . A second source of support is provided by several studies of tsetse that occupy habitats atypical of their group . Thus G . longipennis , of the forest group , occupies savannah and in keeping with its large size and habitat , is as mobile as G . pallidipes [52] , is repelled by humans and readily available to host odor [53] . In expected contrast , G . brevipalpis , a large forest species which has remained in forest , is less available to odor [54] . The smallest tsetse , G . austeni , is a savannah-group fly found in coastal thickets . In accord with its small size and dense habitat , its availability to odor is much less than for other savannah species [54] . The riverine fly , G . tachinoides , lives in relatively open habitats and is relatively responsive to odor [55] , albeit not as much as other tsetse living in savannah – as predicted since it is smaller than such tsetse . Third , and perhaps the most telling , studies of the riverine tsetse , G . fuscipes fuscipes , near Lake Victoria in Kenya , showed that adding odor to traps was ineffective in narrow ( 5–10 m wide ) forest habitats but doubled catches in a larger block of forest covering 1 . 4 km2 [56] . Presumably , the closeness of the habitats ensured that they contained flies with the same innate responsiveness . While the outputs of the model and the predictions of the unifying framework fit well with existing field data , there is a need for new field experiments specifically aimed at confirming and extending present indications . For example , it would be particularly informative to elucidate the response of riverine species of tsetse to visual and olfactory stimuli under circumstances not expected to limit the expression of such responsiveness . One approach would be to study further the behavior of riverine tsetse in large blocks of woodland [56] . Another approach is suggested by the expectation that the catches in the first few minutes of the exposure of a bait depend primarily on the responsiveness of flies already in the ambit of the bait's stimuli , whereas the later catches are governed by the way that habitat size and shape govern the rate at which tsetse diffuse into that ambit from far away . Hence , to highlight the basic responsiveness to bait stimuli in habitats that reduce fly diffusion , it would be pertinent to accumulate the catches of a bait that appears for brief periods interspersed with longer periods in which the baits are hidden while flies move in to re-populate the vicinity [20] . The time needed to produce such re-population would itself be of interest in indicating the rates of fly movement [10] . A further approach would be to use a bait that moves to a succession of stations a short distance apart , stopping at each just long enough to recruit flies from the area covered by the odor plume . Indeed , such minor movement and stopping would come closer than any research yet done to duplicate the common behavior of natural hosts . The simulations offer support for using tiny odorless targets to control riverine tsetse in restricted habitats [18] but warn that in broader habitats such as those that can occur in mangrove ecosystems , a larger target with odor might be more cost-effective . Our results confirm that relatively high densities of targets are needed per unit area of habitat to control riverine tsetse , but these high densities are offset by the fact that such habitats cover a small proportion of the land surface . Thus , in places where people and livestock need to be protected against disease during visits to infested localities , the target density required per total land surface tends to be small , at around 7/km2 ( Torr and Lehane , unpublished ) . While aversion to humans seems to be the main reason why savannah tsetse are minor vectors of sleeping sickness today , they might become more important if climatic or anthropogenic change restricts tsetse habitat . The relationship between habitat and host-finding in tsetse is likely to apply to other blood-sucking insects . While data are less extensive for other insects , there are indications that differences are consistent with expectations . For instance , horse flies , stable flies , and blackfly living in extensive woodlands [48] are highly responsive to host odors whereas in riverine habitats near Lake Victoria these species show the same type of pattern as for tsetse in riverine [56] . Malaria mosquitoes inhabiting savannah woodland ( Anopheles arabiensis , [40] and extensive wetlands ( Anopheles melas , [57] , [58] ) are also highly responsive . On the other hand , bird-biting species of Culex [59] , and Aedes aegypti ( the vector of dengue virus ) in urban settings [60] , seem much less responsive . We suggest that the restricted and heterogeneous habitats of tree canopies and urban environments reduces mobility in much the same way that riverine habitats affect tsetse . Field studies to explore this hypothesis could provide important new insights into the transmission dynamics and control of West Nile and dengue viruses transmitted by Culex pipiens and Aedes aegypti , respectively .
|
Tsetse flies and other blood-sucking insects spread devastating diseases of humans and livestock . We must understand the host-finding behavior of these vectors to assess their epidemiological importance and to design optimal bait methods for controlling or sampling them . Unfortunately , mysteries abound in the host-finding behavior of tsetse . For example , it is strange that visual cues are more important for species found in riverine habitats , where dense vegetation restricts the range of visual stimuli , whereas olfactory cues are more important for species occurring in open savannah . To explain this paradox , we used a deterministic model which showed that restricted riverine habitats can reduce tsetse movement by up to ∼70% . This , and the fact that movement increases with fly size , can explain why savannah tsetse , especially the larger ones , rely relatively greatly on olfactory cues , are particularly available to large stationary baits , are repelled by humans , and often investigate baits only briefly without alighting on them . The results also explain why tiny , inexpensive , and odorless baits can control riverine tsetse effectively , whereas larger odor-baited devices are needed against savannah tsetse . These findings have important bearings on the study of host-finding behavior in other blood-sucking insects , including mosquitoes .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"epidemiology"
] |
2014
|
Explaining the Host-Finding Behavior of Blood-Sucking Insects: Computerized Simulation of the Effects of Habitat Geometry on Tsetse Fly Movement
|
Gene regulatory circuits must contend with intrinsic noise that arises due to finite numbers of proteins . While some circuits act to reduce this noise , others appear to exploit it . A striking example is the competence circuit in Bacillus subtilis , which exhibits much larger noise in the duration of its competence events than a synthetically constructed analog that performs the same function . Here , using stochastic modeling and fluorescence microscopy , we show that this larger noise allows cells to exit terminal phenotypic states , which expands the range of stress levels to which cells are responsive and leads to phenotypic heterogeneity at the population level . This is an important example of how noise confers a functional benefit in a genetic decision-making circuit .
Snapshots of bacterial populations often reveal large phenotypic heterogeneity in the gene expression states of its composite individuals . Such phenotypic heterogeneity in a clonal population of bacterial cells in a single environment has significant consequences for how well the organisms can adapt and survive . On the one hand , a population with little or no heterogeneity may allow for all cells to take advantage of certain optimal conditions to which the population is exposed . In this case , heterogeneity is suboptimal and therefore detrimental to fitness . On the other hand , numerous recent studies have shown that heterogeneous populations allow for cells to account for uncertainty in future environmental conditions [1–6] . In this case , heterogeneity is beneficial to fitness . A straightforward way to maintain high phenotypic heterogeneity is for each cell to exhibit a dynamic response . This allows each cell in its turn to transition among the various states of the population , e . g . via switching , pulsing , or oscillatory dynamics . The heterogeneity is intrinsically encoded in each cell , and is often enhanced by , or even entirely due to , stochasticity , or “noise” , at the molecular level [2–4 , 6] . The ability of molecular noise to cause stochastic phenotype changes has been demonstrated in a number of biological systems . In the context of enzymes , several studies have explored how intrinsic noise due to low numbers of molecules , or even a single molecule , can have dramatic effects through the amplified actions of a few enzymes [7 , 8] . Moreover , studies of bacterial operons , including in the context of bacterial persistence , have suggested that stochasticity could be encoded in the interactions between genes in a genetic regulatory network by ensuring that certain operon states are exposed to low numbers of molecules [9–11] . Recently , a theoretical study has demonstrated the conditions for when deterministic approaches to modeling genetic circuit dynamics break down , due to amplified effects of rare events caused by a small number of regulators [12] . Together , these works suggest that phenotypic heterogeneity could be rooted in low-molecule-number noise , and that this noise could in turn be encoded in the architecture of genetic regulatory networks . The competence response of the gram-positive bacterium Bacillus subtilis provides a striking example of dynamically maintained phenotypic heterogeneity . Under stress , B . subtilis undergoes a natural and transient differentiation event , termed competence , that allows the organism to incorporate exogenous genes into its genome . Previous studies have shown that entry into the competent state is controlled by a genetic circuit that that can be tuned to one of three dynamical regimes [13]: an excitable regime at low stress levels , where cells rarely and transiently enter the competent state; an oscillatory regime at intermediate stress , where cells oscillate in and out of the competent state; and a mono-stable regime at high stress , where cells remain in the competent state . Importantly , oscillatory ( and repeatably excitable ) dynamics lead to phenotypic heterogeneity , since cells are dynamically transitioning in and out of the competent state ( see Fig 1A ) . This heterogeneity is especially important to the survival of B . subtilis: if no cells respond , competence is not exploited , and the population may succumb to the stress . On the other hand , if all cells are permanently in the competent state , this can also be fatal to the population , since competence has been shown to reduce the cell growth rate and prevent cell division due to the inhibition of FtsZ [14 , 15] . Therefore , maintaining a dynamic competence response , and therefore a heterogenous population , is thought to be crucial to survival under stress . The competence circuit includes a negative feedback loop , which is known to play a critical role in controlling the exit from competence [15] , the duration of competence [13] , the variability of the duration of competence [16] , and the integration of signals into competence that permit the existence of a transient competent state [17] . However , the effects of noise on the dynamics of the competence response are only partially understood . Previous work has shown that noise can trigger excitations into the competent state when the circuit is tuned to the excitable regime [15] . This transition into the competent state was shown to be governed by intrinsic noise rather than extrinsic noise [18] . Later work showed further that these excitations have a high variability in their duration , and that this variability is directly linked to the architecture of the competence circuit [16] . In particular , this work employed an analogous synthetic excitable circuit , termed SynEx , to provide evidence that the duration variability is due to intrinsic noise from low molecule numbers in the native circuit . Additionally , a similar strain , SynExSlow , was created that had a mean competence duration comparable to the native strain at the cost of additional complexity in order to reduce the efficiency of a proteolytic negative feedback loop [16] . However , the ability of this intrinsic noise to trigger sustained or repeatable excitations has not yet been quantified . Moreover , the generic effects of intrinsic noise on the three dynamic regimes , and how these effects translate to the physiological function of B . subtilis at the population level , are unknown . Here , using stochastic modeling and quantitative fluorescence microscopy , we study the effects of intrinsic noise on the competence dynamics and the ensuing population heterogeneity of B . subtilis . We uncover a novel effect of noise that goes beyond architecture-dependent stochastic effects in a single cell . Specifically , we find that at both low and high stress levels , noise prevents cells from becoming unresponsive or indefinitely responsive to the stress , and instead allows cells to respond dynamically . These effects expand the range of stress levels over which the population of cells maintains a heterogeneous response distribution , which is critical to the population viability ( see Fig 1B ) . The use of efficient numerical methods and stochastic simulation at several levels of model complexity allows us to elucidate the mechanisms behind these effects . A central prediction from our modeling is that these effects are rooted in noise arising from low numbers of molecules . We verify this prediction using quantitative fluorescence microscopy by comparing the population response of native B . subtilis with that of synthetic mutants harboring the less-noisy SynExSlow circuit . Taken together , these results constitute a fundamental example of how noise can increase the functionality of a phenotypic response .
To elucidate the effects of noise in each of the native and SynEx circuits ( Fig 2 ) , we develop a stochastic model of each circuit , which includes noise , and then compare each to its deterministic analog , which does not include noise . As described in Materials and Methods , we develop the stochastic models at several levels of complexity to investigate the robustness of our findings to our modeling assumptions , and we solve each model using a combination of efficient numerical solution and stochastic simulation . We first describe the behavior of the deterministic models . As shown in Fig 3A , a standard linear stability analysis of the deterministic model for each circuit reveals three dynamical regimes , depending on the value of the control parameter , the ComK induction rate αk . At low induction , each circuit is excitable , resulting in a transient differentiation event into and out of the competent ( high-ComK ) state . At intermediate induction , each circuit is oscillatory , periodically entering and exiting the competent state . At high induction , each circuit is mono-stable , staying in the competent state indefinitely; relaxation to the non-competent state does not occur . These three dynamical regimes have been confirmed in experimental studies of the native competence circuit [13] . We find that these deterministic dynamics are reflected in the steady-state solutions to the minimal stochastic models . As shown in Fig 3B , the three types of dynamics correspond to three shapes of steady-state probability distributions of ComK levels . Excitable dynamics correspond to a distribution confined to low ComK molecule numbers , oscillatory dynamics correspond to a distribution mixed between low and high molecule numbers , and mono-stable dynamics correspond to a distribution centered at high molecule numbers . As described in Materials and Methods , we calculate the fraction f of the distribution in the high-molecule-number state ( see the shaded regions in Fig 3B ) . Within our model , f represents the fraction of time a single cell spends in the competent state , or equivalently , the fraction of an isogenic population of cells found in the competent state at a given time . Importantly , f is the indicator of population heterogeneity , since unresponsive ( f = 0 ) or fully competent ( f = 1 ) populations are homogeneous , while mixed populations ( 0 < f < 1 ) are heterogeneous . We define the range of induction rate αk for which 0 < f < 1 as the viable response range , since unresponsive cells ( f = 0 ) do not benefit from competence , while long-term competence ( f = 1 ) is known to have a detrimental effect on growth rate and cell division [14 , 15] . In Fig 3C , we compare the viable response range of the stochastic model with the boundaries between dynamical regimes predicted by the deterministic model . We see that for both the native and the SynEx circuit , the stochastic range extends beyond the deterministic range for both low and high induction rate αk . At low induction rate , the range expands by more in the SynEx circuit than in the native circuit ( 16 times vs . 8 . 4 times ) . In contrast , at high induction rate , the range expands by more in the native circuit than in SynEx circuit ( 20 times vs . 3 . 5 times ) . The latter effect is much stronger , such that the total expansion of the viable range is three times larger in the native circuit than in the SynEx circuit ( 8 . 4 × 20 = 168 times vs . 16 × 3 . 5 = 56 times ) . Taken together , these observations imply that noise expands the range of stress levels to which cells can respond in a dynamic way , and that this expansion depends on circuit architecture . In the next section , we elucidate the mechanisms behind this expansion . Why does noise expand the viable response range at low induction levels ? As shown in Fig 4A , the reason is that noise leads to repeated excitations into the competent state , which prevents the system from remaining completely unresponsive . In a completely deterministic excitable system , an excitation is caused by initializing the system away from its stable fixed point , and it occurs only once . However , in a stochastic system , noise can cause repeated perturbations away from the stable state , leading to persistent additional excitations . Indeed , in both circuits , noise at the stable state is high , because the stable state corresponds to one or more species being expressed at very low molecule number . Specifically , in the native circuit , one species is at low molecule number ( ComK ) , and in the SynEx circuit , both species are at low molecule number ( ComK and MecA ) ; see Fig 4A . This difference is consistent with the expansion of the viable response range being larger in the SynEx circuit than in the native circuit at low induction levels ( Fig 3C ) . Since the dynamics in both circuits are governed by Poissonian birth-death reactions , low molecule numbers correspond to high intrinsic noise ( variance over the squared mean ) . The corresponding fluctuations are visible in S1A Fig , which shows a zoom-in of the top left panel in Fig 4A . These fluctuations then lead to excitations , driven by the the low-molecule-number species . This is seen in S1B Fig , which overlays the ComK and ComS dynamics for the native circuit in the left column of Fig 4A , and shows that maxima in ComK levels slightly precede minima in ComS levels , indicating that the low-molecule-number species , ComK , is the driver of the excitation . The net result is frequent and persistent noise-induced excitations for both circuits in the low-stress regime . This effect is consistent with the noise-induced excitations seen for these circuits in previous work [15 , 16] . Here , however , we have quantified the effect of these excitations on the stochastic distribution , which describes the heterogeneous population response . Why does noise expand the viable response range at high induction levels ? Here the mechanism is different from at low induction levels . As shown in Fig 4B , the reason is that noise prevents the damping of oscillations , which keeps the system from relaxing to the competent state . In the deterministic system , the mono-stable state is defined by a stability matrix whose eigenvalues are complex with negative real parts ( S2 Fig and S1 Text section 2 ) . This means that the solution relaxes to the mono-stable state in an oscillatory way , i . e . the oscillations are damped . The damped oscillations are clearly visible for the native circuit in S1C and S1D Fig , and for the SynEx circuit in the right panel of Fig 4B ( black lines ) . Intrinsic noise thwarts this relaxation , continually perturbing the system away from the stable point , and preserving a finite oscillation amplitude ( colored lines in Fig 4B ) . Similar effects have been observed in ecological and epidemic models , where they are attributed to the ability of white noise to repeatedly excite a system at its resonant frequency [19] . Here we see the effect at the molecular level in bacteria , and we find that it occurs sufficiently strongly that it supports and significantly extends a heterogeneous population response . Note that in both circuits the low- and high-stress regimes differ in both the number and the stability characteristics of their fixed points ( S2 Fig and S1 Text section 2 ) , further indicating that the noise-induced features arise via different mechanisms in the two regimes . At high induction levels , the expansion of the viable response range is more pronounced in the native circuit than in the SynEx circuit . This effect was demonstrated at the population level in Fig 3C , and it is sufficient to make the expansion of the entire viable response range , at both low and high induction levels , three times larger in the native circuit than in the SynEx circuit . The effect is also demonstrated by the dynamics in Fig 4B: the noise-induced prevention of damping is clearly evident for the native circuit , even at the αk value shown , which is 15 times value predicted deterministically . In fact , at an αk value this large , the deterministic dynamics are no longer damped oscillations; instead they are monostable and non-oscillatory ( S1 Text section 2 ) , and yet the stochastic pseudo-oscillations persist . The reason that the effect of noise is so pronounced in the native circuit is that the mono-stable fixed point corresponds to ComS being expressed at very low molecule numbers , where the intrinsic noise is high ( lower left panel ) . In contrast , in the SynEx circuit , the mono-stable state corresponds to both species being expressed at high molecule numbers , so the intrinsic noise is lower than in the native circuit . This difference , which stems ultimately from the difference in the architecture of the two circuits ( Fig 2 ) , was found in previous work [16] to be responsible for the increased variability in the competence durations of the native circuit compared to the SynEx circuit . Here we demonstrate that , in the context of the current model , this difference between circuits additionally leads to an increase in the expansion of the viable response range . We have tested that the effects discussed above are robust , in that they persist when we relax the three simplifying assumptions of our minimal stochastic model ( see Materials and Methods ) . We relax two of the assumptions by considering a non-adiabatic stochastic model in which the fast dynamics of mRNA production and enzymatic degradation that have been named as the possible source of large comK mRNA variations and linked to competence entry [18] are included explicitly , and by setting the mean molecule numbers in the tens of thousands as opposed to tens ( see S1 Text ) . We find that all noise-induced effects persist , namely ( i ) repeated excitations , ( ii ) the prevention of damping , and ( iii ) the enhancement of effect ii in the native circuit over the SynEx circuit ( see S6 and S7 Figs ) . As shown in S6 and S7 Figs , we also verify quantitatively that effects i and ii produce sufficiently oscillatory dynamics that the power spectrum is peaked , as opposed to the non-peaked power spectrum observed for purely excitable or mono-stable dynamics . Interestingly , when we raise the molecule number , but retain the adiabatic assumption , we find that the effects of noise diminish , and the stochastic model behaves like the deterministic model ( see S8 and S9 Figs ) . This confirms that the effects we observe are rooted in the intrinsic noise arising from low molecule numbers , as expected . Importantly , however , it demonstrates that when coupled with explicit mRNA and competitive degradation dynamics , these intrinsic effects dominate the response up to a much higher molecule number regime . Finally , we relax the third assumption by considering a three-species model for the SynExSlow circuit , in which the dynamics of ComS are accounted for explicitly ( see S10 Fig ) . Recall that SynExSlow is a variant of SynEx that adds an additional PcomG − comS in addition to the PrpsD − comS such that the duration of competence is similar to the native strain [16] . We find that effect ii persists , while effect i does not , indicating that the expansion of the viable response regime at high induction levels is more robust than at low induction levels . Since the high-induction regime is also where the contrast between the native and SynEx circuits is greater , we focus on the high-induction regime in the next section , where we compare our model predictions with experiments . To test our model predictions , we use quantitative fluorescence microscopy to measure the ComK activity levels in populations of B . subtilis cells harboring either the native or the SynExSlow circuits , as described in Materials and Methods ( see Fig 5A ) . To report the competence state , we use a PcomG − cfp reporter that was previously shown to be highly correlated with the activity of PcomK [15] . In particular , we use a previously reported strain , Hyper − αK , to measure competence in the native competence circuit , and we created an analogous SynExKSlow strain to monitor competence in the synthetic SynExSlow competence circuit . ComK expression is induced by increasing the concentration of IPTG , which corresponds to the model parameter αk . As seen in Fig 5B , in both the native and the SynEx strain , as the IPTG concentration increases , the fluorescence distribution across the population changes shape: first it is centered at low values , then it is split between low and high values , and finally it is centered at high values . This change is qualitatively reminiscent of the change seen in the stochastic model in Fig 3B . Moreover , Fig 5C also shows that the transition to a distribution centered at high values occurs at a higher IPTG concentration in cells with the native circuit than in cells with the SynExSlow circuit . This feature is also qualitatively consistent with the finding in Fig 3C that the viable response range is expanded to a greater extent in the native circuit than in the SynEx circuit at high induction levels . To investigate whether our experimental observations agree quantitatively with our theoretical predictions , as well as qualitatively , we fit the fluorescence distributions to the stochastic model , as described in Materials and Methods . Fig 5C shows that both of our central predictions in the high-induction regime are quantitatively confirmed by the data , namely ( i ) that noise extends the transition to a permanently competent state beyond the deterministically predicted induction level , and ( ii ) that it does so to a larger extent in the native circuit than in the SynEx circuit . Fig 5 therefore provides strong experimental support for the the notion that intrinsic noise expands the viable response range by delaying , as a function of induction level , the relaxation of cells to the competent state .
Phenotypic heterogeneity , in which different individuals express particular genes at different levels , is an important survival strategy in uncertain environments . Here we studied dynamically maintained phenotypic heterogeneity in the competence response of B . subtilis , and how it is influenced by intrinsic fluctuations in molecule numbers . By combining theoretical modeling , stochastic simulations , and quantitative microscopy , we showed that intrinsic noise facilitates heterogeneity by expanding the range of stress levels over which heterogeneity is maintained ( the viable response range ) . The effect manifests itself at both low and high stress levels , and the influence of noise is dramatic: in the native competence circuit , noise increases the maximal stress level at which a heterogeneous population response occurs , by 20-fold . Our work advances previous work investigating the effects of circuit architecture on dynamic response . It was previously known that the native competence circuit exhibited higher variability in its competence duration times than a synthetic analog with different architecture ( SynEx ) . This variability was attributed to intrinsic molecule number fluctuations and was thought to provide a fitness advantage , similar to variability in the times to commit to cell states [20] . Yet the advantages of the native design over the synthetic design were not immediately clear . Here , we showed that while the SynEx circuit is more predictable in terms of competence duration times , its dynamic response is limited to a much smaller range of stress levels , which limits its functionality . In both circuits , the viable response range is expanded at both low and high stress levels . The mechanisms in these two cases are different . At low stress levels , intrinsic noise causes repeated , period excitations , effectively sustaining oscillations into the excitable regime . At high stress levels , noise prevents the damping of oscillations , effectively delaying , as a function of stress level , the static and indefinite entry into the competent state . Both mechanisms rely on intrinsic fluctuations and , importantly , persist even at high molecule numbers in a non-adiabatic system . The limited response range observed in the deterministic solution is recovered only in the strict limit of fast switching and high molecule numbers , suggesting that the effects of noise that we observe here are generic . Quantitative microscopy measurements confirmed our theoretical predictions: all cells exhibited competence at high induction levels , no cells exhibited competence at low induction levels , and a bimodal population response was observed in the intermediate regime . Important differences between circuit architectures were also confirmed experimentally , namely that the SynEx circuit begins to oscillate at lower stress levels ( IPTG levels ) than the native circuit , and that the native circuit can withstand roughly 5-fold higher stress levels than the SynEx circuit before indefinitely entering the competent state . An independent fit of the model predictions to the experimental data showed very good agreement . Traditionally , noise in gene expression has often been seen as a nuisance that needs to be controlled , especially in stable environments or when the reproducibility of downstream gene expression is crucial . Thus , much work has concentrated on how to ensure the reliability of gene expression and cell signaling in the presence of intrinsic noise [21–29] . However , as has been shown experimentally and theoretically in the context of antibiotic resistance [3 , 30 , 31] , noise-induced population heterogeneity can be advantageous for adaptation to new conditions [32–35] . Functional applications of noise have also been identified in a number of settings [36] ranging from differentiation decisions to sporulate [37] , apoptose [38] , or allow DNA uptake , such as discussed in this paper . In B . subtilis , a heterogeneous competence response is thought to be optimal since permanent competence curbs cell growth . The effect of phenotypic heterogeneity on the growth rate of populations has also been studied theoretically [2 , 39–44] , showing that while in optimal conditions fluctuations decrease the overall growth rate , in less favorable environments , diversity of gene expression increases the population fitness [44] . The effect of selection on such populations was also considered [43] , and shown to influence the stability of the phenotypic states [6] . We have taken advantage of the finding that comK is the master regulator of competence in B . subtilis in that the regulation of competence primarily occurs by modifying the expression of comK[45] . However , it is not clear whether significant physiological stress would cause ComK production in the same controlled manner as we induce ComK production via IPTG . Indeed , it is likely that the regulatory input into competence via comK has been evolutionarily tuned to stay within the dynamic range of the native competence machinery . Alternatively , it may be that the competence circuit has been tuned to the accept the available range of comK regulatory input discernable from the organism’s environment . Perhaps then the topology that allows for the most dynamic response range given the available signal transduction mechanisms may be subject to natural selection in order to improve fitness . An alternative differentiation program to competence , which is evident in the experiments ( Fig 5A ) , is sporulation under stress , in which cells create a durable endospore . This differentiation program has been shown to occur independently of competence without crosstalk [4 , 5] . This is remarkably illustrated by the existence of dual-activity cells where the mother cell of a spore continuous to undergo competence until the spore forces it to lyse [5] . Since competence has been demonstrated to occur independently of sporulation , further consideration of sporulation is beyond the scope of this work and spores are excluded from the analysis ( see also S1 Text , sections 6 and 7 ) . We have described an example of phenotypic heterogeneity that is maintained by an oscillatory response , and we have demonstrated that intrinsic noise increases the range of stress levels for which oscillations occur . The ability of noise to facilitate oscillations has also been observed in the entrainment of NF-κB in fibroblast cells to oscillating TNF inputs [46] . There , small-molecule-number noise was shown to facilitate both oscillation and entrainment , and phenotypic variability was shown to enlarge the dynamic range of inputs for which entrainment is possible . These results , along with our findings herein , suggest that strategies that exploit the coupling between noise and phenotypic heterogeneity allow for functional population responses over a large variety of conditions .
Our minimal stochastic models of the native and SynEx circuits are based on our previous modeling work [16] , but employ the ( stochastic ) master equation instead of a ( deterministic ) dynamical system in order to capture the effects of intrinsic noise . The master equation describes the dynamics of the probability distribution over the numbers of the relevant molecular species inside the cell [47] . For both circuits the master equation reads d p n m d t = g n - 1 p n - 1 , m + r n + 1 , m ( n + 1 ) p n + 1 , m - ( g n + r n m n ) p n m + q n p n , m - 1 + s n , m + 1 ( m + 1 ) p n , m + 1 - ( q n + s n m m ) p n m . ( 1 ) where pnm is the joint probability distribution over molecule numbers n and m ( see Fig 2 for a diagram and explanation of all variables and parameters ) . In the native circuit , n is the number of ComK proteins and m is the number of ComS proteins . In the SynEx circuit , n is the number of ComK proteins and m is the number of MecA proteins . The dynamics are birth-death processes with mutual regulation: the production rates g and q increase the numbers n and m , respectively , while the degradation rates r and s decrease the numbers n and m , respectively , and the regulation is encoded in the functional dependence of the rates on n and m . The regulation functions follow from our previous work [16] and for the native circuit read g n= α k + β k n h k k h + n h , q n= α s + β s 1 + ( n / k s ) p , ( 2 ) r n m= δ k 1 + n / Γ k + m / Γ s + λ k , s n m= δ s 1 + n / Γ k + m / Γ s + λ s , ( 3 ) while for the SynEx circuit they read g n= α k + β k n h k k h + n h , q n= α m + β m n p k m p + n p , ( 4 ) r n m= r m = δ m + λ k , s n m= s = λ m . ( 5 ) The meaning of the parameters is explained in Fig 2 . The regulatory functions introduce positive and negative feedbacks ( see Fig 2 , top ) . The parameter values used in the model are as in [16] and are given in S1 Text . The model in Eqs 1–5 makes three simplifying assumptions , all of which we later relax . First , as in [15 , 16] we have assumed that mRNA dynamics and the enzymatic degradation process are substantially faster than all other biochemical reactions in the circuits , and are thus adiabatically eliminated ( see S1 Text for details ) . This reduces each model to the two-species form in Eq 1 , depicted by the cartoons in Fig 2 ( top ) . Second , the parameters are chosen such that typical protein copy numbers are small ( in the tens or hundreds per cell ) . Lacking information about the absolute protein numbers in the experiments , we make this assumption because we expect any effects of intrinsic noise to be most evident in the low-number regime , although as we later show , the effects we find persist out to protein numbers in the tens of thousands . Third , we model in Eqs 4 and 5 the SynEx circuit as originally constructed [16] , instead of the “SynExSlow” circuit that we use in experiments ( described later in this section ) . This reduces the model from three species to two , which is more amenable to analytic and numerical solution . Once again , however , we will see that the most important effects of noise that we elucidate are also present in a model of the SynExSlow circuit . We solve Eq 1 in steady state in one of two ways . At low copy numbers , we use the spectral method [48 , 49] , a hybrid analytic-numerical technique that exploits the eigenfunctions of the birth-death process . Derivation of the spectral solution of Eq 1 is given in S1 Text . The spectral method is much more efficient than other numerical techniques [48] , but we find here that it becomes numerically unstable at sufficiently high copy numbers . Therefore , at high copy numbers , we use iterative inversion of the matrix acting on pnm on the right-hand side of Eq 1 ( see S1 Text ) . To obtain individual stochastic trajectories of the system described by Eqs 1–5 , we use the Gillespie algorithm [50] . The deterministic analog of Eq 1 is obtained by performing an expansion in the limit of large molecule numbers [47] . To first order one obtains dn¯dt=gn¯−rn¯ m¯n¯ , ( 6 ) dm¯dt=qn¯−sn¯ m¯m¯ , ( 7 ) where n ¯ and m ¯ are ensemble averages . Eqs 6 and 7 form a coupled dynamical system whose properties we obtain by linear stability analysis . As shown in S2 Fig , both circuits exhibit excitable , oscillatory , and mono-stable regimes , depending on the value of the control parameter αk . The transition from excitable to oscillatory is marked by the annihilation of a stable and an unstable fixed point , leaving only one unstable fixed point . The transition from oscillatory to mono-stable is marked by this unstable fixed point becoming stable . These transitions provide the dashed lines in Fig 3C . For the native competence circuit , we used a variant from our previous study [13] . For the synthetic competence circuit , we reconfigured the original “SynExSlow” circuit created in [16] , in order to introduce a tunable proxy for stress level . This required replacing the tunable Phyperspank − comS with an internally controlled promoter for the ribosomal gene rpsD , as well as adding in Phyperspank − comK . The result was a strain that is resistant to four antibiotics and has comS expressed from a ribosomal promoter , providing for a basal level of expression ( see S1 Text for chromosomal alterations and antibiotic resistance ) . In both strains , ComK expression is induced by increasing the amount of IPTG in the environment . Since stress signals are usually integrated at the ComK promoter , IPTG therefore acts as a proxy for stress and triggers competence . This allows us to simulate stress directly in a controlled manner , rather than using physiological stresses that may themselves induce external variation in the responses . Cells of Bacillus subtilis were prepared by streaking from glycerol stocks onto LB agar plates containing the appropriate antibiotic for maintenance and incubated at 37°C overnight . Single colonies were then selected from the plates and grown in LB broth for three to four hours at 37°C until an OD of 1 . 6 to 1 . 8 was reached . While culturing the cells , argarose pads were made by pouring 6 mL of 0 . 8% w/v low-melting point agarose in re-suspension medium onto a glass coverslip . A second glass coverslip was then placed on top of the medium , and the medium was left to congeal while the culture was grown . Once the culture was ready , cells were spun down and resuspended in the resuspension medium twice to wash away the LB . To deposit cells , the top glass coverslip was removed , and then 2 μL of cells were dropped on 37°C low melting point agarose pads . The pads were then cut into squares with a 5mm edge , each containing a single drop of cells . After drying for one additional hour , the pads were flipped over and placed on a glass-bottom dish . The dish was then sealed with parafilm . Images of the cells were then obtained at 100X magnification on an Olympus IX81 system using the ImagePro software from MediaCybernetics along with customized macros . IPTG stock solutions were dissolved in ethanol to a concentration of 100 mM . Working ( 1000X ) stocks were diluted with Milli-Q water to 30 mM , 10 mM , 3 mM , 1 . 5 mM , 0 . 75 mM , respectively , by serial dilution and then added to the appropriate resuspension media at a ratio of 1:999 to achieve the final concentrations indicated . The resuspension media at the specified concentrations of IPTG were used in all steps above after the initial growth in LB . See S12 Fig for time-lapse images of strains containing either the native or SynExSlow competence circuits over the span of 24 hours . Template plasmids with homologous recombination arms for the Bacillus subtilis chromosomal loci were modified through restriction enzyme digest and ligation of DNA inserts ( see S1 Text for loci ) . The inserts were created by polymerase chain reactions using primers from Integrated DNA Technologies while using genomic DNA or other plasmids as templates . The PY79 strain of Bacillus subtilis was modified through homologous recombination using a One-Step Transformation protocol by inducing competence . 50 ng of plasmid DNA was replicated in TOP10 E . coli cells ( Invitrogen , Life Sciences , Inc ) and purified using a MiniPrep spin column ( Sigma-Aldrich ) . The DNA was then mixed with culture growing in minimal salts for thirty minutes and then subsequently were rescued using 2xYT rich medium . Positive colonies were then selected on LB agar plates containing selective concentrations of antibiotics . Fluorescence histograms were obtained from microscopy images using a pixel-based analysis . A mask was created on each image to identify the areas that the cells occupy ( see S3 Fig ) . A histogram of fluorescence intensity values was then generated for pixels within that area . Sterlini-Mandelstram Resuspension Medium was used during time-lapse microscopy and followed the protocol as in references [51 , 52] . The actual protocol used consists of making two salt solutions: A and B . Solution A consists of 0 . 089 g of FeCl3 ⋅ 6H2O , 0 . 830 g of MgCl2 ⋅ 6H2O and 1 . 979 g MnCl2 ⋅ 4H2O in 100 mL of filtered water . Solution A is filter sterilized ( not autoclaved ) and stored at 4°C . Solution B consists of 53 . 5 g NH4Cl , 10 . 6 g Na2SO4 , 6 . 8 g KH2PO4 , and 9 . 7 g NH4NO3 . Solution B is then also filter sterilized and stored at 4°C . Sporulation salts are made by combining adding 1 mL of Solution A and 10 mL of Solution B to filtered water for a total 1 L . This solution is then autoclaved . The final Resuspension media is created by combining 93 mL of sporulation salts , 2 mL of 10% v/v L-glutamate , 1 mL of 0 . 1M CaCl2 , and 4 mL of 1M MgSO4 on the day of the experiment . One-step transformation media consists of 6 . 25 g of K2HPO4 ⋅ 3H2O , 1 . 5 g of KH2PO4 , 0 . 25 g of trisodium citrate , 50 mg of MgSO4 ⋅ 7H2O , 0 . 5 g of Na2SO4 at pH 7 . 0 , 125 μL of 100 mM FeCl3 , 5 μL of 100 mM MnSO4 , 1 g of glucose , and 0 . 5 g of glutamate added into filtered water for a total of 250 mL . The media is filter sterilized using 0 . 2 micron Millipore filters . 2xYT recovery medium consists of 16 . 0 g of Tryptone , 10 . 0 g of Yeast Extract , 5 . 0 g of NaCl added to filtered water to a total volume of 1L . The media is then filter sterilized using 0 . 2 micron Millipore filters . For the ComK distributions in the model , pn = ∑m pnm , we determine the fraction f of cells in the responsive , high ComK protein concentration state using two independent methods . First , we use a generalized method of separating the distribution’s two modes: since the distribution is often not completely bimodal ( see Fig 3B , middle column ) , we find the average n* = ( n1 + n2 ) / 2 of the two inflection points surrounding the putative local minimum between the two modes , and define f = ∑ n = n * ∞ p n . In the case of a bimodal pn , this method indeed well approximates the location of the actual local minimum . Second , we fit pn to a mixture of two Poisson distributions , using the Kullback-Leibler divergence as the cost function . There are three fitting parameters , the two Poisson parameters and the relative weighting between them , and the weighting provides f . We see in S4 Fig that the two methods give similar results for the dependence of f on the control parameter αk , demonstrating that our determination of f is robust to the method used . The two-Poisson method is smoother but is numerically unstable at low αk . Furthermore , it does not capture the transition to f = 1 at high αk , since a roughly equal mixture of two Poisson distributions with similar means ( f ∼ 0 . 5 ) will always provide a better fit than a single Poisson distribution ( f = 1 ) . Therefore , in Fig 3C , we use the two-Poisson method at intermediate αk , while we use the inflection-point method for low αk and to determine the transition to f = 1 at high αk . At low/intermediate αk we concatenate the curves from the two methods and use smoothing near the concatenation point . Since f = 0 is approached asymptotically , we quantify the expansion of the viable range at low stress by determining the minimum αk value for which f > ϵ = 10−2 . The expansion factors at low αk are robust to the values of ϵ and the smoothing parameters . To compare the model predictions to the experimental data , we analyze the fluorescence distributions ( Fig 5B ) in the same way as the model distributions . Specifically , we calculate the fraction of the comK population in the responsive state f for each experimental distribution using the two-Poisson method above . Because the mapping between IPTG concentration and the model parameter describing the level of external stress αk is unknown , we infer the most likely value of αk corresponding to each experimental distribution by fitting the theory to the data . First we use the mode of the [IPTG] = 0 distributions to subtract the background fluorescence from the remaining data . To avoid binning , we then fit the cumulative distribution instead of the probability distribution ( sample fits are shown in S5 Fig ) . We use a maximally constrained least-squares fit , where all parameters are fixed as in S1 Text except αk and the unknown parameter X describing the conversion of pixel intensity to molecule number . Given a value of X , we find the values of αk for each distribution that minimize the sum of the squared error S in each case . X is then chosen by minimizing the sum of minimum S values over all distributions . These f and αk values inferred from the data are plotted in Fig 5C . The error bars on αk are obtained by finding the αk values where S reaches 1 . 25 of its minimum value ( sample plots of S vs . αk are shown in S5 Fig ) .
|
Fluctuations , or “noise” , in the response of a system is usually thought to be harmful . However , it is becoming increasingly clear that in single-celled organisms , noise can sometimes help cells survive . This is because noise can enhance the diversity of responses within a cell population . In this study , we identify a novel benefit of noise in the competence response of a population of Bacillus subtilis bacteria , where competence is the ability of bacteria to take in DNA from their environment when under stress . We use computational modeling and experiments to show that noise increases the range of stress levels for which these bacteria exhibit a highly dynamic response , meaning that they are neither unresponsive , nor permanently in the competent state . Since a dynamic response is thought to be optimal for survival , this study suggests that noise is exploited to increase the fitness of the bacterial population .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"fluorescence",
"imaging",
"medicine",
"and",
"health",
"sciences",
"cellular",
"stress",
"responses",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"pathogens",
"signal",
"processing",
"cell",
"processes",
"bacillus",
"microbiology",
"light",
"microscopy",
"noise",
"reduction",
"prokaryotic",
"models",
"model",
"organisms",
"systems",
"science",
"mathematics",
"microscopy",
"bioassays",
"and",
"physiological",
"analysis",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"imaging",
"techniques",
"medical",
"microbiology",
"fluorescence",
"microscopy",
"dynamic",
"response",
"microbial",
"pathogens",
"equipment",
"sterilization",
"filter",
"sterilization",
"equipment",
"preparation",
"cell",
"biology",
"bacillus",
"subtilis",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"organisms",
"fluorescence",
"competition"
] |
2016
|
Noise Expands the Response Range of the Bacillus subtilis Competence Circuit
|
Emerging pathogens undermine initiatives to control the global health impact of infectious diseases . Zoonotic malaria is no exception . Plasmodium knowlesi , a malaria parasite of Southeast Asian macaques , has entered the human population . P . knowlesi , like Plasmodium falciparum , can reach high parasitaemia in human infections , and the World Health Organization guidelines for severe malaria list hyperparasitaemia among the measures of severe malaria in both infections . Not all patients with P . knowlesi infections develop hyperparasitaemia , and it is important to determine why . Between isolate variability in erythrocyte invasion , efficiency seems key . Here we investigate the idea that particular alleles of two P . knowlesi erythrocyte invasion genes , P . knowlesi normocyte binding protein Pknbpxa and Pknbpxb , influence parasitaemia and human disease progression . Pknbpxa and Pknbpxb reference DNA sequences were generated from five geographically and temporally distinct P . knowlesi patient isolates . Polymorphic regions of each gene ( approximately 800 bp ) were identified by haplotyping 147 patient isolates at each locus . Parasitaemia in the study cohort was associated with markers of disease severity including liver and renal dysfunction , haemoglobin , platelets and lactate , ( r = ≥0 . 34 , p = <0 . 0001 for all ) . Seventy-five and 51 Pknbpxa and Pknbpxb haplotypes were resolved in 138 ( 94% ) and 134 ( 92% ) patient isolates respectively . The haplotypes formed twelve Pknbpxa and two Pknbpxb allelic groups . Patients infected with parasites with particular Pknbpxa and Pknbpxb alleles within the groups had significantly higher parasitaemia and other markers of disease severity . Our study strongly suggests that P . knowlesi invasion gene variants contribute to parasite virulence . We focused on two invasion genes , and we anticipate that additional virulent loci will be identified in pathogen genome-wide studies . The multiple sustained entries of this diverse pathogen into the human population must give cause for concern to malaria elimination strategists in the Southeast Asian region .
Plasmodium knowlesi malaria is widespread in Southeast Asia ( SEA ) . Descriptions of the aetiology of knowlesi malaria support a zoonotic origin of infection [1] and highlight variability in disease severity between those at risk across the region [2] . For example , very young children living in a forested area of Southern Vietnam have asymptomatic mixed Plasmodium species infections that include P . knowlesi [3] . Adults and children in Malaysian Borneo experience symptomatic single species P . knowlesi infections that are severe in >10% of patients and can be fatal [4] , [5] . P . knowlesi transmission is restricted to the Leucosphyrus group of mosquito vectors found in forested areas of Southeast Asia [6] , [7] . The vector group is diverse and capable of simultaneous transmission of human and non-human primate adapted Plasmodium species [8] . The majority of reported cases of P . knowlesi malaria are associated with time spent in the jungle or jungle fringe areas where the ranges of the natural vertebrate hosts , the long and pig tailed macaques ( Macaca fascicularis and Macaca nemestrina ) and leucosphyrus vectors overlap [9] , [10] . However , a change in pattern has recently emerged in Malaysian Borneo , where children living in a deforested area are infected [11] . This new pattern may signal a change in vector or vector habitat preference and a move towards human-to-human transmission . Restricted spread of P . knowlesi within human populations is attributed to non-urban vector habitat . Also human-host adapted Plasmodium species , where prevalent , may present a biological barrier to the entry of P . knowlesi into human populations concurrently at risk from human adapted and zoonotic species infections . On a backdrop of vector , human and parasite diversity , it would be folly for malaria elimination strategists to underestimate the importance of the multiple geographically dispersed entries of P . knowlesi into the human population [10] . The scene is set for a host switch of P . knowlesi from macaques to humans if pressed and as predicted by Garnham in 1966 [12] . Parasitaemia in malaria is a measure of the number of parasitized erythrocytes in the infected host at the time of sampling . The asexual replication cycle of P . knowlesi is 24 hours and therefore parasitaemia can increase daily in uncontrolled infections . Rising parasitaemia in P . knowlesi infections is associated with disease severity frequently involving renal failure , liver dysfunction and respiratory distress but not coma or severe anaemia [5] , [13] . However , not all patients develop high parasitaemia , even following several days of untreated infection [5] . Parasite and/or host factors contributing to the rapid development of hyperparasitaemia in some patients infected with P . knowlesi have not been investigated . Successful erythrocyte invasion by the infective merozoite stage of Plasmodium species is the result of a complex recognition , reorientation and entry process orchestrated by merozoite protein families conserved within the genus [14] , [15] , [16] . Of these , the reticulocyte binding-like protein ( RBP ) family is present in all Plasmodium species studied and members are involved in erythrocyte selection and invasion ( Table S1 ) . P . falciparum has five functional paralogous RBP members , PfRh1 , PfRh2a , PfRh2b , PfRh4 and PfRh5 [17] . This family of proteins was first discovered in P . vivax [18] , [19] . P . vivax has two well described functional members and more putative members have been identified recently [20] . The P . falciparum Rh proteins are thought to provide multiple invasion pathways allowing for invasion of a wide range of erythrocyte phenotypes , lessening restriction and explaining hyperparasitaemia [16] , [21] , [22] . There is also evidence for differential expression of the PfRh genes in human infections but with no clear association with parasitaemia and invasion efficiency [22] , [23] , [24] . P . knowlesi has two members of the RBP gene family , named P . knowlesi normocyte binding proteins ( Pknbp ) xa and Pknbpxb [25] . Pknbpxa is located on chromosome 14 and Pknbpxb on chromosome 7[26] . A recent study on an experimental line of P . knowlesi demonstrated binding of Pknbpxa but not Pknbpxb protein products to human erythrocytes implicating Pknbpxa in human erythrocyte invasion [27] . Here we report on a prospective study of patients with P . knowlesi malaria . We confirm and then exploit the association between P . knowlesi parasitaemia with clinical and laboratory measures of disease progression . We then address the question that parasitaemia in naturally acquired human infections is associated with particular alleles of the P . knowlesi merozoite invasion genes Pknbpxa and Pknbpxb .
This non-interventional study was approved by Medical Research and Ethics Committee , Ministry of Health Malaysia and the Ethics Committee Faculty of Medicine and Health Sciences , University Malaysia Sarawak . The study was approved to recruit patients 15 years and above with informed signed consent . Children ( <15 years ) were not recruited into the study . Two recruitment sites were selected for the study . The first , Hospital Sarikei , serves four districts in the Sarikei Health Division , population size 133 , 572 ( 2012 ) and Hospital Sibu , a referral hospital serving the Rejang basin . Patients with microscopy positive all-cause malaria were recruited with consent by the attending healthcare professionals . Each patient was given a unique study identifier code . Retrospective exclusion was based on PCR results . ( For a detailed description of patient recruitment see Text S1 ) Serum , plasma and whole blood samples were stored frozen on site and transported at sub-zero temperatures to the Malaria Research Centre , University Malaysia Sarawak , ( UNIMAS ) at regular intervals during the study . DNA amplification , cloning and sequencing were conducted in UNIMAS and serum and plasma samples were shipped on dry ice to St George's University of London for glucose , lactate and IL-10 assays ( See Text S1 ) . DNA was extracted from dried bloodspot samples using the InstaGene method [28] to confirm the infecting parasite species . Nested PCR of the small subunit rRNA gene as described previously was used as follows: The first nest used primer pairs rPlu 5 and rPlu6 [29] and the second nests were specific for P . falciparum , P vivax [Paul C Divis , unpublished] , P . malariae and P . knowlesi [1] , [30] . A study dataset containing patient demographic information , history , clinical and laboratory information was prepared from the study history sheet and patient case notes using FileMaker Pro 10v . 1 ( FileMaker Inc . ) . Alleles occurring at the two genetic loci were added to the dataset during the course of the study . See below . There were 232 patients with PCR confirmed single species P . knowlesi infections who fulfilled the study criteria ( Figure S1a ) . Of these , 165 ( 71% ) had mild disease with no abnormal clinical or laboratory indicators [5] , [31] . In order to avoid generating redundant information and incur unnecessary costs these patients were sorted by parasitaemia and alternate patients were de-selected without biasing the range of parasitaemia in this group . Following de-selection , 147 patients were included in the genetic association study . This group included patients with mild malaria as above , all patients in the study with severe disease and all of those with some abnormal findings but with otherwise uncomplicated malaria using the WHO criteria for complicated malaria in the non-immune adult[31] . Log transformed parasitaemia was normally distributed however some clinical and laboratory variables remained non-normally distributed . Spearman's ( non-parametric ) and Pearson's ( parametric ) correlation ( r ) were calculated Prism GraphPad v 4 and Stata 8 for Macintosh . Pure DNA template was extracted from frozen EDTA whole blood samples using QIAamp DNA Mini kit ( QIAGEN ) following the manufacturer's instructions . Pknbpxa and Pknbpxb DNA sequence was generated from five reference P . knowlesi patient isolates using high stringency methodology as follows . Primer sequences were designed from published Pknbpxa and Pknbpxb sequences EU867791and EU867792 respectively [25] . In the first instance large segments of each gene ( 8501 bp Pknbpxa and 3506 bp Pknbpxb ) were amplified and cloned as single fragments . The 8501 bp Pknbpxa fragment was amplified using the primer pair PknbpxaF5 5'AGGTGCAAGCTGGGAACAAG and PknbpxaR2 5'CTACACGACACACAATGCACC with LongRange PCR Enzyme Mix ( QIAGEN ) . Each reaction was in a final volume 25 µL containing 2 µL DNA template , 0 . 4 µM each primer , 500 µM each dNTP and 1 U Long Range enzyme in 1x Long Range buffer ( 2 . 5 mM MgCl2 ) . The reaction conditions were 93°C for 3 mins , 9 cycles at 93°C for 15 sec , 57°C for 30 sec , 68°C for 14 mins followed by 27 cycles at 93°C for 15 sec , 57°C for 30 sec and 68°C for 14 mins with addition of 20 sec/cycle . The Pknbpxb 3506 bp fragment was amplified using the primer pair Xb273F 5'GCATGGTCAAAAGAACCCC and Xb3430R 5'CTTCTATGGACGCTTCAGGT with Elongase Enzyme Mix ( Invitrogen , Life Technologies ) . Each reaction was in a final volume of 20 µL containing 3 µL DNA template , 0 . 25 µM each primer , 500 µM each dNTP , 1 U Elongase in 1 x Elongase buffer ( 1 . 5 mM MgCl2 ) under the following conditions: 93°C 30 sec , 34 cycles at 93°C for 30 sec , 55°C for 30 sec , 68°C for 3 min followed by a final extension at 68°C for 10 mins . The fragments were cleaned , gel purified and cloned into the pCR TOPO XL vector , TOPO XL PCR Cloning Kit ( Invitrogen , Life Technologies ) following the manufacturer's instructions . Resulting plasmids were recovered using S . N . A . P . MiniPrep Kit ( Invitrogen , Life Technologies ) . Each clone was sequenced in the forward and reverse directions beginning with M13F and M13R sequences flanking the insert followed by walk-in sequencing . Each sequencing reaction was performed in10 µL final volume with 2 µL BigDye Terminator v3 . 1 Cycle Sequencing ( Applied Biosystems , Life Technologies ) , approximately 500 ng of plasmid with insert , 5 pmol primer with 35 cycles at 96°C for 20 sec , 50°C for 15 sec and 60°C for 4 minutes . Sequence reactions were ethanol/sodium acetate precipitated as per manufacturers instructions and outsourced to 1st BASE Pte Ltd ( Malaysia ) for sequencing . DNA sequences were aligned by CLUSTALW using MegAlign ( Lasergene v7 . 0 , DNASTAR ) . Two TOPO XL clones were generated from independent PCR reactions for each gene per reference isolate . Within clone DNA sequence conflicts were resolved before aligning the sequence of each clone . Between clone conflicts per isolate were resolved using a third clone . The five reference sequences for Pknbpxa ( 8501 bp ) and for Pknbpxb ( 3506 bp ) were aligned using CLUSTALW , MegAlign Lasergene v 7 . 0 ( DNASTAR ) and exported to DnaSP v5 . 10 to calculate Nucleotide diversity ( π ) [32] , [33] . Polymorphic fragments approximately 1000 bp suitable for direct PCR sequencing were identified in each gene . The primer pair PknbpxaF5 5'-AGGTGCAAGCTGGGAACAAG-3' and 7428R1 5'- GCCAAGTCCAAACTTTTCCC-3' was designed to amplify a polymorphic Pknbpxa 1184 bp fragment under the following conditions: 3 . 0 µL DNA template , 0 . 4 U Phusion High-fidelity DNA polymerase ( Thermo Scientific ) , 0 . 25 µM each primer , 500 uM each dNTP , 1 x Phusion buffer ( 1 . 5 mM MgCl2 ) in 20 µL final volume . The cycling conditions were 98°C for 30 sec and then 38 cycles at 98°C for 7 sec , 64 . 8°C for 20 sec and 72°C for 36 sec , followed by a final extension at 72°C for 10 minutes . Samples that failed to amplify were repeated with a new forward primer Ex1F 5'-GGTCCAAGAAATGTGCAAATG-3' designed from the chromosomal fragment Pk_strainH_chr14 , correct at the time of the study ( PlasmoDB [26] ) . The fragments were amplified using 3 µL DNA template , 1 U Elongase ( Invitrogen life Technologies ) , 0 . 25 µM each primer , 500 µM each dNTP , 1 x Elongase buffer ( 1 . 5 mM MgCl2 ) in 20 µL final volume . Cycling conditions were: 93°C for 30 sec , 35 cycles at 93°C for 30 sec , 56°C for 30 sec and 68°C for 120 sec , followed by a final extension at 68°C for 10 minutes . The primer pair Xb272F and 3430R ( as above ) was used to amplify the Pknbpxb 3506 bp fragment in patient isolates as follows: 0 . 25 µM each primer; 4 . 0 µL DNA template; 500 µM each dNTP; 1 x Phusion buffer and 0 . 4 U Phusion DNA polymerase in 20 uL final volume . Cycling conditions were 98°C for 30 sec , then 38 cycles at 98°C 7 sec , 64 . 3°C 20 sec , 72°C 105 sec with a final step 72°C for 10 mins . Samples that were not amplified were repeated with the same primer pair and Elongase ( Invitrogen Life Technologies ) as per Pknbpxb amplification for cloning , table S2 . Fifteen µL of PCR products were cleaned with the PCR DNA fragments extraction kit , ( Geneaid , Biotech Ltd . ) as per manufacturer's instructions . Direct PCR sequencing was performed using 5 pmol primer ( PknbpxaF11 5'-TAAGCGAATCGAATAAGCAGCAG-3 for the Pknbpxa fragment and XB2318F 5'-GGTGTTCATGAAGATGTGCG-3' for the Pknbpxb fragment ) with 2 µL BigDye Terminator v3 . 1 Cycle Sequencing ( Applied Biosystems , Life Technologies ) . Between 34–36 ng of PCR template was included in 10 uL final volume reactions under the following conditions: 96°C 20 sec , 50°C 15 sec 60°C 4 min for 35 cycles . The reactions were Ethanol/Na acetate precipitated before sending to 1st BASE Pte Ltd ( Malaysia ) for sequencing as above . Only sequences with unambiguous base calls were included see table S2 which summarises PCR reactions and gives the number repeated , the number excluded and why . Sequences with two calls at particular sites , indicative of mixed genotype infections , were among those excluded . Nucleotide sequence diversity ( π ) was determined using DnaSP v5 . 10 software . A sliding window of 400 bases with 25 base steps was used when analysing the 8501 bp Pknbpxa fragment and 200 bases with step size 25 for the 3506 bp Pknbpxb fragment using DnaSP v5 . 10 software . Nucleotide diversity for the haplotyping fragments ( approximately 800 bp in length ) for both genes was determined on sliding window of 100 bases , with a step size of 10 bp . Parsimony informative sites , singleton sites , and the number of synonymous and non-synonymous substitutions were determined using DnaSP v5 . 10 . The number of haplotypes ( H ) and haplotype diversity ( Hd ) for each locus was determined using DnaSP v5 . 10 . A Minimum Spanning Haplotype Network was derived using Arlequin v3 . 5 [34] Continuous clinical and laboratory variables were tested for normality and those that failed the Kolmogorov-Smirnov test were log transformed . Outliers were identified using the interquartile rule . Values that were not within ±1 . 5 times the interquartile range were removed ( Table S3 ) . It should be noted that outliers were retained in the patient summaries ( Table 1 ) and other analyses . To identify the haplotypes groups , in Pknbpxa and Pknbpxb genes , a systematic method was applied to count the number of variations in each position and aggregate those with same number of occurrences . Only positions with mean allelic frequency >12 % were considered . The assigned groups covered most of the possible allelic combinations ( Table S4 ) . Pknpbxa and then Pknbpxb haplotyping sequences from each patient isolate were aligned and polymorphisms with minimal allelic frequency ( MAF ) >12% were added to the patient dataset . The method above was applied to identify haplotype combination groups each with 2-3 alleles for Pknpbxa and Pknpbxb ( Table S4 ) . All data , clinical , laboratory and allelic groups were combined and imported into Prism GraphPad v 4 for graphical representation and tests for significant differences . In order to validate our results thirteen computer-generated randomized datasets were created . For this the patient clinical and laboratory data ( 23 variables ) were assigned the haplotype data in a random fashion and the randomization repeated 12 times . We then performed 't' tests on the 299 possible combinations ( 23×13 ) to detect statistically significant clustering within the random groups . By applying confidence limits of 95% , 14 . 95 combinations out of 299 would be expected to be significant by chance alone . We detected only 8 random events . Therefore it is highly unlikely that our findings could be the result of chance or statistical artifact . All variables were tested independently even when likely to be on the same causal pathway . Based on the Minimum Spanning Haplotype Network method , Arlequin v3 . 5 [34] , the haplotype groups were mapped onto the minimum spanning network by applying the analysis of molecular variance ( AMOVA ) . The networks were resolved first with the Gephi v0 . 8 . 2 Beta [35] and manually edited to include missing mutations , Arlequin v3 . 5 , to connect each haplotype identified in the study . In addition association between parasitaemia and relevant variables were adjusted for potential confounders by multivariate linear regression using a stepwise backward elimination process using Stata version 12 . 0 ( Stat Corp . , College Station , TX , USA ) . Variables associated with parasitaemia in a univariate analysis ( p<0 . 1 ) were included in the multivariate models . p = <0 . 05 was required to retain variables in the final model . Data were transformed when heteroskedasticity was detected ( Cook and Weisberg's test ) . Linkage disequilibrium ( LD ) was inferred with Haploview [36] and the data were transformed to be loaded with the X chromosome format . Both Pknbpxa and Pknbpxb haplotypes per patient isolate were analysed as a continuum to deduce LD within and between them .
Of 389 patients admitted into the study between January 2008 and February 2011 , 304 had PCR-confirmed single species Plasmodium infections: 232 ( 76% ) P . knowlesi , 24 ( 8% ) P . falciparum and 48 ( 16% ) P . vivax ( Text S1 and Figures S1a and S1b ) . Demographic information with pre-treatment , clinical and routine laboratory results for all three species infections are summarised in table 1 . P . vivax and P . falciparum data are included for comparison . Eighty-five P . knowlesi patients were deselected ( see below and materials and methods section ) creating a subset of 147 P . knowlesi patients for Pknbpxa and Pknbpxb genotyping . Clinical , demographic and laboratory data of the P . knowlesi subset [n-147] were representative of the total number of knowlesi patients [n = 232] with the exception of parasitaemia ( Table 1 ) . The subset of patients [n = 147] had a significantly higher geometric mean parasitaemia 10492 ( 2090-51684 ) parasites/uL ( IQR ) compared with 6993 ( 1742-36734 ) in the larger group , p = 0 . 03 ( Mann Whitney U test ) . Parasitaemia was not used as a criterion for de-selection but a shift in parasitaemia was expected when approximately 50% of the relatively large group of patients with mild malaria ( no abnormal clinical or laboratory results ) were randomly removed . Forty-six ( 46 ) women and 101 men remained in the subset ( n = 147 ) . Women had a higher pulse rate , lower PCV and haemoglobin than men but this is unlikely to be related to their infection ( Figure S2 ) . The following variables; conjugated bilirubin ( p = 0 . 021 , total protein ( p = 0 . 001 ) , serum albumin ( p = 0 . 031 ) and serum globulin ( p = 0 . 001 ) all associated with recruitment hospital site , probably due to different measuring systems , and were removed from further analyses . The clinical and laboratory variables ( Table 1 ) were analysed individually for association with parasitaemia ( Table 2 for the n = 147 subset and Figure S3 for the n = 232 complete P . knowlesi cohort ) . Parasitaemia in both groups was associated with plasma lactate , serum creatinine and blood urea ( r = >0 . 50; p = <0 . 0001 ) and with haemoglobin , neutrophils , platelets , total bilirubin , AST and sodium levels ( r = >0 . 33 and <0 . 50 , p = <0 . 0001 ) . Local Pknbpxa and Pknbpxb diversity was determined by sequencing , to high stringency , large fragments of both genes from five P . knowlesi isolates collected in different locations and times ( Table S5 ) . P . knowlesi Pknbpxa ( 8501 bp ) comprising all but the first 18 and last 689 bases of exon II was amplified , cloned and sequenced ( Figure 1a ) and Pknbpxb ( 3506 bp ) comprising exon I , the intron and 3102 bp of exon II ( Figure 2a ) . Pknbpxa reference sequences were aligned with the published sequence from the P . knowlesi experimental H line EU867791 . There were no gaps in the alignment and phylogenetic analysis indicated that the Pknbpxa gene was dimorphic ( Figure S4 ) . The 8501 bp Pknbpxa gene fragment was polymorphic ( nucleotide diversity , π = 0 . 0142±0 . 0029 , n = 5 ) and comprised 168 non-synonymous and 63 synonymous polymorphic sites ( Figures 1a-c and Table 3 ) . There was a cluster of non-synonymous SNP's from base 389 to 1388 ( Figure 1c ) and this region was selected for direct PCR sequencing and haplotyping 147 patient isolates at the Pknbpxa locus . The Pknbpxa reference sequences were submitted to GenBank , Accession Numbers KF186568-KF186572 , ( Table S5 ) . Pknbpxb reference sequences , comprising exon I to midway through exon II , were generated ( Figure 2a ) . Due to DNA polymerase slippage while sequencing through the "AA" homo polymeric region towards the 5' end of exon I and also due to "AT" tandem repeats within the 243 bp intron the sequences were trimmed to 3100 bp , removing exon I and the intron . Alignment of the five 3100 bp reference sequences with the published Pknbpxb sequence from P . knowlesi H ( EU867792 ) showed that the reference sequences had three single nucleotide deletions at positions G2477 , A2478 and C2488 relative to EU867792 . The deletions did not introduce stop codons , however translation into amino acid sequence identified an additional amino acid position in the published Pknbpxb protein sequence ( ACJ54536 ) . This resulted in a four amino acid motif ( GKIY ) unique to the published sequence that differed from a conserved three amino acid motif ( ENL ) in patient isolates ( Figure S5 ) . The Pknbpxb reference sequences were submitted to GenBank , Accession Numbers KF186573-KF186577 , ( Table S5 ) . There were 20 non-synonymous and 8 synonymous sites between the five Pknbpxb reference sequences with nucleotide diversity π = 0 . 00381±0 . 0007 ( Figure 2 a - c and Table 3 ) . The highest concentration of non-synonymous SNPs was between bases 2275 and 3156 ( Figure 2c ) and this region was chosen for haplotyping patient isolates . Of 147 patient isolates 138 Pknbpxa ( 885 bp ) and 134 Pknbpxb ( 879 bp ) sequences were generated by direct PCR sequencing . Pknbpxa sequencing results were not obtained for seven patient isolates and two isolates had unacceptable sequencing readouts ( Table S2 ) . Three patients appeared to have mixed Pknbpxb genotype infections , seven isolates failed to amplify in each of Pknbpxb PCR reactions and three isolates had poor sequence outputs ( Table S2 ) . Gaps or deletions were not detected when the 138 Pknbpxa 885 bp sequences were aligned . There were 83 ( 9 . 4% ) polymorphic sites and 802 ( 90 . 6% ) invariant sites ( Table 3 ) . Sixty-two ( 74 . 6% ) of the polymorphic sites were parsimony informative . Phylogenetic and network analyses showed a clear dimorphism involving a 29 base haplotype with 25 non-synonymous and 4 synonymous changes ( Table S6 ) . Seventy-seven ( 56% ) isolates clustered in the KH195 dimorphic group and 61 ( 44% ) formed the KH273 dimorphic cluster ( Figure 3A ) . The Pknbpxa amino acid changes relative to the published reference sequence ACJ54535 are given in table S7a . Seventy-five ( 75 ) Pknbpxa DNA haplotypes were resolved in the 885 bp fragments from 138 patient isolates with haplotype diversity 0 . 9729 ( SD±0 . 007 ) . There were 12 ( 16% ) haplotypes in the KH273 dimorphic cluster all but four with a frequency >2 and 63 ( 84% ) haplotypes in the KH195 cluster . All but 11 of the Pknbpxa KH195 haplotypes had a frequency <1 ( Figure 3A ) . The three gaps observed in five Pknbpxb reference sequences were conserved in all patient isolates ( n = 134 ) in the study relative to the published sequence Pknbpxb EU86772 . Alignment of the 134 Pknbpxb 879 bp sequences identified 46 polymorphic sites ( Table 3 ) and 25 of the sites were parsimony informative . Fifty-one ( 51 ) Pknbpxb haplotypes were resolved with haplotype diversity = 0 . 922 ( SD±0 . 014 ) . A minimum spanning haplotype network shows some dimorphic characteristics at the Pknbpxb locus , Figure 4A . Pknbpxb amino acid changes relative to the published reference sequence ACJ54536 are given in table S7b . Twelve Pknbpxa and two Pknbpxb haplotype groups were resolved , each with two or three allelic forms . The alleles were added to the patient dataset and analysed for significant differences in clinical and laboratory markers of disease severity ( Table S4 ) . Pknbpxa alleles that segregated with markers of disease severity were within the KH195 dimorphism . For example patients infected with parasites with Pknbpxa group 6 , 913C allele iii were restricted to the KH195 dimorphism and had; higher parasitaemia ( p = 0 . 02 ) , WBC's ( p = 0 . 02 ) , blood urea ( p = 0 . 003 ) , serum creatinine ( p = 0 . 0057 ) , plasma lactate ( p = 0 . 003 ) , IL-10 ( p = 0 . 003 ) , lower platelets ( p = 0 . 001 ) , systolic blood pressure ( p = 0 . 005 ) and prolonged duration of fever ( p = 0 . 036 ) ( Figure 3B and Figure S6 a-j ) . When isolates with the 913C polymorphism were overlaid on the haplotype network a degree of haplotype clustering was observed although the allele appeared in two distinct nodes ( Figure 3B ) . Pknbpxa haplotype group 8 describes a complex polymorphic site ( 982T/G/C ) . Parasites in the KH273 dimorphism all had 982T allele i ( n = 61 ) . The KH195 dimorphism had two alleles , allele ii , 982G ( n = 51 ) and allele iii , 982C ( n = 26 ) . Alleles ii and iii had some association with markers of disease progression . Patients infected with parasites with 982G allele ii had higher plasma lactate ( p = 0 . 006 ) , higher IL-10 ( p = 0 . 03 ) and lower haemoglobin ( p = 0 . 034 ) and those with 982C allele iii had higher AST ( p = 0 . 033 ) , ( Figure 3C and Figure S7 a-f ) . When the 982C and 982G , group 8 alleles ii and iii were mapped onto the Pknbpxa haplotype network they formed clusters . However five haplotypes with group 8 allele iii appeared on the edges of the group 8 allele ii cluster ( Figure 3C ) . There was some overlap between Pknbpxa groups 6 and 8 ( Figures 3C boxed area ) . Two Pknbpxb allelic groups were resolved . Pknbpxb group 1 had two alleles , 2637A:2638C and 2637C:2638A . Patients infected with parasites with the 2637C:2638A allele had lower haemoglobin ( p = 0 . 004 ) and higher axillary temperature ( p = 0 . 001 ) ( Figure 4B and Figure S8a and S8b ) . Isolates with 2637C:2638A appeared as two clusters at the edges of the Pknbpxb haplotype network ( Figure 4B ) . Pknbpxb group 2 was more complex with three major alleles ( i , ii , iii ) involving 6 polymorphic sites ( Table S4 ) . Four patient isolates were not easily grouped and excluded from the Pknbpxb group 2 analysis . Patients infected with parasites with Pknbpxb group 2 allele iii had increased parasitaemia ( p = 0 . 016 ) , plasma lactate ( p = 0 . 004 ) , total bilirubin ( p = 0 . 047 ) and AST ( p = 0 . 002 ) , Figure 4B and Figure S9a-f . Haplotype clustering was observed when Pknbpxb group 2 alleles i , ii and iii were mapped onto the Pknbpxb haplotype network ( Figure 4C ) . Pknbpxb group 2 allelic cluster i was not associated with markers of disease progression and formed a discrete cluster in the Pknbpxb network . Group 2 allele ii also formed a discrete cluster and allele iii appeared as 2 clusters . In addition there was allelic sharing between Pknbpxb haplotype group 1 allele ii and Group 2 allele iii ( Figure 4C boxed ) . A strict 29 bp haplotype defines the Pknbpxa KH195 - KH273 dimorphism in the Pknbpxa haplotyping sequence ( 885 bp ) . The intensity of r2 indicates that several Pknbpxa alleles , including those defining the dimorphism are in strong LD r2>0 . 8 forming linked blocks in the matrix ( Figure 5 ) . The r2 values for linked sites between the two genes were weak . However , there was some between gene linkage with high D' values ( >0 . 99 ) and LOD>2 ( Figure 5 ) . For example Pknbpxa position 810 and Pknbpxa position 1105 ( Figure 5 , positions marked 1 and 2 . Also Pknbpxb positions 2403 and 3110 with the main blocks defining the Pknbpxa dimorphism ( Figure 5 positions marked 3 and 4 ) .
Patients with P . knowlesi malaria in Sarawak are infected with diverse parasites at the Pknbpxa and Pknbpxb loci . Some of the diversity clusters with increased parasitaemia and measures of disease severity . To our knowledge this is the first time that particular alleles of members of the Plasmodium RBP gene family have been linked to increased parasitaemia and disease in patients with malaria . Members of the RBP's are represented in all Plasmodium species studied so far [17] . They are diverse in nature and thought to be responsible for erythrocyte selection during merozoite invasion of host erythrocytes . Even though humans are not the natural hosts of P . knowlesi we did not find evidence of human host selection , represented by clonality at these important loci . In agreement with P . falciparum ( Pf ) Rh1 , PfRh2a , PfRh2b and P . vivax RBP-2 non-synonymous substitutions were predominant at each P . knowlesi RBP locus [37] . The 5' end of Pknbpxa , corresponding to the putative erythrocyte-binding site , was particularly polymorphic although polymorphic sites occurred along the entire 8105 bp fragment , similar to P . vivax RBP-2 [37] . Diversity involving non-synonymous substitutions near functional sites on Plasmodium merozoite surface proteins is not unusual and a particular problem in vaccine design [38] , [39] . Non-synonymous diversity is most often attributed to immune evasion rather than altered function . Although a single amino acid change in experimental lines of P . falciparum reticulocyte binding-like orthologue PfRh5 changed function and conferred infectivity to Aotus monkey erythrocytes [40] . Therefore , assigning loss of host erythrocyte restriction to a single amino acid suggests that non-synonymous substitutions on other members of this invasion gene family may alter function as well as antigenicity . Patients in our study were infected with P . knowlesi parasites with predominantly non-synonymous substitutions at the RBP invasion gene loci . This observation will be taken forward to examine altered protein function between Pknbpxa and Pknbpxb protein variants and the impact of variability on parasite virulence and host invasion restriction . We did not observe parasite clonality in P . knowlesi infections that would suggest restricted entry of P . knowlesi genotypes into the human host at this or other loci [1] . However , approximately half ( 44% ) of the patients in our study were infected with Pknbpxa KH273 parasites , comprising only 12 haplotypes , and we cannot rule out some selection in the absence of Pknbpxa and Pknbpxb sequence data from the natural macaque hosts . Highly relevant to our study , children in Ghana infected with P . falciparum isolates with the CAMP dimorphism of EBA-175 , a member of another important Plasmodium invasion gene family , were at higher risk of a fatal outcome [41] . In our study Pknbpxa alleles that clustered with increased markers of disease progression were within the Pknbpxa KH195 dimorphism . Polymorphisms within the CAMP dimorphism were not interrogated in the Ghanaian study and this approach may have identified putative virulent haplotypes . However , taken together with our results , characterising within species differences in parasite virulence may provide insight into the potential health impact of malaria , especially during outbreaks . The relationship between P . knowlesi parasitaemia and markers of disease severity is consistent with other published studies [4] , [5] , [42] , [43] . High parasitaemia was not associated with prolonged duration of symptoms in this or other studies suggesting parasite or host specific reasons for the apparent increase in rate of parasitaemia in some patients [5] , [42] , [44] . Here we show that parasitaemia was significantly higher in patients infected with parasites with Pknbpxa group 6 , 913C allele iii or Pknbpxb group 2 allele iii . Patients infected with the Pknbpxa allele had some but not all of the markers associated with parasitaemia in this study . For example , they had significantly higher markers of renal dysfunction but not liver dysfunction or significant changes in haemoglobin or PCV . What at first consideration may have suggested a phenotypic effect of increased invasion efficiency and rapid progression to high parasitaemia may well be the opposite . Patients infected with this variant had higher parasitaemia and longer duration of symptoms . It is possible that a prolonged progression to high parasitaemia may impact pathology . It is worth noting that these patients also had increased lactate and lower systolic blood pressure . In addition to higher parasitaemia , patients infected with Pknbpxb group 2 allele iii had increased total Bilirubin , AST and plasma lactate . Taken within the important caveat that parasitaemia is central to our study and in multivariate linear regression models parasitaemia is independently associated with PCV , AST ( transformed data ) , urea and plasma lactate ( transformed data ) , there was some stratification between the disease markers that clustered with the particular Pknbpxa and Pknbpxb alleles . Patients infected with parasites with Pknbpxb group 1 allele ii , 2637C:2638A had lower haemoglobin and higher axilliary temperature but not higher parasitaemia . The Pknbpxa alleles but not Pknbpxb were associated with markers of renal dysfunction . To our knowledge this is the first report of an association between specific invasion gene alleles and markers of disease progression , including parasitaemia , in malaria even though hyperparasitaemia is one of the WHO criteria for severe malaria in P . falciparum and P . knowlesi infections . Purified P . knowlesi H Pknbpxb protein did not bind to human erythrocytes under experimental conditions calling into question the role of this protein in human infections [27] . However , here , patients infected with particular P . knowlesi Pknbpxb alleles had significantly different haemoglobin levels , axillary temperature , parasitaemia , PCV , lactate , bilirubin and AST . These altered disease phenotypes suggest that at least some Pknbpxb variants in nature are important in human infections . Pknbpxb was less diverse than Pknbpxa and there was some evidence of linkage between the two genes even though they appear on different chromosomes suggesting a degree of co-operation [26] . Co-operation between members of invasion gene families was reported in P . falciparum infections [45] . Patients infected with different allelic forms of P . knowlesi Pknbpxa and Pknbpxb had differences in markers of disease progression . Parasites with putatively virulent alleles formed two clusters on the Pknbpxa minimum spanning tree network with some overlap and were confined to the KH195 dimorphism . Putatively virulent Pknbpxb alleles were confined to two closely related clusters also with some overlap , a third Pknbpxb cluster was not associated with markers of disease progression . Genetic diversity , haplotype clustering and associated differences in markers of disease severity may explain the wide range of P . knowlesi disease phenotypes observed in communities across South East Asia . As expected polymorphisms on small fragments representing <10% of two P . knowlesi genetic loci did not capture all patients with complications in our study . Therefore further work on full-length Pknbpxa and Pknbpxb gene sequences may improve the resolution of our study . Also it is expected that virulence would not be restricted to two genetic loci . Historically , experimental lines of P . knowlesi have informed Plasmodium biology , including erythrocyte invasion [46] . More recently clinical studies on P . knowlesi malaria have added context to the study of severe malaria . Current advances in pathogen genome sequencing and application , to even small quantities of parasite DNA such as that available from our patient isolates , will allow us to extend our work on parasite virulence to multiple candidate loci genome wide . This , taken together with the recent publication of P . knowlesi culture adaptation to human erythrocytes and an efficient transfection system will provide the means to assign disease phenotype to genotype using rational and systematic experimental design not only in vitro but in vivo [47] . Information emerging from clinical and laboratory studies on P . knowlesi is new and pertinent to our understanding of malaria pathophysiology , including malaria caused by other Plasmodium species infections ( Table 1 ) [5] . P . knowlesi is a zoonotic pathogen that is permissive in a variety of differentially susceptible non-human primates [48] , [49] , [50] , [51] , [52] . Surely there is a compelling argument to use P . knowlesi as a robust representative animal model for malaria pathophysiology . The lack of such a model so far has impeded proof of principle testing towards the rational development of new tools to treat and manage severely ill patients with malaria .
|
Plasmodium knowlesi , a parasite of Southeast Asian macaques , has entered the human population . Approximately 10% of P . knowlesi infections are severe , 1–2% are fatal , in Sarawak , Malaysian Borneo . Increase in parasitaemia is associated with disease severity , but little is known about parasite virulence in this newly described human pathogen . Here we present the results of a study on P . knowlesi parasites collected from 147 patients . We use the isolates to produce DNA sequences from a polymorphic ( genetically variable ) region of two P . knowlesi genes , Pknbpxa and Pknbpxb , that are involved in parasite entry into host red blood cells . We addressed the question that some parasite genotypes may have an invasion advantage leading to severe disease in human infections . We analysed the DNA sequences with matched clinical and laboratory data from the patient cohort ( n = 147 ) . We found that specific DNA sequences ( Pknbpxa and Pknbpxb alleles ) clustered with high parasitaemia and markers of disease severity . Here , for the first time , we provide evidence that variant alleles of the Plasmodium Reticulocyte Binding-Like Protein invasion gene family can influence disease progression in patients with malaria . The biological characteristics of the variants will be studied to aid our understanding of malaria pathophysiology and to inform intervention strategies .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"veterinary",
"diseases",
"zoonoses",
"biology",
"and",
"life",
"sciences",
"parasitic",
"diseases",
"veterinary",
"science"
] |
2014
|
Disease Progression in Plasmodium knowlesi Malaria Is Linked to Variation in Invasion Gene Family Members
|
Inflammation is a physiological response to tissue trauma or infection , but leukocytes , which are the effector cells of the inflammatory process , have powerful tissue remodelling capabilities . Thus , to ensure their precise localisation , passage of leukocytes from the blood into inflamed tissue is tightly regulated . Recruitment of blood borne neutrophils to the tissue stroma occurs during early inflammation . In this process , peptide agonists of the chemokine family are assumed to provide a chemotactic stimulus capable of supporting the migration of neutrophils across vascular endothelial cells , through the basement membrane of the vessel wall , and out into the tissue stroma . Here , we show that , although an initial chemokine stimulus is essential for the recruitment of flowing neutrophils by endothelial cells stimulated with the inflammatory cytokine tumour necrosis factor-α , transit of the endothelial monolayer is regulated by an additional and downstream stimulus . This signal is supplied by the metabolism of the omega-6-polyunsaturated fatty acid ( n-6-PUFA ) , arachidonic acid , into the eicosanoid prostaglandin-D2 ( PGD2 ) by cyclooxygenase ( COX ) enzymes . This new step in the neutrophil recruitment process was revealed when the dietary n-3-PUFA , eicosapentaenoic acid ( EPA ) , was utilised as an alternative substrate for COX enzymes , leading to the generation of PGD3 . This alternative series eicosanoid inhibited the migration of neutrophils across endothelial cells by antagonising the PGD2 receptor . Here , we describe a new step in the neutrophil recruitment process that relies upon a lipid-mediated signal to regulate the migration of neutrophils across endothelial cells . PGD2 signalling is subordinate to the chemokine-mediated activation of neutrophils , but without the sequential delivery of this signal , neutrophils fail to penetrate the endothelial cell monolayer . Importantly , the ability of the dietary n-3-PUFA , EPA , to inhibit this process not only revealed an unsuspected level of regulation in the migration of inflammatory leukocytes , it also contributes to our understanding of the interactions of this bioactive lipid with the inflammatory system . Moreover , it indicates the potential for novel therapeutics that target the inflammatory system with greater affinity and/or specificity than supplementing the diet with n-3-PUFAs .
In vertebrates , tissue trauma or infection causes the rapid initiation of an inflammatory reaction . The early phase of this phylactic response results in the localised recruitment of cells of the innate immune system from the blood , a tissue infiltrate that is dominated by neutrophils . The molecular processes that support the initial interactions between blood-borne neutrophils and the endothelial cells lining postcapillary venules ( the site in the blood vasculature where leukocytes are recruited during inflammation ) are well described ( see Figure 1 for a schematic representation of the steps in the neutrophil recruitment process ) . In response to the localised production of inflammatory mediators , such as the cytokine tumour necrosis factor-α ( TNF ) , activated endothelial cells decorate themselves with specialised adhesion receptors of the selectin family [1]–[3] . Due to their ability to rapidly form strong but short-lived bonds with carbohydrate counter-ligands on the neutrophil surface , E- and P-selectin are capable of tethering neutrophils from rapidly flowing blood [4] . The sequential formation and dissolution of selectin bonds also support a characteristic and dynamic form of adhesion , referred to as rolling [5] . Rolling adhesion does not require neutrophil activation . However , neutrophil migration through the vessel wall and into the inflamed tissue is dependent upon the receipt of an activating stimulus [5] , [6] . We have previously shown that endothelial cells can present peptide agonists of the CXC-chemokine family to rolling neutrophils and thus stabilise adhesion [7] , [8] . Ligation of the neutrophil CXC-Receptor-2 ( CXCR2 ) by these agents is essential for activation of the β2-intergrin adhesion receptors and the reorganisation of the actin cytoskeleton that support transendothelial cell migration [8] . Here , we show that in the presence of an antibody that blocks chemokine interactions with CXCR2 , neutrophils roll on the endothelial cell surface indefinitely ( Figure 2 ) , demonstrating that a chemokine signal is essential for neutrophil activation on TNF-stimulated endothelial cells . However , the removal of this primary activating stimulus provides no information on the requirement for additional , downstream signals , which might coordinate transit of the vessel wall and onward migration into stromal tissues .
Dietary omega-3 polyunsaturated fatty acids ( n-3-PUFAs ) have anti-inflammatory properties . For example , their inclusion in the diet in the form of n-3-PUFA–rich fish oil reduces the symptoms of disease as well as the use of nonsteroidal anti-inflammatory drugs in arthritis patients with severe inflammatory joint disease [9] . In addition , dietary n-3-PUFAs may offer protection against vascular pathology associated with atherosclerosis , having been reported to be efficacious in epidemiological studies [10] . By modulating inflammation within the artery wall , n-3-PUFAS also alter the cellular and the structural composition of advanced atherosclerotic plaques in a manner that could reduce the incidence of plaque rupture or ulceration , a process that precedes tissue infarction ( e . g . , heart attack or stroke ) [11] . Despite these documented anti-inflammatory benefits , the mode ( s ) of interaction of these lipids with the immune and inflammatory systems are not well understood . When we supplemented endothelial cell culture medium with the major n-3 -PUFA found in dietary fish oil supplements , eicosapentaenoic acid ( EPA; 20:5n-3 ) , it was incorporated into cellular phospholipids so that upon withdrawal of the free fatty acid from the culture medium , a pool of esterified EPA remained localised within endothelial cell membranes ( Figure 3A and 3B ) . After withdrawal of free fatty acid and stimulation with TNF , EPA-treated cells were able to support similar levels of neutrophil adhesion to those that had received no lipid supplement ( Figure 4A ) . However , detailed analysis showed that the behaviour of neutrophils on the two populations of endothelial cells was very different . In response to TNF , but in the absence of EPA , a small population of adherent neutrophils ( ∼20% ) rolled throughout the duration of a flow adhesion assay ( Figure 4B ) . The remaining cells became activated on the endothelial cell surface , and this population of neutrophils became progressively smaller as cells migrated across the endothelial cell monolayer ( Figure 4B ) . In contrast , when endothelial cells had been treated with EPA prior to TNF stimulation , there was a marked reduction in the number of cells undergoing transendothelial cell migration ( Figure 4B ) . This was reduced at the earliest time point and did not increase over the duration of the experiment . Interestingly however , many of the cells that did become activated on the surface of endothelial cells reverted to a rolling form of adhesion ( Figure 4B ) , implying that after the receipt of an initial chemokine stimulus , a second signal was required to allow prolonged adhesion and migration across the endothelial cell monolayer . Experiments using different concentrations of EPA showed that these inhibitory effects were evident at concentrations as low as 50 nM ( Figure 4C ) . To define the molecular mechanisms underlying these observations , we tested two prevalent hypotheses . The first , predicated on the observation that n-3-PUFAs regulate the transcription of endothelial cell inflammatory genes by down-regulating the activity of the nuclear factor-κB , predicts changes in the levels of adhesion receptor and chemokine expression after supplementation with EPA [12]–[14] . However , previous studies have investigated the regulation of gene expression in the presence of up to 100 µM of n-3-PUFAs , whereas physiological blood plasma levels of free fatty acid are of the order of 1 µM [15] , a level that can be increased several fold upon supplementation . Using 5 µM EPA as an approximation of the highest concentration achievable in vivo , we did not see changes in the expression of endothelial cell adhesion molecules when assessed for RNA transcripts or protein expression ( Figure 5 and Table 1 ) . Neither was there any alteration in the levels of cytokines and chemokines secreted by endothelial cells ( Table 2 ) . Thus , in this model , we were able to discount the gene regulation hypothesis . The second hypothesis proposes that upon endothelial cell activation , EPA may compete with the n-6-PUFA arachidonic acid ( AA; 20:4n-6 ) for cyclooxygenase enzymes ( COX1 and COX2 ) after both fatty acids are liberated from membrane phospholipids by endogenous phospholipases [16] . Ordinarily , AA is metabolised by COX into 2-series endoperoxides , with downstream synthases converting these into the biologically active 2-series prostanoids [17] . However , when utilised as a COX substrate , the metabolism of EPA generates alternative 3-series prostanoids [16] . The 2-series prostanoids generated by COX activity are known to regulate aspects of the inflammatory response . For example , in the COX2 knockout mouse , neutrophil recruitment is dramatically reduced upon ischaemia and reperfusion-induced injury of the liver when compared to wild-type control animals [18] . In a murine model of lipopolysaccharide-induced lung inflammation , inhibitors of COX function ( indomethacin and aspirin ) modestly increased neutrophil recruitment [19] . Conversely in vitro , inhibition of COX function by aspirin can inhibit neutrophil migration [20] while having no effect on the levels of neutrophil adhesion to the endothelial cell monolayer [21] . Moreover , in murine models of acute and chronic inflammation , a reduction in PGE2 production by genetic deletion of its membrane-bound synthase , moderates the formation of inflammation-associated granulation tissue and angiogenesis , as well as decreasing the nociception of pain , indicating that PGE2 is proinflammatory in these models [22] . PGD2 also appears to have proinflammatory functions , as overexpression of the synthase generating this prostanoid increases production of inflammatory cytokines and chemokines , leading to exaggerated levels of eosinophil and lymphocyte recruitment [23] , a mechanism that operates through the PGD2 receptor DP-2 . In contrast , in acute peritoneal inflammation , knockout of PGD2 synthase increased inflammatory cytokine production and retarded the rate of inflammatory resolution by a mechanism that operated through the DP-1 receptor [24] . Thus , PGD2 appears to have pro- or anti-inflammatory capabilities depending on the nature of the inflammatory insult . The inflammatory activity of the equivalent 3-series prostanoids is not known . Here , by introducing a panel of COX inhibitors into endothelial cell cultures at the same time as they were activated with TNF , the effects of EPA supplementation could be replicated , with neutrophil transmigration being dramatically inhibited ( Figure 6 ) . Moreover , adding a molecular excess of AA to EPA-supplemented endothelial cells at the point of TNF activation could reverse the blockade of migration ( Figure 6 ) . Taken together , these data imply that a COX-derived product of AA is required for the transmigration of neutrophils across TNF-stimulated endothelial cells , and that in the presence of EPA , this pathway is efficiently antagonised . Endothelial cells generate COX products constitutively . For example , prostacyclin ( PGI2 ) and prostaglandin D2 ( PGD2 ) are endothelial cell-derived vasoactive prostanoids that are also involved in the regulation of haemostasis , being antagonists of platelet activation [17] , [25] . These prostanoids are difficult to measure in the systemic blood , as they have reported half-lives in plasma that are measured in minutes [26] , [27] . However , the constitutive nature of their production is demonstrated by the presence in urine and body fluids of their downstream metabolic products , PGF1α and delta-PGJ2 , respectively [28] , [29] . Prostanoids have a documented ability to regulate the migration of a number of leukocyte subsets . For example , PGD2 induces chemotaxis of eosinophils and T-lymphocytes [30] in vitro . Interestingly , the migration of monocyte-derived migratory dendritic cells ( DCs ) may be tightly regulated by interplay between different prostanoids . Thus , the ability of these cells to traffic out of tissue into lymph nodes via the lymphatic circulation is dependent upon the presence of PGE2 as a differentiation signal [31]–[34] . In this context PGE2 promotes the function ( but not the expression ) of the chemokine receptor CCR7 , so that DCs efficiently respond to the chemokines CCL19 and CCL21 . Importantly , a number of studies have shown that the presence of PGD2 can in turn inhibit the ability of DCs to migrate out of the lungs or the skin during an inflammatory response , although the molecular mechanism by which this inhibition is achieved remains undescribed [35] , [36] . As prostanoids are well documented to regulate leukocyte migration [37] , we tested the hypothesis that PGD2 was the endothelial cell-derived agent providing the stimulus for neutrophil migration across the monolayer . We established a population of surface adherent neutrophils on TNF-treated endothelial cells supplemented with EPA and perfused synthetic PGD2 across the endothelial cells and neutrophils to see whether this would reintroduce transmigration . The provision of exogenous PGD2 but not PGD3 ( which could be derived from EPA ) , fully restored the ability of neutrophils to cross the endothelial cell monolayer ( Figure 7A ) . Prostaglandin D2 has two receptors . Chemoattractant-receptor homologous molecule expressed on Th2 cells receptor ( CRTH2 or DP-2 ) is not expressed in neutrophils [35] , whereas the DP-1 receptor has been reported in these cells [38] . Here , neutrophils perfused across TNF-stimulated endothelial cells in the presence of a DP-1 receptor antagonist ( BW868C ) showed a greatly diminished efficiency of endothelial cell transmigration ( Figure 7B ) . Conversely , the transmigration of neutrophils was re-established on EPA-supplemented endothelial cells when a synthetic DP-1 receptor agonist ( BW245C ) was perfused across cells adherent to the surface of the monolayer ( Figure 7C ) . Importantly , we could demonstrate that PGD2 was operating directly on the neutrophils , because when these cells were harvested after migrating across a TNF-stimulated endothelial cells they had up-regulated CD11b ( the alpha subunit of the β2 integrin CD11b/CD18 that is required for efficient neutrophil transmigration ) and proteolytically shed l-selectin ( Figure 8 ) . However , neutrophils harvested from the surface of EPA-treated endothelial cells did not shed l-selectin or up-regulate CD11b . Thus , in this assay system , the delivery of a PGD2-mediated signal to the neutrophils was necessary for full cellular activation and efficient transendothelial migration . Taken together , these data show that PGD2 operates through the neutrophil DP-1 receptor to provide a signal that is essential for transendothelial cell migration . Moreover , the ability of neutrophils to respond to this signal is abolished when endothelial cells are preloaded with EPA from which PGD3 is generated . This implies that PGD3 may be an effective antagonist of the DP-1 receptor . To test this hypothesis , we perfused neutrophils across TNF-stimulated endothelial cells in the presence of exogenous PGD3 . Neutrophil transmigration was inhibited in a dose-dependent manner by PGD3 , showing that in the presence of endogenously generated PGD2 it could effectively antagonise this process ( Figure 7D ) . Importantly , we were able to show that cells supplemented with EPA-generated an increased amount of PGD3 ( Figure 7E ) . The absolute levels of PGD2 and PGD3 that are generated in our assay system , and thus the ratio of their abundance , are not easily assessed . The levels of PGD2 measured as an endothelial cell releasate were at the limits of detection ( Table 3 ) , presumably due to the short half-life of this prostanoid under physiological conditions; however , significant levels of PGD3 were assayed upon EPA supplementation , and concentrations in the order of 5–10 nM had accumulated over the 24 h of the EPA supplementation period . The half-life of PGD3 in serum or albumin-containing medium has not been reported to our knowledge; thus it is not clear whether the measurements made here report the true levels of PGD3 production , or if metabolic degradation of this prostanoid renders these measurements underestimates . Irrespective of this , our experiments utilising synthetic PGD2 demonstrate that concentrations on the order of 1 nM were sufficient to fully recapitulate the process of migration on EPA-supplemented endothelial cells , and when using PGD3 as an antagonist of TNF-induced transmigration marked effects were evident at 10–100 pM . Thus , the measurable levels of PGD3 that we report would certainly be sufficient to effectively antagonise the transmigration of neutrophils in our experimental model . Our study demonstrates a hitherto unknown regulatory step in the recruitment of neutrophils by cytokine-stimulated endothelial cells ( refer to Figure 1 to see how this new step fits with the known regulatory mechanisms of neutrophil recruitment ) . After initial tethering by selectin molecules , a chemokine signal induces arrest of the cell on the endothelial cell surface . However , the chemokine signal is not sufficient to support transmigration across the endothelial cell monolayer , and the arachidonic acid-derived prostanoid , PGD2 , is an essential downstream regulator of this process . PGD2 operates through the DP-1 receptor and this signal can be effectively antagonised by PGD3 which is generated from EPA released by the action of phospholipase enzymes on the phospholipids of EPA supplemented cells . Not only does this study reveal a new step in the recruitment of neutrophil recruitment during inflammation , it also reveals a novel anti-inflammatory mechanism of action of the dietary n-3-PUFA , EPA .
Endothelial cells ( EC ) were isolated and cultured , as previously described [39] , with or without 0–5 µM EPA ( Sigma ) for 24 h . TNF-α ( 100 U/ml; R&D systems ) was added to the EC for the final 4 h of culture . In some experiments , EC were treated with 100 U/ml TNF-α in the presence of 10 µM indomethacin or 0 . 1 µM SC-560 or 1 µM NS-398 ( Sigma ) . In the AA reconstitution experiments , 5 µM AA ( Sigma ) was added to the culture medium simultaneously with TNF-α . Blood from healthy adult volunteers was collected into tubes coated with EDTA ( 1 . 6 mg/ml ) , and neutrophils were separated using 2-step density gradients of Histopaque 1119 and 1077 ( Sigma ) , as previously described [40] . After washing in 0 . 15% BSA in PBS , cells were counted using a Coulter Multisizer ( Coulter Electronics ) , and neutrophils , which were >95% pure , were resuspended at a concentration of 1×106/ml in PBS/Alb with calcium and magnesium . mRNA was isolated from EC using the Qiagen RNEasy Mini Kit 50 ( Qiagen ) following the manufacturer's instructions . Real-time PCR ( RT-PCR ) was performed using QuantiTect probe RT-PCR kit according to the manufacturer's instructions ( Qiagen ) . Primers were purchased from Applied Biosystems . The expression of each target gene was normalised to β-actin expression , and the data presented represent fold change compared to untreated EC . Supernatants were collected from EC that had been incubated with 0–5 µM EPA for 24 h and had 100 U/ml TNF added to the culture for the final 4 h of culture . The Luminex kit was purchased from Upstate/Chemicon , and the experiment performed to the manufacturer's instructions . Data were collected and analysed from the samples using a Luminex100 machine ( Luminex ) . Glass capillaries ( microslides ) containing treated EC monolayers were incorporated into a flow-based adhesion assay as described [39] . Briefly , microslides were attached to cell and fluid reservoirs by silicon tubing at one end and to a withdrawal syringe pump at the other end . After mounting on the stage of a phase contrast microscope , EC were washed for 2 min with PBS containing 0 . 15% BSA . Neutrophils were then perfused ( 1×106/ml ) at 0 . 1 Pa for 4 min , followed by 15 min of wash . Video images were recorded throughout the experiment and neutrophil behaviour analysed offline using Image Pro software ( Image-Pro Plus ) . In some experiments , neutrophils were perfused across EPA- and TNF-treated EC , allowed to adhere , and subsequently perfused with 1 nM PGD2 or 1 nM PGD3 ( both from Cayman Chemicals ) . Alternatively , neutrophils were treated with BW868c ( Cayman Chemicals ) , a DP1 receptor antagonist , for 10 min prior to perfusion over TNF-α-stimulated EC . Neutrophils were also treated with the DP1 receptor agonist BW245c ( Cayman Chemicals ) prior to perfusion over EPA- and TNF-α-treated EC . Neutrophils were also treated with 0–1 nM PGD3 , immediately prior to perfusion across TNF-stimulated EC . In all experiments , lipid reagents , agonists , and antagonists were stored in 100% ethanol under nitrogen gas . Once diluted , the medium contained <0 . 01% ethanol . All controls contained equivalent concentrations of ethanol . First-passage confluent EC were treated with 5 µM of EPA for 24 h . EC were removed from the plastic culture dish by scraping and stored in 0 . 88% KCl solution at −20°C until analysis . Total lipid was extracted with chloroform∶methanol ( 2∶1 , v/v ) containing butylated hydroxytoluene ( 50 mg/l ) as antioxidant . Fatty acids were subsequently hydrolysed from the lipid and simultaneously methylated by incubation with methylation reagent ( methanol containing 2% v/v H2SO4 ) at 50°C for 2 h . Fatty acid methyl esters were separated and identified using a Hewlett Packard 6890 gas chromatograph ( Hewlett Packard ) fitted with a 30 mm×32 mm BPX 70 capillary column , film thickness 0 . 25 µm . Helium , at the initial flow rate of 1 . 0 ml/min , was used as the carrier gas . Injector and detector temperatures were 275°C , and the column oven temperature was maintained at 170°C for 12 min after sample injection . The oven temperature was programmed to increase from 170 to 210°C at 5°C/min . Fatty acid methyl esters were identified by comparison with authentic standards . Peak areas were quantified using ChemStation software ( Hewlett Packard ) . Each fatty acid was expressed as weight percent of total fatty acids present . Eicosanoids were extracted from control EC using C18 Sep-Pak cartridges ( Waters ) . Briefly , cartridges were conditioned with 5 ml of high-performance liquid chromatography ( HPLC ) -grade MeOH and rinsed twice with water . Reactions were terminated with addition of 100% ice-cold MeOH , containing 2 ng of internal standard PGE2-d4 . Sample volume was adjusted to 10% MeOH with water and applied to the column . The column was rinsed with water and the sample eluted with 2 ml of MeOH . Nitrogen gas was used to dry the sample , which was resuspended in 100 µl of MeOH and stored at −80°C until analysis . Prostaglandins were separated on a C18 ODS2 , 5 µm , 150×4 . 6-mm column ( Waters ) using a gradient of 50%–90% B over 20 min ( A , water∶acetonitrile∶acetic acid , 75∶25∶0 . 1; B , methanol∶acetonitrile∶acetic acid , 60∶40∶0 . 1 ) at 1 ml/min . Products were quantitated by directing the HPLC output directly into the electrospray source of a Q-Trap mass spectrometer ( Applied Biosystems 4000 Q-Trap ) operating in the negative mode ( −4 , 500 V ) . Individual prostaglandins were monitored in the Multiple Reaction Monitoring ( MRM ) mode using specific parent to daughter transitions of m/z 349–269 for PGD3 with collision energies of −26 V . Products were identified and quantified with reference to the appropriate standards run in parallel under the same conditions , with 2 ng of PGE2-d4 ( m/z 355–275 ) added as an internal standard . Neutrophils for analysis were isolated from whole blood as described above . Control cells were stained for flow cytometry directly after isolation . Cells rolling on EPA-treated endothelial cells in microslides were harvested by elution with 0 . 02% EDTA after 4 min of neutrophil perfusion and 11 min of wash buffer perfusion ( totalling 15 min of contact with endothelial cells ) . In order to harvest neutrophils that had migrated across endothelial cells , endothelium was cultured on 1 . 5 mg/ml type I collagen gels ( Becton Dickinson ) . After endothelial cell stimulation with TNF for 4 h , neutrophils were added for 15 min . Surface-adherent cells were washed off with 0 . 02% EDTA , and the gel was dissolved using type VII collagenase ( Sigma ) . Neutrophils were harvested from the dissolved gel by centrifugation . All neutrophil populations were stained for surface expression of CD11b ( PE-conjugated , clone 2LPM19c , 2 µg/ml; DAKO ) or CD62L ( FITC-conjugated , clone Dreg56 , 10 µg/ml , Beckton Dickinson ) . Expression was analysed by flow cytometry using a DAKO CyAn , and data analysed using Summit software ( Becton Dickinson ) . Data are expressed as mean fluorescent intensity . Data were analysed using Prism software ( GraphPad software ) . Results are presented as means±standard error of the mean ( SEM ) . Comparisons between individual treatments were by paired t-test where appropriate . ANOVA was performed to assess the effect of EPA concentration on PMN transmigration . Significant findings were investigated further using Bonferroni multiple comparison test or Dunnett's test .
|
Inflammation is a physiological response to tissue trauma or infection . Neutrophils , which circulate in the blood stream , are the first inflammatory cells to be recruited to a site of tissue inflammation . In response to recruitment signals provided by chemotactic peptides called chemokines , neutrophils traverse the endothelial cell lining of blood vessels . This process involves a multistep cascade of neutrophil adhesion and activation events on the endothelial barrier . While investigating the anti-inflammatory functions of the omega-3 fatty acid , EPA , which is found , for instance , in dietary fish oil , we identified an additional unexpected lipid-derived signal that is essential for neutrophil migration across the endothelium . Our experiments show that a chemokine delivered the first signal needed to bind neutrophils firmly to the endothelial surface . However , in order to traverse the endothelium , a subsequent signal delivered by prostaglandin-D2 ( PGD2 ) , a lipid derived from the omega-6 fatty acid arachidonic acid , was essential . When EPA , was introduced into the experiment , it was used to form PGD3 . This alternative lipid blocked interactions between PGD2 and its receptor on neutrophils , preventing the process of migration across the endothelial barrier . Thus , we reveal a new step in the recruitment of neutrophils during inflammation , and a novel anti-inflammatory mechanism of action of dietary EPA .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"physiology/immune",
"response",
"physiology/cardiovascular",
"physiology",
"and",
"circulation",
"nutrition",
"immunology/innate",
"immunity",
"cell",
"biology/cell",
"adhesion",
"immunology/leukocyte",
"activation"
] |
2009
|
Omega-3 Fatty Acids and Inflammation: Novel Interactions Reveal a New Step in Neutrophil Recruitment
|
Apicomplexan parasites are dependent on an F-actin and myosin-based motility system for their invasion into and escape from animal host cells , as well as for their general motility . In Toxoplasma gondii and Plasmodium species , the actin filaments and myosin motor required for this process are located in a narrow space between the parasite plasma membrane and the underlying inner membrane complex , a set of flattened cisternae that covers most the cytoplasmic face of the plasma membrane . Here we show that the energy required for Toxoplasma motility is derived mostly , if not entirely , from glycolysis and lactic acid production . We also demonstrate that the glycolytic enzymes of Toxoplasma tachyzoites undergo a striking relocation from the parasites' cytoplasm to their pellicles upon Toxoplasma egress from host cells . Specifically , it appears that the glycolytic enzymes are translocated to the cytoplasmic face of the inner membrane complex as well as to the space between the plasma membrane and inner membrane complex . The glycolytic enzymes remain pellicle-associated during extended incubations of parasites in the extracellular milieu and do not revert to a cytoplasmic location until well after parasites have completed invasion of new host cells . Translocation of glycolytic enzymes to and from the Toxoplasma pellicle appears to occur in response to changes in extracellular [K+] experienced during egress and invasion , a signal that requires changes of [Ca2+]c in the parasite during egress . Enzyme translocation is , however , not dependent on either F-actin or intact microtubules . Our observations indicate that Toxoplasma gondii is capable of relocating its main source of energy between its cytoplasm and pellicle in response to exit from or entry into host cells . We propose that this ability allows Toxoplasma to optimize ATP delivery to those cellular processes that are most critical for survival outside host cells and those required for growth and replication of intracellular parasites .
Toxoplasma gondii is an obligate intracellular protozoan parasite of humans and other warm-blooded animals , and is closely related to Plasmodium sp . , the causative agent of malaria . Although the majority of Toxoplasma infections in humans are asymptomatic , severe disease and death can occur in developing fetuses and in immunocompromised individuals . Infection of and spread between host cells by Toxoplasma and its close relatives Plasmodium , Cryptosporidium , and Eimeria is critically dependent on actin-myosin based motility systems in the parasite . The actin filaments required for host cell invasion and for general parasite motility are believed to be associated with the cytoplasmic tails of adhesins in the parasite plasma membrane . Specifically , the microneme proteins thrombospondin-related anonymous protein ( TRAP ) of Plasmodium sp . and MIC2 of Toxoplasma are believed to interact with F-actin through the glycolytic enzyme aldolase-1 [1] , [2] . Myosin-A , a type XIV myosin , is also critical for gliding motility [3] . This protein is found in a complex with an atypical myosin light chain [4] and two accessory proteins , GAP45 and the integral membrane glycoprotein GAP50 [5] , of which the latter is responsible for anchoring the motor complex in the parasite's inner membrane complex ( IMC ) . This organelle consists of flattened membrane cisternae that are closely apposed to the parasite plasma membrane and extends from the anterior end of the parasite to the posterior end . The only obvious connection between the main body of the parasite cytoplasm and the space between its plasma membrane and IMC is found at the extreme anterior end of the parasite . At this location , the IMC is clearly interrupted , creating a gap through which the conoid and rhoptries protrude [6] . It is also believed that it is through this gap that the parasites' micronemes access and fuse with the plasma membrane . Both actin polymerization and myosin action are ATP-dependent processes . Most eukaryote cells are capable of generating ATP through multiple mechanisms: glycolysis and the oxidative phosphorylation of acetyl-CoA derived from pyruvate , β-oxidation of fatty acid , or from certain amino acids . It is evident that apicomplexan parasites possess a complete glycolytic pathway [7] as well as all enzymes for the TCA cycle and mitochondrial electron transport chain . It was therefore very surprising that the only obvious homolog of pyruvate dehydrogenase in Plasmodium and Toxoplasma is targeted to the apicoplast , a plastid-like organelle , along with several glycolytic enzymes [7]–[9] . In other eukaryotes , pyruvate dehydrogenase is targeted to mitochondria where it is involved in the conversion of glycolysis-derived pyruvate into acetyl-CoA and thus occupies a critical link between glycolysis and oxidative phosphorylation . The apparent absence of pyruvate dehydrogenase from apicomplexan mitochondria therefore suggests that glycolysis and oxidative phosphorylation are not coupled in Toxoplasma and Plasmodium sp . It remains possible , however , that these organisms have evolved a structurally distinct pyruvate dehydrogenase . In other cells , acetyl-CoA can also be produced through the β-oxidation of fatty acids or the catabolism of amino acids . This process is also unlikely to occur in apicomplexan parasites as , at least in the case of Plasmodium sp . , the genome does not appear to encode the requisite enzymes for these processes [9] . In this study , we investigated the likely sources of the ATP that is critical for gliding motility of Toxoplasma tachyzoites . Our data indicate that glycolysis is the major source of energy for parasite motility and that oxidative phosphorylation , if it occurs at all , makes only a minor contribution . We also demonstrate that the glycolytic enzymes of Toxoplasma are distributed smoothly over the cytoplasm of intracellular parasites . In a surprising finding , we found that the glycolytic enzymes undergo a translocation from the parasite cytoplasm to the pellicle upon egress from host cells and remain pellicle-associated throughout the extracellular phase of Toxoplasma and during invasion of host cells . Only after host cell invasion is complete do the enzymes revert to a smooth cytoplasmic distribution . This translocation of the glycolytic enzymes between the parasite cytoplasm and the pellicle appears to be triggered by the changes in potassium concentrations experienced by the parasites during egress from and invasion into host cells . These findings suggest that Toxoplasma gondii tachyzoites are able to relocate their major source of ATP production to the site where energy is required at each stage during their life cycle .
Toxoplasma invasion into and egress from its animal host cells are dependent on an actin-myosin-based motility system and thus require ATP for myosin action as well as actin filament formation . In most cells , the ATP necessary for these processes can be generated by glycolysis and by oxidative phosphorylation . The Toxoplasma genome encodes all the enzymes required for ATP production by glycolysis . It is not clear , however , whether apicomplexan parasites can produce ATP by oxidative phosphorylation given that pyruvate dehydrogenase , a key enzyme in coupling glycolysis and oxidative phosphorylation , appears to be absent from the parasite's mitochondria [7]–[11] as are some subunits of the ATP synthase [12] , [13] . In order to better understand the roles of both metabolic pathways in supplying energy for parasite movement , we performed parasite motility assays under conditions where the parasites either had to rely on glycolysis for most of their energy production or on oxidative phosphorylation . Our observations are summarized in Figure 1 . When assays were performed in the presence of glucose , a carbon source for both glycolysis and oxidative phosphorylation , we observed robust movement in a majority of the parasites ( 71 . 6±7 . 4% ) . Omission of glucose resulted in a drastic decrease in the fraction of mobile parasites ( 2 . 7±2 . 9% ) . When experiments were performed in the presence of glucose but under a nitrogen atmosphere or in the presence of KCN , conditions where any oxidative phosphorylation should be inhibited , Toxoplasma motility was not significantly affected with 70 . 5±12 . 4% and 63 . 1±16 . 1% of parasites moving , respectively . Although these observations indicate already that glycolysis is the major source of energy for parasite movement , they do not exclude the possibility that oxidative phosphorylation does make some contribution . To test this possibility , Toxoplasma motility experiments were performed in the absence of glucose but in the presence of potential substrates for the parasites' TCA cycle: pyruvate , lactate , or glutamine , both individually and in combination . As can be seen in Figure 1 , only a small fraction of the parasites demonstrated motility ( 8 . 4±5 . 8% ) and , as we observed in the absence of any substrate , this was limited to trails that were barely longer than the 2 µm cut-off limit ( see Materials and Methods ) . Additional evidence against a role for oxidative phosphorylation in generating energy for Toxoplasma motility was derived from motility experiments performed in the presence or absence of atovaquone , a potent antiparasitic agent that acts as an inhibitor of the cytochrome bc1 complex in apicomplexan parasites [14]–[16] . As can be seen in Figure 1 , atovaquone does not have an obvious effect on Toxoplasma motility at a concentration of 0 . 5 µM in our typical experimental setup . Prolonged treatment of intracellular Toxoplasma with the same concentration atovaquone resulted in parasite death as had been previously observed [14]–[16] ( data not shown ) . Together , these observations suggest that glycolysis is the major source of energy for Toxoplasma motility and that oxidative phosphorylation , if it occurs at all , makes at best a very small contribution . The plasma membrane of apicomplexan parasites is found in close association with large flattened cisternae , referred to as the inner membrane complex or IMC . The IMC extends beneath the entire plasma membrane of the parasites , except at the extreme anterior end where the plasma membrane is in direct contact with the cytoplasm ( Mann and Beckers , 2001 ) . The space between the plasma membrane and IMC is only 18 . 4±0 . 6 nm ( Figure S1 ) and no trans-IMC transport mechanisms have been identified to date . It is therefore not clear whether nutrients like glucose enter parasites only at the anterior end , where there is direct contact between the plasma membrane and the cytoplasm , or if import occurs all over the parasite plasma membrane . To test which of these scenarios is used for glucose acquisition , we expressed a myc-tagged version of the Toxoplasma hexose transporter TgGT1 in the parasite . TgGT1 is a high affinity facilitative hexose transporter [17] and appears to be the only hexose transporter encoded by the Toxoplasma genome . As can be seen in Figure 2 , TgGT1 is found exclusively in the parasite plasma membrane and is distributed evenly over the parasite surface . This result suggests that glucose transport into the parasite is not confined to a particular part of its plasma membrane . The actin-myosin XIV based motile apparatus of apicomplexan parasites is found in the narrow space between the parasite plasma membrane and IMC which extends from the anterior to the posterior part of the organisms [1] , [5] . Given that parasite motility appears to be powered primarily by glycolysis and that the glucose transporter TgGT1 is found along the entire surface of Toxoplasma tachyzoites ( Figure 2 ) , we wondered whether part or all of the Toxoplasma glycolytic apparatus was also present in close proximity to the parasite pellicle and therefore its motile apparatus . Jewett and Sibley had , in fact , shown that a fraction of the glycolytic enzyme aldolase is found in this space [1] . Specifically , aldolase was reported to form a bridge between F-actin and the cytoplasmic tail of the adhesin MIC2 . Given the critical role of aldolase in glycolysis , we sought to repeat these observations . As the observations of Jewett and Sibley were based on cross-reactivity of commercial anti-rabbit aldolase antisera with Toxoplasma aldolase , we initially employed similar antisera . The Toxoplasma genome contains two potential aldolase-encoding sequences , but only one of these , aldolase-1 , is expressed in Toxoplasma tachyzoites and the second appears to represent a pseudogene ( [7] , data not shown ) . As the commercial anti-rabbit aldolase antisera do not cross-react with Toxoplasma aldolase-1 in our hands ( Figure S2A ) , we generated an antiserum that is specific to Toxoplasma aldolase-1 and that does not cross-react with mammalian aldolases ( Figure S2B ) . As an alternative strategy , we also generated a Toxoplasma line that stably expresses myc-tagged Toxoplasma aldolase-1 . As can be seen in Figure S2B , the anti-Toxoplasma aldolase-1 antiserum reacts with a single protein of the expected MWapp in immunoblots of parasite lysates . In parasites expressing myc-tagged aldolase-1 , the antiserum detects the same protein as in non-transfected parasites , as well as a slightly larger protein species that represents the myc-tagged enzyme . A monoclonal anti-myc antibody only reacts with the larger protein species , which is only present in parasites expressing myc-aldolase-1 . Based on the blot with anti-aldolase-1 , it appears that the myc-aldolase-1 is expressed at a level that is approximately three times higher than endogenous aldolase-1 . Jewett and Sibley found that the anti-rabbit aldolase antiserum reacted with material found in the anterior part of the parasite and that this signal overlapped substantially with that of the microneme protein MIC2 [1] . When we used our affinity-purified antibodies to Toxoplasma aldolase-1 to localize this enzyme in intracellular parasites , we found it to be distributed throughout the parasites' cytoplasm ( Figure 3A and Figure 4 ) , irrespective of the fixation conditions used . In extracellular parasites , on the other hand , the protein localized to the parasite periphery ( Figure 3A , C ) where it overlapped with IMC1 , a membrane skeleton protein of the Toxoplasma IMC [18] . The peripheral location of the Toxoplasma aldolase-1 in extracellular parasites is specific; other cytoplasmic proteins , such as the calcium-dependent protein kinase CDPK1 [19] ( Figure 3B ) and YFP ( Figure 4 ) , retain their normal cytoplasmic distribution in both intracellular and extracellular parasites . The observed redistribution of aldolase-1 is also unlikely to be a fixation artifact . We compared the distribution of both aldolase-1 and YFP in intra and extracellular parasites fixed with −20°C methanol or paraformaldehyde and glutaraldehyde . As can be seen in Figure 4 , aldolase-1 is found at the pellicle of extracellular Toxoplasma and in the cytoplasm of intracellular parasites , irrespective of the fixation condition used . As expected , the cytoplasmic distribution of YFP was also not affected by the fixation method used . To further assure ourselves that we were observing a relocation of aldolase-1 , we repeated the experiments in Figure 3A with parasites expressing myc-tagged aldolase-1 . As can be seen in Figure 5A and C , myc-aldolase-1 displays a similar change in distribution between intracellular and extracellular parasites . It is interesting to note that we did not observe an overlap between the aldolase-1 or myc-aldolase-1 signal with that of the microneme protein MIC2 ( Figure S3 ) which stands in contrast to the previous observations [1] . Given the striking difference in aldolase-1 distribution we observed between intracellular and extracellular Toxoplasma tachyzoites , we sought to determine if the redistribution of this enzyme occurred at a specific stage in parasite development or a particular point in parasite-host cell interaction . As Toxoplasma aldolase-1 reverts to a smooth cytoplasmic distribution at some point after host cell invasion we first tested whether this occurred during or shortly after host cell invasion , or if it occurred at a later stage . We therefore allowed freshly isolated extracellular parasites to interact with new host cells for 2 minutes and monitored the aldolase-1 distribution immediately after this incubation and after various lengths of incubation at 37°C . Extracellular and intracellular parasites could be distinguished by the inability of the latter to be recognized by an antibody to the surface antigen SAG1 in intact host cells . After short incubations , numerous parasites were captured in the process of invading host cells as judged by the fact that only a part of their surface reacted with the SAG1 antiserum ( Figure 5B ) . In these parasites , the aldolase-1 remains located at the parasite periphery . The aldolase-1 distribution was also not affected in parasites that had just completed host cell invasion ( Figure 6A ) . The peripheral location of the aldolase-1 was maintained in a large fraction ( 74±5% ) of parasites at 30 minutes after invasion but decreased to less than 10% after 4 hours ( Figure 6B ) and was no longer detectable after parasites had undergone one round of replication ( Figure 5C; Figure S4 ) . The cytoplasmic distribution of aldolase-1 was maintained as long as the parasites remained inside host cells , irrespective of the number of parasites per vacuole or the stage of parasite replication ( Figure 5C; Figure S4 ) . The pellicle of Toxoplasma consists of three membranes , the plasma membrane and the membranes of the IMC . During parasite replication , two immature daughter parasites are formed in the cytoplasm of the mother cell . These daughters possess all structural elements of the mature parasite except for the plasma membrane , which they only acquire from the mother parasite at the final stage of replication . Typical preparations of extracellular Toxoplasma always contain a few parasites that are released from host cells at various stages of daughter cell formation . When the distribution of aldolase-1 was analyzed in such parasites , it was evident that the enzyme is only associated with the pellicle of mature parasites and not with the IMC of the immature daughters ( Figure 5D ) . This finding suggests that IMC association of aldolase-1 is not only controlled by the parasite's environment but is also under strict developmental control by limiting pellicle association to mature parasites only . We considered the possibility that the change in location of aldolase-1 between intracellular and extracellular Toxoplasma reflected the proteolytic destruction of preexisting enzyme pool and the synthesis of a novel pool of protein that was targeted to a new location , possibly through differences in post-translational modifications . Pretreatment of parasites with 10 µM cycloheximide , a concentration sufficient to block >98% of de novo protein synthesis in Toxoplasma [19] , did not affect translocation of aldolase-1 to the pellicle upon egress or its translocation to the cytoplasm after host cell invasion ( data not shown ) , indicating that we are indeed detecting the translocation of preexisting enzyme . It appears that translocation of aldolase-1 from the cytoplasm to the pellicle only occurs at or shortly after Toxoplasma egress from its host cells . Egress is not synchronous or predictable under physiological conditions but is readily induced by the physical disruption of the host cells [20] . Although earlier time points could not be obtained for practical reasons , aldolase-1 translocation to the Toxoplasma pellicle could be detected as early as 30 seconds after parasite egress and was essentially complete by 60 minutes ( Figure 7A ) . This finding implies that the parasite can sense whether it is inside or outside of a host cell and can translocate aldolase and presumably the rest of its glycolytic apparatus accordingly . We have previously described that parasite egress from its decaying host cells is triggered by the reduction in the [K+] of the surrounding host cytoplasm , which in turn results in an increase in cytoplasmic [Ca2+] [20] . As the relocation of Toxoplasma aldolase-1 also occurs upon host cell egress , we wondered whether it might also be controlled by changes in environmental [K+] . We therefore broke Toxoplasma-infected HFF cells in media containing [K+] consistent with the environment inside or outside animal host cells and monitored changes in aldolase-1 distribution . As can be seen in Figure 7A , disruption of parasite-infected HFF cells in a medium mimicking the extracellular environment ( EC medium ) results in translocation of aldolase-1 to the parasite pellicle with half-maximal translocation observed at about 15 minutes . When parasite-infected host cells are disrupted in medium with a high [K+] ( IC medium ) this translocation does not occur and aldolase-1 remains distributed evenly over the parasite cytoplasm ( Figure 7B , stage 1 ) . This effect is entirely reversible in that a subsequent exposure to extracellular medium results in rapid translocation of aldolase-1 to the parasite pellicle ( Figure 7B , stage 2 ) . These findings indicate that translocation of aldolase-1 from the Toxoplasma cytoplasm to its pellicle is controlled by the [K+] in the surrounding medium and requires increases in [Ca2+] . The transfer of aldolase-1 from the parasite periphery to the cytoplasm that we observed after host invasion also appears to be triggered by a change in extraparasitic [K+] . When extracellular Toxoplasma are transferred to a medium with [K+] similar to the intracellular milieu , aldolase-1 is transferred from the parasite pellicle to its cytoplasm ( Figure 7C ) . As was observed during normal infection of host cells , this occurred with substantially slower kinetics than the forward reaction . Reversal had occurred in only ∼60% of parasites after 1 hour in high [K+] medium and was essentially complete after 4 hours . The translocation of aldolase-1 from the Toxoplasma pellicle to its cytoplasm in response to high [K+] medium was also entirely reversed upon transfer of parasites to EC medium ( data not shown ) . Activation of Toxoplasma motility in response to a reduction of environmental [K+] requires an increase in [Ca2+]c in the parasite [20] . To determine if an increase in [Ca2+]c is also required to trigger translocation of glycolytic enzymes in response to [K+] changes , we incubated parasites for 30 minutes in high [K+] medium with BAPTA/AM , a chelator of cytoplasmic Ca2+ , followed by a 30 minute incubation in extracellular medium containing the same compound . Chelation of cytoplasmic Ca2+ did not affect the cytoplasmic distribution of Toxoplasma aldolase-1 while in high [K+] medium ( Figure 7C ) . When the BAPTA/AM-treated parasites were subsequently transferred to EC medium , the aldolase-1 remained evenly distributed throughout the parasite cytoplasm whereas aldolase-1 in mock-treated parasites had translocated to the pellicle ( Figure 7C ) . When parasites that had been previously incubated in EC medium and uniformly displayed pellicle-associated aldolase-1 were subsequently treated with BAPTA/AM , we did not observe any change in pellicle-association of aldolase-1 compared to untreated parasites ( Figure 7C ) . When these BAPTA/AM-treated parasites were subsequently transferred to IC medium , aldolase-1 reverted to a cytoplasmic distribution that was indistinguishable from untreated parasites ( Figure 7C ) . Aldolase-1 translocation from the parasite cytoplasm to its pellicle does not require either F-actin or microtubules . When intracellular parasites were pre-treated with cytochalasin-D and subsequently released from their host cells , aldolase-1 relocation to the parasite pellicle was not affected ( Figure 7D ) . Disruption of Toxoplasma microtubules with oryzalin had , as described before [21] , a profound effect on parasite morphology but also did not affect aldolase-1 translocation to the pellicle after egress from host cells ( Figure 7E ) . As cytochalasin-D and oryzalin-treated parasites are incapable of host cell invasion [22] , [23] , we could not determine whether F-actin or microtubules played a role in the pellicle-to-cytoplasm translocation after this process . Pellicle association of aldolase-1 in intact extracellular Toxoplasma is very stable , even during incubations of up to 24 hours at 37°C in complete medium . The distribution of the enzyme in the parasite pellicle was also not noticeably affected during this incubation ( data not shown ) . Jewett and Sibley had reported that Toxoplasma aldolase appeared to undergo an anterior-to-posterior translocation in moving parasites [1] . We did not observe such a translocation irrespective of whether we monitored the endogenous enzyme with anti-Toxoplasma aldolase-1 antiserum or the myc-aldolase-1; the enzyme was found evenly distributed along the pellicle of both motile and non-motile parasites ( Figure 7D ) . These observations demonstrate that the translocation of aldolase-1 from the Toxoplasma cytoplasm to its pellicle is triggered by the decrease in [K+] surrounding the parasites in compromised host cells and that this response is mediated by an increase in [Ca2+]c in the parasite . Aldolase-1 translocation from the parasite pellicle to its cytoplasm upon host cell invasion is also triggered by the increased [K+] in its environment . Unlike what occurs during parasite egress , however , this process does not appear to be mediated by an increase in [Ca2+]c in the parasite . Although the molecular mechanism underlying aldolase-1 translocation is not known at this time , it is clear that neither F-actin nor microtubules are essential . Although we did not detect any redistribution of aldolase-1 in motile Toxoplasma tachyzoites and the translocation of aldolase-1 to the parasite periphery was not affected by cytochalasin-D ( Figure 7D ) , these findings do not necessarily conflict with a role of peripheral aldolase-1 in providing a link between the adhesin MIC2 and F-actin as was suggested by Jewett and Sibley [1] . We did , however , want to consider an alternative possibility in which translocation of aldolase-1 from the Toxoplasma cytoplasm to its periphery was only one element in the relocation of the entire glycolytic apparatus and hence a major source of ATP . To test this model , we expressed epitope-tagged versions of other enzymes along the glycolytic pathway of Toxoplasma tachyzoites and monitored their distribution in intracellular and extracellular parasites . As can be seen in Figure 8 and Figure S5 , enzymes at the beginning ( hexokinase ) , in the middle ( glyceraldehyde-3-phosphate dehydrogenase-1 ) , and at the end of the glycolytic pathway ( pyruvate kinase-1 ) demonstrated subcellular distribution patterns that are very similar to those of aldolase-1 . All three enzymes are found throughout the cytoplasm of intracellular Toxoplasma and associated with the pellicles of extracellular parasites . During glycolysis , a single NAD+ is converted to NADH for each conversion of glyceraldehyde-3-phosphate to 1 , 3-bisphosphoglycerate . As cells contain only a limited amount of NAD+ this critical compound needs to be regenerated constantly to ensure continuation of glycolysis . In the absence of oxidative phosphorylation , NAD+ , is regenerated in most eukaryotes by lactic acid fermentation . In this process , the end product of glycolysis , pyruvate , is converted to lactic acid by lactate dehydrogenase ( LDH ) , converting a single NADH to NAD+ in the process . Given that the data in Figure 8B demonstrate that all tested glycolytic enzymes in extracellular Toxoplasma are found in close association with the parasite's pellicle , we wondered whether Toxoplasma LDH was found in the same location . The Toxoplasma genome encodes two isoforms of this enzyme , LDH1 and LDH2 , of which the latter is only expressed in bradyzoites [24] . When myc-tagged Toxoplasma LDH1 was expressed in tachyzoites we found that , like the glycolytic enzymes , this protein is also found in the cytoplasm of intracellular parasites and in close association with the pellicle in extracellular organisms ( Figure 8 ) . In summary , hexokinase , aldolase-1 , glyceraldehyde-3-phosphate dehydrogenase-1 , pyruvate kinase , and lactate dehydrogenase-1 translocate from the Toxoplasma cytoplasm to its pellicle upon parasite egress , and back to the cytoplasm after host cell invasion . This finding suggests that all enzymes required for ATP production by glycolysis are translocated in a concerted fashion between the Toxoplasma cytoplasm and its pellicle depending on whether the parasite is inside or outside of its host cell . To characterize this pellicle association in more detail we fractionated Toxoplasma homogenates at different salt concentrations and monitored the distribution of aldolase-1 between the soluble and insoluble fractions . Association of aldolase-1 with the pellicle in extracellular parasites does not appear to involve a covalent or otherwise stable interaction with the IMC membrane or associated cytoskeletal elements . When extracellular parasites are mechanically disrupted at approximate physiological salt concentrations ( 150 mM ) and subjected to differential centrifugation , the majority of aldolase-1 is recovered in the supernatant . We noticed , however , that 20–30% of aldolase-1 was consistently recovered in the pellet fraction ( Figure 9A ) . This association was sensitive to the salt concentration in the extraction buffer . When extractions were performed at higher KCl concentration ( 300 mM ) all aldolase was recovered in the soluble fraction . At 25 mM KCl , on the other hand , we recovered 70–80% of aldolase-1 in the pellet fraction . To analyze this phenomenon in greater detail we compared the fractionation of aldolase-1 in intracellular and extracellular Toxoplasma at 25 mM KCl . As can be seen in Figure 9B , pellicle association of aldolase-1 is only observed in extracellular parasites under these conditions . The association is specific in that the cytoplasmic enzyme CDPK1 is completely solubilized under these conditions . Pellicle-association of aldolase-1 appears to require intact membranes as addition of Triton –X100 result in the complete solubilization of aldolase-1 . To determine if the salt-resistant fraction of aldolase-1 in extracellular Toxoplasma is indeed IMC associated , we homogenized parasites in 25 mM or 300 mM KCl buffer and performed pre-embedding immuno-electron microscopy on the isolated membrane fractions . It should be noted that association of the plasma membrane with the outer face of the IMC did not appear to be affected at the KCl concentrations used in this study . As can be seen in Figure 10 ( panels A , B ) , no aldolase was detected in association with the pellicle of intracellular Toxoplasma prepared in 25 mM KCl . In pellicles isolated from extracellular parasites at 25 mM KCl labeling of the cytoplasmic face of the IMC was readily detected ( Figure 10 , panels C and D ) . This labeling was absent from pellicles in samples prepared in the presence of 300 mM KCl ( Figure 10 , panel E ) . Taken together with the data shown in Figures 3 , 4 , and 5 these findings indicate that a substantial fraction of aldolase-1 in extracellular Toxoplasma is intimately associated with the cytoplasmic face of the IMC membrane .
Obligate intracellular parasites like Toxoplasma gondii and other apicomplexa face vastly different environments during their normal life cycles in that they move between different host animals , different organ systems , and between the extracellular and intracellular milieu . It is therefore critical that these parasites have the ability to sense and adapt to changes in their environment . One of the most striking of these differences is found between extracellular and intracellular apicomplexan parasites . Whereas extracellular parasites are highly motile they are incapable of replication . Intracellular parasites , on the other hand , are non-motile but replicate efficiently with doubling times of as little as 8 hours . This observation suggests that Toxoplasma needs to have the ability to adjust numerous cellular processes that support these processes depending on the environment it finds itself in . Considering its central role in all cellular processes , we reasoned that the production of ATP in Toxoplasma might be one process that would be tightly regulated with respect to overall activity and location as the parasite moves between the intracellular and extracellular environment . The Toxoplasma genome encodes all the enzymes required for energy production by glycolysis . It is not certain , however , if the parasite can generate energy by oxidative phosphorylation since the only recognizable homolog of pyruvate dehydrogenase appears to be targeted to the parasite's apicoplast where it is hypothesized to play a role in fatty acid biosynthesis [8] , [9] . To determine the contribution of glycolysis and oxidative phosphorylation to the production of energy needed for Toxoplasma motility , we analyzed parasite movement under conditions where either glycolysis or the mitochondrial electron transport chain was blocked . It was clear from these experiments that glycolysis is responsible for supplying the majority of energy required for parasite movement ( Figure 1 ) . Further analysis revealed that all glycolytic enzymes tested underwent a striking change in their cellular distribution after Toxoplasma egress from host cells ( Figures 3 , 8 , and S5 ) . Specifically , the enzymes were found throughout the parasite cytoplasm in intracellular Toxoplasma but relocated to the parasite pellicle after egress from the host cell . They remained at the parasite pellicle as long as parasites were extracellular or undergoing host cell invasion ( Figure 5 ) . In almost all cases , the glycolytic enzymes were distributed evenly along the pellicle of extracellular Toxoplasma and we did not detect any translocation of aldolase-1 along the anterior-to-posterior axis of the parasite during motility or host cell invasion ( Figure 5A and 7D ) . The glycolytic enzymes reverted to a smooth cytoplasmic distribution upon completion of host cell invasion ( Figure 6 ) . While a fairly uniform cytoplasm-to-pellicle translocation was observed during Toxoplasma egress , the pellicle-to-cytoplasm translocation occurred over a variable period of time , ranging from several minutes up to several hours , but was essentially complete 4 hours after invasion . The observation that aldolase-1 distribution along the anterior-to-posterior axis is not obviously altered in motile or invading parasites differs from what was reported by Jewett and Sibley [1] . The reason for this difference is unclear at this time but it may well be that different detection reagents recognize different pools of aldolase-1 . Translocation of the glycolytic enzymes between the Toxoplasma cytoplasm and its pellicle is controlled by [K+] in the parasites' environment ( Figure 7 ) . Incubation of isolated parasites in a medium with a [K+] consistent with the intracellular milieu of animal host cells ( ∼145 mM ) results in a cytoplasmic location of the enzymes tested . Transfer of these parasites to medium with a [K+] that is consistent with the extracellular milieu ( ∼5 mM ) results in the rapid translocation of the enzymes to the parasite pellicle ( Figure 7A ) . This translocation is completely reversed upon re-incubation of the parasites in media with intracellular levels of [K+] . We previously described that Toxoplasma egress is also activated in response to a transient increase in [Ca2+]c that is triggered by a reduction of the [K+] in the decaying host cell [20] . The translocation of glycolytic enzymes from the parasite cytoplasm to its pellicle that occurs in response to lower [K+] also appears to require an increase in [Ca2+]c as it is blocked by pre-incubation of parasites with BAPTA/AM ( Figure 7C ) . The reverse process , in which the glycolytic enzymes are translocated from the Toxoplasma pellicle to its cytoplasm , is induced by the increase in [K+] experienced by the invading parasite , but this process does not require an increase in [Ca2+]c in the parasite ( Figure 7C ) . The mechanism that brings about the actual translocation of the glycolytic enzymes between the Toxoplasma cytoplasm and its pellicle is not known at this time . Although Jewett and Sibley have demonstrated an interaction between aldolase-1 and F-actin , disruption of the latter with cytochalasin-D did not affect aldolase-1 translocation from the cytoplasm to the pellicle ( Figure 7D ) . The translocation of glycolytic enzymes is also not affected by the disruption of parasite microtubules with oryzalin ( Figure 7E ) . Although these observations rule out a major contribution of F-actin and microtubules in translocation of glycolytic enzymes in Toxoplasma , the possibility remains that a different active transport mechanism drives this process . Our observations are also consistent with a model that does not require such mechanisms , however . In this model , glycolytic enzymes diffuse freely through the cytoplasm of intracellular parasites . Toxoplasma egress and the accompanying increase in [Ca2+]c would then result in the generation of binding sites for the glycolytic enzymes on the IMC , resulting in their eventual immobilization on that structure . The specific mechanism through which the glycolytic enzymes are associated with the Toxoplasma pellicle is not clear at this time . The observation that association is disrupted at higher KCl concentrations suggests that it is not mediated by covalent modification of the protein by , for example , palmitoylation . The ease with which aldolase-1 is solubilized with detergents also argues against a high affinity interaction with the parasite's cytoskeleton . Instead , pellicle association appears to depend on ionic interactions with either lipids or proteins in the IMC membrane . It is interesting to note in this context that placement of a YFP tag at either the carboxy or amino-terminus of aldolase-1 disrupts relocation of the enzyme ( data not shown ) . Although this effect could well be due to disruption of the tertiary or quaternary structure of the enzyme , it is also possible that the presence of a large fusion partner disrupts the interaction of aldolase-1 with factors that are critical for its translocation or pellicle association . The pellicle of apicomplexan parasites consists of three membranes: the plasma membrane and the two membranes of the IMC . The space between the plasma membrane and IMC is only 18±0 . 6 nm wide ( Figure S1 ) yet it houses multiple processes that are critical to parasite survival such as the myosin XIV motor complex [5] , F-actin filaments [1] , transporters for nutrients and ion homeostasis , as well as systems that control these processes . Sustained Toxoplasma motility and general survival therefore requires a reliable supply of ATP into the space between the parasite plasma membrane and IMC . As we have demonstrated here , the glycolytic apparatus of Toxoplasma tachyzoites is closely associated with the pellicle in extracellular parasites . Specifically , we have found that a substantial fraction of aldolase-1 is closely associated with the cytoplasmic face of the IMC . It is very important to note here that our data are not inconsistent with the presence of aldolase-1 in the space between the plasma membrane and IMC as proposed by Jewett and Sibley [1] . Given that these two structures remain intimately associated with each other during our subcellular fractionation experiments ( Figure 10 ) it is unlikely that any aldolase-1 found in this space would be completely removed during extractions at higher KCl concentrations . This suggests then that only a small fraction of total aldolase-1 is likely to be present between the plasma membrane and IMC . This fraction could then be dedicated specifically to establishing an interaction between MIC2 and F-actin as proposed by Jewett and Sibley [1] . Our demonstration that Toxoplasma egress triggers a redistribution of its glycolytic enzymes from the cytoplasm to the IMC brings to mind the recent observation by Campanella et al [25] that multiple glycolytic enzymes , including aldolase , are intimately associated with the inner face of the plasma membrane of freshly isolated erythrocytes , but are cytoplasmic in deoxygenated cells [25] . These authors argued that close association of glycolytic enzymes with the erythrocyte plasma membrane could result in the generation of a highly compartmentalized pool of ATP , thus allowing for its direct and efficient consumption by e . g . nutrient or ion transporters . They also proposed that placing glycolytic enzymes in close proximity to each other at a membrane could improve the overall efficiency of ATP production by channeling reaction intermediates between individual enzymes . Our finding that glycolytic enzymes are associated with the pellicles of extracellular Toxoplasma tachyzoites indicates that ATP production in these organisms is also highly localized . In the case of Toxoplasma this would not only markedly improve ATP delivery to active transporter mechanisms in the parasite plasma membrane but also to its motile apparatus in the space between the plasma membrane and IMC . As was suggested for glycolysis in erythrocytes , the close association of glycolytic enzymes with the Toxoplasma pellicle could also markedly improve the overall efficiency of local ATP production by limiting the free diffusion of glycolytic reaction intermediates . This impact would be even greater if the glycolytic enzymes would physically associate with each other and thus allow for true substrate channeling . Although we have not been able to identify such complexes thus far , we do not rule out their existence , given that these interactions may well require labile cofactors or depend on specific but low-affinity interactions . Further analysis of the various enzymes is likely to shed light on this issue . Our experiments also need to be repeated on other members of the phylum Apicomplexa to determine if they extend to Cryptosporidium and Plasmodium species , causative agents of cryptosporidiosis and malaria .
Toxoplasma gondii tachyzoites of the RH ( HX− ) strain were grown in confluent monolayers of human foreskin fibroblasts ( HFF ) maintained in α minimal essential medium ( αMEM; Invitrogen , Carlsbad , CA ) supplemented with 2% fetal bovine serum ( Hyclone , Logan , UT ) , 2mM glutamine , 100 IU/ml penicillin and 100 µg/ml streptomycin at 37°C with 5% CO2 . EC buffer: 120 mM NaCl , 1mM CaCl2 , 5 mM MgCl2 , 25 mM Hepes-NaOH pH 7 . 2 , 1 mg/ml BSA . IC buffer: 142 mM KCl , 5 mM NaCl , 2 mM EGTA , 5 mM MgCl2 , 25 mM Hepes-KOH pH 7 . 2 , 1 mg/ml BSA . Where indicated , these buffers were supplemented 25 mM glucose , 25 mM 2-deoxyglucose , 2 mM KCN , or with 0 . 5 mM sodium pyruvate , 3 mM sodium lactate , 10 mM glutamine , or combinations thereof . IC-SUC buffer: 44 . 7 mM K2SO4 , 10 mM MgSO4 , 106 mM sucrose , 5 mM glucose , 20 mM Tris-H2SO4 pH 8 . 2 , 1 mg/ml BSA . The Toxoplasma aldolase-1 open reading frame was generated by PCR and cloned in pCR 2 . 1 TOPO ( Invitrogen ) . The insert was recovered after digestion of the plasmid with BglII and EcoRI and ligated between the BglII and EcoRI sites of pRSET B ( Invitrogen ) . Recombinant his6-tagged aldolase-1 was expressed in Escherichia coli BL21 ( DE3 ) strain ( Stratagene , La Jolla , CA ) and purified on a HisTrap Chelating HP column ( Amersham Biosciences , Piscataway , NJ ) . The recombinant protein was used to immunize a mouse by subcutaneous injection ( Cocalico Biologicals , Reamstown , PA ) . Anti-aldolase-1 antibodies were further purified by affinity chromatography on immobilized recombinant Toxoplasma aldolase . The open reading frames of TgGT1 , hexokinase ( HK ) , aldolase-1 ( ALD1 ) , glyceraldehyde-3-phosphate dehydrogenase-1 ( GAPDH1 ) , pyruvate kinase-1 ( PK1 ) , and the tachyzoite lactate dehydrogenase-1 ( LDH1 ) were amplified from Toxoplasma gondii cDNA ( Thermoscript RTPCR , Invitrogen ) using the following primer pairs: TgGT1 , 5′-gacggatccatcATGGCGACGGAGGAGATGCG-3′ and 5′-atgcctaggAATACCACCTCCGTCCCCTTGG-3′; HK , 5′-cttagatctaaaATGCAGCCTCGTCAACCAGGC-3′ and 5′-tggcctaggcacTCAGTTCACATCTGCGATCAG-3′; ALD1 , 5′-acaagatctaccATGTCGGGATACGGTCTTCCC-3′ and 5′-ctccctaggcgtTTAGTACACGTAGCGTTTCTC-3′; GAPDH1 , 5′-cacagatctaagATGGTGTGCAAGCTGGG-3′ and 5′-taacctaggagcTTACGCGCCGTCCTGGACG-3′; PK1 , 5′-ctcagatctacaATGGCATCTAAACAACCGC-3′ and 5′-ctgcctaggagtTTACTCCACAGTAAGAACC-3′; LDH1 , 5′-tagagatctaaaATGGCACCCGCACTTGTGCAG-3′ and 5′-tttcctaggcgcTTACGCCTGAAGAGCAGCAAC-3′ . The Bgl II , Bam HI , and Avr II restriction sites are underlined and non-coding sequences are shown in lowercase . Isolated PCR products for HK , ALD1 , GAPDH1 , PK1 , and LDH1 were digested with BglII and AvrII or BamHI and AvrII ( TgGT1 ) and inserted between the BglII and AvrII sites of the vector pTG70 , thus creating in-frame fusion between an N-terminal c-myc epitope and the different glycolytic enzymes . The PCR product for TgGT1 was digested with Bam HI and AvrII and ligated between Bgl II and Avr II sites of pTG168 thus creating an in-frame fusion between TgGT1 and a C-terminal myc epitope . Expression of the fusion proteins in both plasmids was driven by the α-tubulin promoter . After confirmation of all constructs by sequencing , they were transfected into Toxoplasma tachyzoites [5] . Stable transfectants expressing the myc-tagged protein of interest were selected by chloramphenicol as previously described [26] . Infected HFF cells ( 24 h ) were washed 3 times and mechanically disrupted on ice in ice-cold EC or IC buffer containing 25 mM glucose . Parasites were processed for immunofluorescence immediately after host cell egress ( T0 ) , or after an incubation of 15 minutes ( T15 ) or 60 minutes ( T60 ) at 37°C . Parasites that were incubated 60 minutes in IC buffer with glucose at 37°C were further washed 3 times in EC buffer with glucose , incubated 15 minutes ( T15 ) or 60 minutes ( T60 ) at 37°C and processed for immunofluorescence as described below . Parasites expressing myc-aldolase-1 ( 5×106 per coverslip ) were allowed to invade host cell monolayers in complete medium for 2 minutes and subsequently incubated for 30 minutes , 2 hours , 4 hours , or 24 hours at 37°C . After two washes in PBS , cells were incubated for 30 minutes at 4°C with rabbit anti SAG1 antiserum BSA/PBS . After 3 washes in PBS , cells were fixed for 15 minutes with methanol at −20°C and processed for immunofluorescence as described below . The fraction of motile parasites , as well as the extent and consistency of parasite movement was optimal if parasites were released from infected host cells 30–32 hours after infection . At this stage , the vast majority ( >95% ) of parasites were intracellular . Infected HFF monolayers were typically scraped into IC buffer at 4°C . Parasites were released from cells by serial passage ( 3–4× ) through a 18G needle and ( 2× ) through a 25G needle . Parasites were recovered by centrifugation for 10 minutes at 600× g and resuspended in the buffer of choice . Parasites ( 5×106 ) were placed on acid washed coverslips and incubated for 10 minutes at 25°C to allow for parasites attachment . Motility assays were performed for 15 minutes at 37°C . In experiments utilizing a nitrogen atmosphere , both the preincubation at 25°C and the actual motility assay at 37°C were performed in sealed vials flushed with dry nitrogen gas . Parasite motility was assessed by monitoring the trails of the cell surface antigen SAG1 deposited by moving parasites [27] . After fixation for 15 minutes in methanol at −20°C the parasites and deposited trails were visualized using a rabbit anti-SAG1 and Alexa594-conjugated goat-anti-rabbit IgG secondary antibodies . Length of individual trails was determined using ImageJ software ( National Institutes of Health , Bethesda , MD ) . Parasites that had left a trail longer than 2 µm were considered motile , parasites with shorter or no trails were considered non-motile . In the vast majority of cases , trails left by motile parasites exceeded 10 microns . Host cells that were infected for 24 hours were washed 3 times in either IC buffer or IC-SUC buffer containing 25 mM glucose and either DMSO or 20 µM BAPTA/AM . Infected host cells were harvested in the same buffers and passed through a 25G needle to release the parasites from the parasitophorous vacuole . After a 30 minute incubation at 37°C , parasites were washed 3 times in EC buffer containing 25 mM glucose and either DMSO or 20 µM BAPTA/AM , incubated for 30 minutes at 37°C and processed for immunofluorescence . Likewise , extracellular parasites were washed 3 times in EC buffer containing 25 mM glucose and DMSO or 20 µM BAPTA/AM , and incubated 30 minutes at 37°C . After 3 washes in IC or IC-SUC buffers containing 25 mM glucose and either DMSO or 20 µM BAPTA-AM , parasites were incubated 30 minutes at 37°C and processed for immunofluorescence . Cytochalasin D treatment was performed by incubating intracellular parasites for 15 minutes at 37°C with 10 µM drug . Infected host cells were disrupted by passing them through a 25G needle . Parasites were washed twice in EC buffer containing 25 mM glucose and 10 µM cytochalasin D before performing motility assay as described above but in the presence of 10 µM cytochalasin D . Oryzalin treatment was performed in the same manner except that drug was used at a concentration of 2 . 5 µM and intracellular parasites were treated for 24 hours . Parasites pellets ( 1×108 ) were resuspended in 25 mM MOPS-KOH pH 7 . 0 containing 5 mM MgCl2 , protease inhibitors ( Sigma ) , and 25 mM , 150 mM , or 300 mM KCl and disrupted by sonication for 2×20 seconds at 4°C using 50% power output and a continuous duty cycle ( Branson Sonifier 450 , Danbury , CT ) . Homogenates were centrifuged for 10 minutes at 14 . 000× g and 4°C . The pellets were resuspended in the same buffers containing 1% Triton X-100 , incubated on ice for 5 minutes , and subjected to centrifugation as above . Samples of the original homogenate , the supernatant , and pellet fractions equivalent to 5×106 parasites were separated by SDS-PAGE on 10% polyacrylamide gels , transferred to nitrocellulose , and probed with the indicated antibodies . Bound antibodies were visualized using a Li-Cor Odyssey Infrared Imaging System ( Lincoln NE ) and the appropriate secondary antibodies . Extracellular Toxoplasma tachyzoites were allowed to adhere to acid-washed coverslips for 10 minutes at 4°C prior to fixation . Routinely , intracellular and extracellular parasites were fixed in cold methanol ( −20°C ) for 15 minutes at room temperature . Where indicated , parasites and parasite-infected cells were fixed instead in 3% formaldehyde and 0 . 01% glutaraldehyde in PBS for 15 minutes at 4°C . Coverslips were washed twice in PBS and remaining reactive aldehyde groups were neutralized with 1 mM NaBH4 in PBS for 10 minutes . After three washes in PBS cells were permeabilized for 10 minutes at 4°C with 1% TX100 in PBS . After 3 washes in PBS , samples were blocked with 3% BSA in PBS ( BSA/PBS ) for 15 minutes and subsequently incubated for 30 minutes with the indicated primary antibodies in BSA/PBS . After three washes in PBS , coverslips were incubated for 30 minutes with goat-anti-rabbit or goat-anti-mouse IgG conjugated to Alexa 488 or Alexa 594 ( Molecular probes , Eugene , OR ) and DAPI ( 5 ng/ml ) in BSA/PBS . Coverslips were washed three times in PBS and mounted in MOWIOL . Cells were examined using a Nikon Eclipse TE2000-U epifluorescence microscope . Stacks of images were acquired using a Hamamatsu digital CCD camera ( IEEE1394 ) directed by Metamorph ( version 6 . 2r5 , Sunnyvale , CA ) software and processed for optical deconvolution by Auto deblur+Auto visualize program ( version 9 . 3 . 3 , Watervliet , NY ) using 10 total iterations and low noise level for the algorithm settings . Extracellular and intracellular parasites ( 108 ) were resuspended on ice in 25 mM MOPS-KOH pH 7 . 0 containing 5 mM MgCl2 , protease inhibitors ( Sigma ) , and 25 mM or 300 mM KCl and disrupted by sonication as described above . The pellicle fraction was recovered by centrifugation for 10 minutes at 14 . 000× g and 4°C , resuspended in the same buffer and centrifuged again . The pellet fractions were resuspended in 3%BSA in the homogenization buffer containing mouse-anti-aldolase-1 or a nonspecific antiserum at 1∶50 dilution for 1 hour on ice followed by incubation with 10 nm gold-conjugated donkey anti-mouse IgG secondary antibody ( Jackson Immuno Research Laboratories Inc . , West Grove , PA ) diluted 1/15 in the same buffer . Samples were washed 3 times in homogenization buffer and fixed in 1% glutaraldehyde in the same buffer for 1 hour at 4°C . After one wash in PBS , samples were incubated in 1% tannic acid in PBS for 20 minutes at 4°C . After 3 washes in 0 . 1 M sodium cacodylate pH 7 . 3 , samples were treated with 1% osmium tetroxide in 0 . 1 M sodium cacodylate pH 7 . 3 for 20 min . After 3 washes in water , samples were dehydrated in increasing concentrations of ethanol and after 2 washes in propylene oxide , embedded in Epon . The polymerized blocks were sectioned at 60 nm with a Leica Ultracut UCT ultramicrotome ( Leica Microsystems ) . Sections were stained with 2% uranyl acetate and Sato's lead stain and were viewed on an FEI Tecnai 12 electron microscope ( FEI , Hillsboro , OR ) . Images were collected with a Gatan model 794 multiscan digital camera ( Gatan , Pleasanton , CA ) .
|
Protozoan parasites of humans are important causes of disease throughout the world . Amongst these , the apicomplexan parasites are an especially important group of pathogens , as they include Plasmodium species , the causative agents of malaria , and Toxoplasma gondii , an important cause of disease in people with AIDS . These parasites divide their stay in the infected human into two discrete stages , the motile extracellular stage that is responsible for parasite spread throughout its host , and the intracellular stage that is devoted entirely to replication . Here we demonstrate that Toxoplasma relocates its main source of energy when it moves between the intracellular and extracellular environment . It appears that this allows the parasite to optimize energy delivery to those processes that are critical for extracellular survival and invasion into new host cells and those required for subsequent intracellular growth and replication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/chemical",
"biology",
"of",
"the",
"cell",
"infectious",
"diseases/hiv",
"infection",
"and",
"aids",
"biochemistry/cell",
"signaling",
"and",
"trafficking",
"structures",
"microbiology/parasitology",
"infectious",
"diseases/protozoal",
"infections",
"microbiology/microbial",
"physiology",
"and",
"metabolism",
"cell",
"biology/chemical",
"biology",
"of",
"the",
"cell",
"microbiology/medical",
"microbiology"
] |
2008
|
Host Cell Egress and Invasion Induce Marked Relocations of Glycolytic Enzymes in Toxoplasma gondii Tachyzoites
|
The Vif protein of HIV-1 allows virus replication by degrading several members of the host-encoded APOBEC3 family of DNA cytosine deaminases . Polymorphisms in both host APOBEC3 genes and the viral vif gene have the potential to impact the extent of virus replication among individuals . The most genetically diverse of the seven human APOBEC3 genes is APOBEC3H with seven known haplotypes . Overexpression studies have shown that a subset of these variants express stable and active proteins , whereas the others encode proteins with a short half-life and little , if any , antiviral activity . We demonstrate that these stable/unstable phenotypes are an intrinsic property of endogenous APOBEC3H proteins in primary CD4+ T lymphocytes and confer differential resistance to HIV-1 infection in a manner that depends on natural variation in the Vif protein of the infecting virus . HIV-1 with a Vif protein hypo-functional for APOBEC3H degradation , yet fully able to counteract APOBEC3D , APOBEC3F , and APOBEC3G , was susceptible to restriction and hypermutation in stable APOBEC3H expressing lymphocytes , but not in unstable APOBEC3H expressing lymphocytes . In contrast , HIV-1 with hyper-functional Vif counteracted stable APOBEC3H proteins as well as all other endogenous APOBEC3s and replicated to high levels . We also found that APOBEC3H protein levels are induced over 10-fold by infection . Finally , we found that the global distribution of stable/unstable APOBEC3H haplotypes correlates with the distribution a critical hyper/hypo-functional Vif amino acid residue . These data combine to strongly suggest that stable APOBEC3H haplotypes present as in vivo barriers to HIV-1 replication , that Vif is capable of adapting to these restrictive pressures , and that an evolutionary equilibrium has yet to be reached .
The human APOBEC3 ( A3 ) family of DNA cytosine deaminases is encoded by seven genes arranged in tandem on chromosome 22 ( reviewed by [1] , [2] ) . These proteins inhibit the replication of a broad number of parasitic elements , including many retroviruses , some DNA viruses , and several endogenous retroelements and retrotransposons by both deaminase-dependent and -independent mechanisms ( reviewed by [2]–[4] ) . Several lines of evidence indicate that A3D , A3F , A3G , and A3H contribute to HIV-1 restriction by packaging into assembling virus particles and , upon virus entry into new target cells , deaminating viral cDNA cytosines to uracils and impeding the progression of reverse transcription ( reviewed by [2]–[4] ) . These cDNA uracil lesions template the insertion of genomic strand adenines during second strand reverse transcription and ultimately manifest as G-to-A mutations which destroy virus infectivity by the introduction of deleterious missense and nonsense mutations within viral open reading frames . HIV-1 encodes an accessory protein called Vif ( virion infectivity factor ) that functions to neutralize cellular A3 proteins . HIV-1 Vif assembles an E3 ligase complex comprised of CBF-β , ELOB , ELOC , CUL5 , and RBX2 to mediate the poly-ubiquitination and proteasomal degradation of restrictive A3s ( [5] , [6] and references therein ) . This process enables HIV-1 to replicate in its main target cell , CD4+ T lymphocytes , which express multiple A3s and would otherwise be non-permissive for viral replication [7]–[9] . However , this process is less than 100% effective , as G-to-A mutations are commonly observed in viral sequences from clinical specimens and , depending on the patient , may be biased toward a GG-to-AG dinucleotide context indicative of A3G or a GA-to-AA characteristic of A3D , A3F , and/or A3H [7]–[23] . The present day human A3 locus is a result of multiple gene duplication events during evolution from an ancestral mammalian locus [24] . Unlike the Z1 domain ( e . g . A3A ) or Z2 domain ( e . g . A3C ) deaminase genes , which show considerable copy number variation between mammalian phylogenetic tree branches , the Z3 domain gene ( e . g . A3H ) exists in only one copy in all mammalian genomes sequenced to date [25] . Overexpression studies have shown that A3H and the orthologous Z3 domain deaminases from several mammalian species have a conserved capacity to restrict retrovirus replication , and likewise are neutralized by the Vif proteins of each species' lentivirus [25]–[33] . However , a role for A3H in vivo has not yet been elucidated since endogenous expression of the protein has been difficult to detect [34] . A3H is the most polymorphic of the human A3 genes due to circulation of at least 7 distinct A3H haplotypes [35] , [36] . These haplotypes are comprised of various combinations of 5 single nucleotide polymorphisms located in exons 2 , 3 and 4 that range in allele frequencies globally from 6 to 87% [37] . Previous overexpression and pulse-chase experiments have shown that 3 A3H haplotypes yield proteins with relatively long half-lives ( stable ) , 1 produces a protein with weak stability , and another 3 make completely unstable proteins [35] , [36] , [38] . For example , transient transfection of cells with A3H haplotype II cDNA with SNPs at residues 15 , 18 , 105 , 121 , and 178 ( NRRDD ) yields a protein readily detectable by immunoblotting , whereas A3H haplotypes I ( NRGKE ) and III ( ΔRRDD ) produce weakly expressed or undetectable proteins , respectively [35] , [36] . However , it is not yet known whether these dramatic haplotype-associated stability/instability phenotypes also manifest for endogenous A3H proteins expressed in primary immune cells . This distinction is important because only stably expressed haplotypes would be predicted to exert selective pressure on HIV-1 replication and potentially contribute to virus restriction and diversification in vivo . Indeed , a recent paper linked putatively stable A3H haplotype II to enhanced restriction , higher frequencies of G-to-A mutation , and more favorable clinical phenotypes ( lower viral loads and higher patient CD4+ T cell numbers ) [39] . Here , we test the hypothesis that HIV-1 infectivity will be influenced by both host A3H genotype as well as viral vif genotype . We detect the expression and induction of endogenous A3H protein from stable A3H alleles following HIV-1 infection . By constructing a set of molecular clones encoding Vif proteins of varying abilities to antagonize stable A3H , we showed that expression of one allele of stable A3H is sufficient to inhibit hypo-functional Vif virus replication and promote G-to-A mutagenesis in the expected GA-to-AA dinucleotide context . In contrast , a hyper-functional Vif variant effectively counteracted stable A3H activity . Virus adaptation to stable A3H may be occurring on a global scale because the geographic distribution of a key hyper/hypo-Vif amino acid correlates with the distribution of stable/unstable A3H haplotypes . Taken together , these data demonstrate that stable A3H haplotypes affect HIV-1 replication in the primary target cells of the virus , and further suggest that viral evolution requires the Vif protein to adapt to counteract the restrictive pressure imposed by these enzymes . Therefore , the combination of A3H haplotype and viral vif variation may be critical and linked factors in HIV-1 adaptation to human populations .
To address whether the dramatic protein stability phenotypes observed previously in overexpression studies ( [36] , [38] , [40]; Figure 1A ) extend to endogenous A3H haplotypes in primary T cells , we used our recently developed anti-A3H monoclonal antibodies [34] as well as a new commercial anti-A3H polyclonal antibody to probe protein expression in CD4+ T lymphocytes from healthy donors with different haplotypes ( Materials and Methods , Figure 1B & Figure S1 ) . Primary CD4+ lymphocytes were isolated by negative selection , stimulated with IL2 and PHA for 72 hrs , and subjected to immunoblotting . Three of 24 donors were heterozygous for one copy of a predicted stable A3H allele , and none were homozygous ( 6% allele frequency; Table S1 ) . Anti-A3H immunoblots demonstrated clear differences in steady-state protein levels , with robust detection of haplotypes II and V , weaker detection of haplotype I , and no detection of haplotypes III and IV ( Figure 1B ) . A3G and HSP90 protein levels were expressed similarly between donors . To confirm the prediction from previous overexpression studies that the observed difference in A3H protein levels is not at the RNA level , RT-qPCR [9] was used to quantify A3 mRNA levels in primary CD4+ T lymphocytes . Similar A3H mRNA levels were observed regardless of haplotype ( Figure 1C ) . The mRNA levels of the other A3 genes were also unrelated to A3H genotype , with some minor variation observed between donors ( typically less than 2-fold , as reported [8] , [9] ) . Thus , consistent with prior overexpression and pulse-chase experiments to measure protein half-life [35] , [36] , [38] , the large difference between the various A3H haplotypes in primary CD4+ cells is most likely due to a protein level mechanism . Primary CD4+ T lymphocytes express 6 A3 family members [8] , [9] . We asked whether or not the ability of Vif to antagonize A3H could be separated from its ability to antagonize the other A3 proteins expressed in primary CD4+ T cells . Previous work indicated that Vif proteins of different HIV-1 isolates show varying capacities to counteract stable A3H ( NL4-3/IIIB and LAI [7] , [25] , [34] , [41] , [42] ) , but these studies did not address spreading infections in T cells where A3D , A3F , and especially A3G selective pressures might influence Vif function . To help inform this construction , we performed a series of HIV-1 IIIB N48H adaptation experiments in which this lab-derived strain was subjected by stepwise passage to increasing ratios and levels of stable A3H haplotype II expressed in SupT11 cells ( Figure 2A & B ) . An N48H variant was used to initiate these experiments because prior work had already demonstrated that this natural variation improved the A3H counteraction capacity of the Vif protein of IIIB/NL4-3 [42] . These experiments were initiated by infecting ( MOI 0 . 05 ) uniform pools of stable A3H-low expressing cells ( weakly selective ) and , after an 8 day incubation , viral supernatants were transferred to the next highest selective condition , and this process was repeated until 100% stable A3H-high conditions were reached ( Materials and Methods ) . In comparison to the starting virus , HIV-1 IIIB N48H , which only showed fast replication kinetics in SupT11 control vector and A3H-low expressing cells , one of the adapted viruses gained the capacity to replicate quickly in SupT11 cells expressing high levels of stable A3H . The majority ( 5/9 ) of vif sequences from this adapted population encoded a K63E amino acid substitution . Spreading infection experiments with an isogenic set of molecular clones showed that K63E combined with N48H to improve virus replication even in the presence of high levels of stable A3H ( Figure 2C ) . Furthermore , we noticed that the K63E substitution recovered in these adaptation experiments is part of a cluster of 4 amino acids that distinguishes the IIIB lab strain from an isolate recovered from a homozygous stable A3H haplotype II patient [34] . The Vif protein from this particular isolate is even better than LAI Vif at counteracting stable A3H haplotype II [34] . We therefore predicted that the combination of N48H ( to be more LAI Vif-like [42] ) and GDAK60-63 to EKGE60-63 ( to be more haplotype II patient Vif-like [34] ) would result in a hyper-functional Vif protein capable of fully antagonizing stable A3H haplotype II ( hyper-Vif; Figure 3A ) . In addition , the separation-of-function approach required the generation of a Vif variant even more sensitive than lab-Vif to restriction by stable A3H haplotype II . This was done by introducing F39V into the HIV-1 IIIB lab strain , which is a substitution that confers sensitivity to stable A3H haplotype II [41] ( Figure 3A ) . The A3 neutralization activities of the hyper- , lab- , and hypo-Vif HIV-1 IIIB variants were assessed by comparing spreading infection kinetics in SupT11 cell lines stably expressing a control vector , A3D-HA , A3F-HA , A3G-HA , and low , intermediate , and high levels of untagged A3H haplotype II ( Figures 3B , 3C & S2 ) . The A3 expression level in each SupT11 clone was sufficient to delay ( A3D ) or fully suppress ( A3F , A3G , A3H haplotype II ) replication of a Vif-null HIV-1 IIIB control virus , consistent with our original studies using these stable cell lines [7] . Importantly , the hyper- , lab- , and hypo-Vif HIV-1 IIIB variants spread with nearly identical efficiencies in the SupT11 cell lines expressing the control vector , A3D-HA , A3F-HA , and A3G-HA , demonstrating fully intact capacities to neutralize these restriction factors . In contrast , each isolate exhibited differential replication kinetics on the SupT11 lines expressing low , intermediate , and high levels of stable A3H haplotype II . The hyper-Vif isolate replicated with strong kinetics under all conditions including the highest stable A3H haplotype II expression levels , the lab-Vif isolate was delayed under intermediate and high A3H expression conditions , and the hypo-Vif isolate showed restricted replication phenotypes almost indistinguishable from the Vif-deficient control . These data demonstrate that naturally occurring amino acid residues that positively or negatively influence Vif-mediated neutralization of A3H can do so without impacting Vif activity against A3D , A3F , and A3G and therefore allow for a direct interrogation of endogenous A3H function in primary T lymphocytes . To determine the relative importance of the polymorphisms of A3H and Vif on HIV-1 replication in primary cells , a series of spreading infection experiments was initiated in CD4+ cells from individuals who encode different haplotypes of A3H with viruses encoding the hyper- , lab- , and hypo-Vif alleles that differentially antagonize A3H . We found that all 3 viruses replicated with similar kinetics in primary CD4+ T cells from donors homozygous for unstable A3H haplotype I or heterozygous for unstable A3H haplotypes III and IV . Indicating that hyper- , lab- , and hypo-Vif viruses are all capable of neutralizing the restrictive levels of A3D , A3F , and A3G expression in primary cells ( Figure 4A & S3 ) . In contrast , only the hyper-Vif virus replicated with robust kinetics in CD4+ T cells from donors heterozygous for stable A3H haplotypes II or V ( Figure 4B & S3 ) . The lab-Vif and hypo-Vif viruses showed delayed and strongly inhibited replication kinetics , respectively , in CD4+ T cells from donors heterozygous for stable A3H haplotypes ( Figure 4B & S3 ) . All of the heterozygous stable A3H haplotype II donors also had a copy of unstable A3H haplotype I , so these large differences in replication kinetics are due to only a single stable A3H allele . Thus , these experiments demonstrate that even a single allele of endogenous stable A3H constitutes a formidable barrier to replication of susceptible HIV-1 isolates . The APOBEC3 proteins must be packaged into viral particles to restrict HIV-1 replication . To confirm that stable , endogenous A3H acts by this mechanism , aliquots of cells and virus-containing supernatants were taken on days 3 , 5 , 7 and 9 post-infection for each donor/virus condition and analyzed by immunoblotting ( Figure 4C ) . First , we observed that A3H is induced at the protein level over the course of infection . This is most evident in cells infected with the hypo-Vif virus , most likely because this Vif variant fails to bind endogenous A3H and trigger its degradation . The level of A3H induction observed here at the protein level corresponds roughly to the level of induction observed previously at the mRNA level ( >10-fold [7] ) . Second , there is a strong correlation between cellular Vif , cellular A3H , and packaged A3H levels . This is most evident upon direct comparison of the hyper-Vif and hypo-Vif infections . For the hyper-Vif infection of stable haplotype II cells , Vif disappears , cellular A3H is induced weakly , and very little viral A3H is observed in particles . In contrast , for the hypo-Vif infection of stable haplotype II cells , Vif decreases modestly ( consistent with its role in mediating the degradation of A3D , A3F , and A3G ) , cellular A3H is induced strongly , and large amounts of A3H are observed in particles . As expected from the SupT11 experiments described above , the lab-Vif spreading infection elicited intermediate phenotypes . Most importantly , levels of A3H in viral particles correlated with virus replication kinetics , with the hyper-Vif virus with low A3H incorporation spreading robustly and the hypo-Vif virus with high A3H incorporation being restricted almost completely . Thus , HIV-1 replication , as well as the induction and encapsidation of A3H , are influenced by both host A3H haplotype and the viral vif genotype . The hallmark activity of HIV-1-restrictive A3 family members is viral cDNA deamination of C-to-U , with the uracil lesions being converted into genomic strand G-to-A mutations ( reviewed by [3] , [4] , [43] , [44] ) . To determine the correlation between mutagenesis and the relative restriction incurred by each of the separation-of-function Vif variants in combination with different A3H haplotypes , the viruses from each stable and unstable A3H experimental condition were subjected to analysis by differential DNA denaturation ( 3D ) -PCR [7] , [45] , [46] . This technique depends on the fact that every PCR amplicon has a characteristic denaturation temperature that must be reached in order to yield visible PCR product , and G-to-A mutations within the amplicon will result in the appearance of product at significantly lower temperatures . After 15 days of spreading infection , viral supernatants were removed and used to infect CEM-GFP reporter cells . After allowing 2 days for infection and integration , total cellular DNA was prepared , quantified , and subjected to 3D-PCR analysis of a 564 bp HIV-1 pol region ( Figure 4D ) . All viruses from unstable A3H haplotype III/IV expressing cells showed no evidence for G-to-A hypermutation , as the lowest denaturation temperature with visible PCR product was the same as that of the original molecular clone . The level of G-to-A mutation in hypo-Vif viruses from stable A3H haplotype II/I cells was clearly the highest , but levels for lab-Vif and hyper-Vif viruses were also significantly above the background level established by the amplification cut-off for the original molecular clone . Interestingly , a significant level of G-to-A mutation was also evident in hypo-Vif and lab-Vif viruses ( but not in hyper-Vif virus ) from unstable haplotype I/I cells , indicating that A3H haplotype I protein is capable of lower levels of viral cDNA deamination and may contribute to HIV-1 mutagenesis , though not to obvious infectivity decreases . To analyze the mutation spectrum and local dinucleotide mutation contexts , we used normal high-fidelity PCR ( 98°C denaturation temperature ) and Sanger sequencing to analyze the pol region of proviral DNA from an independent experiment ( i . e . , viruses from day 15 of the experiment shown in Figure 4A–C ) . Consistent with the 3D-PCR experiment discussed above , the highest level of G-to-A mutation occurred in hypo-Vif viruses from cells expressing stable A3H haplotype II , and the lowest level of G-to-A mutation occurred in hyper-Vif viruses from cells expressing unstable A3H haplotype I ( 5 . 0 G-to-A mutations per kb versus 0 . 2 G-to-A mutations per kb; Figure 4E ) . Most ( 76% ) of the hypo-Vif viruses' G-to-A mutations occurred in a GA-to-AA context , consistent with the 5′TC deamination preference of A3H shown previously [7] , [39] , [47] . In contrast , few G-to-A mutations were observed in a GG-to-AG context characteristic of A3G , further supporting the SupT11 experiments shown above that demonstrate that each of the Vif separation-of-function variants retains equivalent and strong A3G antagonism ( Figure 4E & S4 ) . If A3H exerts selective pressure on HIV-1 , then Vif variants most capable of neutralizing its activity should predominate in areas where stable A3H haplotypes also predominate . A geographic breakdown of 9713 HIV-1 isolates represented in the Los Alamos database revealed that F39 ( an A3H resistance residue ) predominates in Africa , whereas V39 ( an A3H susceptibility residue ) predominates in Asia ( Figure 5A & Table S2 ) . In addition , the N48 residue in Vif that confers sensitivity to high levels of stable A3H haplotype II [7] , [41] , [42] in HIV-1 IIIB and NL4-3 , is present in only 9% of African isolates but in 30% of isolates from Asia . Interestingly , these Vif allelic distributions correspond to the worldwide estimates for A3H haplotypes from the 1000 genomes project previously reported [35] , [48] , with stable haplotypes predominating in Africa and unstable haplotypes in Asia ( Figure 5A & Table S3 ) . The estimated relative risk for having the Vif variant most capable of neutralizing A3H given the stable A3H genotype relative to the unstable genotype is 2 . 0 with a 95% credible set of ( 1 . 7 , 2 . 4 ) . Thus , HIV-1 Vif appears to have the capacity to adapt to the A3H haplotype of a population . A major prediction is therefore that stable A3H may be a protective factor in HIV-1 acquisition , especially in instances in which a stable A3H haplotype individual is exposed to a hypo-Vif inoculum from an unstable A3H haplotype patient ( Figure 5B ) . All current evidence indicates that Vif heterodimerizes with CBF-β , directly binds restrictive A3s , and recruits an E3-ligase ubiquitin complex to target them for proteasomal degradation [3] , [4] , [43] , [44] . Recently , a crystal structure of the Vif-CBF-β-ELOB-ELOC-CUL5 complex was determined , providing long-awaited details of the molecular architecture of Vif [49] . The separation-of-function Vif variants described here provide an opportunity to define the A3H interaction surface . Interestingly , all of the amino acids that comprise these variants map to the same solvent-exposed surface of Vif ( Figure 6A ) . The critical hypo- and hyper-Vif residues , F39 and GDAK60-63 , are particularly close together . Moreover , the delayed and differential spreading infection phenotypes of HIV-1 isolates with each of these single amino acid substitutions in SupT11 T cells expressing high levels of stable A3H haplotype II indicates that each of these residues makes partial contribution to the overall hyper-Vif phenotype ( Figure 6B ) . Analogous but physically distinct separation-of-function Vif mutants have been described for A3F and A3G . For instance , the DRMR14-17 motif is required for A3F degradation , but does not affect A3G neutralization [50] , [51] . Furthermore , another motif , YRHHY40-44 , specifically compromises the A3G interaction [51] . The fact that the A3H interaction surface defined above and these A3F- and A3G-specific motifs map to distinct solvent exposed surfaces strongly indicates that Vif may use different binding modes in order to neutralize each of these restriction factors .
A3H is the most polymorphic A3 family member in the human population , and here we provide the first protein-level demonstration that the differential stability and inducibility of endogenous A3H haplotypes in primary T cells impacts HIV-1 replication in its normal target cells . We also report that natural variation in HIV-1 Vif results in the differential neutralization of stable A3H haplotype II and influences virus replication and hypermutation activities in primary CD4+ lymphocytes . The hyper-Vif virus showed strong replication kinetics and low levels of G-to-A mutation under all conditions tested , whereas the hypo-Vif virus was only restricted and hypermutated in primary CD4+ T lymphocytes expressing endogenous levels of stable A3H haplotype II . Because the separation-of-function variants were based on naturally occurring HIV-1 amino acid residues , our data combine to indicate that stable haplotypes of endogenous A3H may constitute a replication barrier to a significant subset of circulating HIV-1 strains . This conclusion is supported by significant correlations between A3H haplotype stability and predicted Vif functionality in worldwide human and HIV-1 genotype information ( p<0 . 001; Figure 5 and Tables S2 & S3 ) . Homology to other A3s with high-resolution structural information indicates that N15 is a highly conserved residue near the end of α-helix 1 and therefore most likely required for A3H structural integrity . NMR and mutagenesis studies with the catalytic domain of A3G have shown that this helix is essential for the stability and integrity of the entire deaminase domain , as it makes several essential contacts with internal residues [52] , [53] . For instance , an alanine substitution of the homologous A3G residue N208 renders the enzyme catalytically dead [52] . In contrast , the glycine at position 105 is predicted to be in the middle of β-strand 4 , located far from N15 and on the backside of the enzyme relative to the catalytic zinc-coordinating active site . This location and the observed intermediate stability of A3H haplotype I protein combine to suggest a different mechanism , possibly by altering an interaction with a cellular binding partner [34] . Consistent with these predictions , all evidence indicates that A3H haplotypes III and IV ( ΔN15 ) encode complete loss-of-function proteins , whereas haplotype I ( G105 ) encodes a hypofunctional variant because higher-than-background levels of G-to-A mutations are observed in the hypo-Vif virus in haplotype I expressing T cells . Our data agree with the main conclusion of a study published recently by the Simon group [39] . They concluded that A3H haplotype II is a relevant HIV-1 restriction factor by showing more G-to-A mutations accumulating in viruses replicating in A3H haplotype II donor PBMC , in comparison to the same viruses replicating in haplotype I donor PMBC [39] . Our studies help explain their results by showing that stable A3H haplotypes are indeed expressed at the protein level in primary HIV-1 target cells and that , depending upon the functionality of the infecting virus's Vif protein , the outcomes can range from aphenotypic ( hyper-Vif ) to strong infectivity restriction and hypermutation ( hypo-Vif ) . Importantly , the wide variation in G-to-A hypermutation levels observed in our controlled experiments may help explain the similarly wide variation reported in patient derived HIV-1 sequences [7]–[23] . Variation occurs for both overall G-to-A mutation frequency and for local dinucleotide preferences with , in many instances , GA-to-AA mutations predominating ( e . g . [10] , [11] ) . These in vivo biases could be due to the combination of stable A3H haplotypes and infection by hypo-functional Vif isolates . The results presented here , together with recent data from the Simon group [39] , combine to indicate that stable A3H haplotypes may be functioning as contemporary HIV-1 restriction factors . Stable A3H haplotypes may contribute to disease progression by limiting HIV-1 replication within an infected individual , as indicated by statistically lower viral loads and higher CD4+ T cell counts in haplotype II patients in comparison to haplotype I patients [39] . However , stable A3H haplotypes may also affect rates of transmission . In particular , our studies predict lower rates of virus acquisition when an infected patient has an unstable haplotype and their uninfected partner has a stable A3H haplotype ( Figure 5B ) . Moreover , in instances where HIV-1 breaches this transmission barrier , our studies predict that the Vif protein will have to adapt in order to effectively counteract stable A3H . Thus , HIV-1 may need to adapt differently to the A3 repertoire in different humans , supported by correlations between worldwide Vif and A3H genotypes ( p<0 . 001; Figure 5 and Tables S2 & S3 ) . Overall , polymorphisms in human A3H and HIV-1 Vif appear to be combining to actively limit HIV-1 pathogenesis .
Vif-proficient ( GenBank EU541617 ) and Vif-deficient ( X26 , X27 ) HIVIIIB A200C proviral constructs have been described [54] , [55] . Substitutions to construct the hypo-Vif and hyper-Vif variants were introduced with site-directed mutagenesis using primers RSH6951 5′TCA-AGG-AAA-GCT-AAG-GAC-TGG-GTT-TAT-AGA-CAT-CAC-TAT-GAA-AG & RSH6952 5′CTT-TCA-TAG-TGA-TGT-CTA-TAA-ACC-CAG-TCC-TTA-GCT-TTC-CTT-GA for the F39V substitution , RSH6949 5′TTT-ATA-GAC-ATC-ACT-ATG-AAA-GTA-CTC-ATC-CAA-AAA-TAA-GTT-CAG-AAG-TAC-AC & RSH6950 5′GTG-TAC-TTC-TGA-ACT-TAT-TTT-TGG-ATG-AGT-ACT-TTC-ATA-GTG-ATG-TCT-ATA-AA for the N48H substitution , and RSH6965 5′TCC-AAA-AAT-AAG-TTC-AGA-AGT-ACA-CAT-CCC-ACT-AGA-GAA-GGG-CGA-GTT-AGT-AAT-AAC-AAC-ATA-TTG-GGG-TCT-GCA-TAC-AGG & RSH6966 5′CCT-GTA-TGC-AGA-CCC-CAA-TAT-GTT-GTT-ATT-ACT-AAC-TCG-CCC-TTC-TCT-AGT-GGG-ATG-TGT-ACT-TCT-GAA-CTT-ATT-TTT-GGA for the GDAK60-63EKGE substitutions . SupT11 and CEM-GFP T cells were maintained in RPMI supplemented with 10% fetal bovine serum ( FBS ) and 0 . 5% penicillin/streptomycin ( P/S ) . 293T cells were cultured in DMEM supplemented with 10% FBS and 0 . 5% P/S . The generation and characterization of the SupT11 panel stably expressing vector , A3D-HA , A3F-HA , A3G-HA , and untagged A3H haplotype II have been described [7] . APOBEC3H monoclonal antibodies P1H6-1 , P1D8-1 , P3A1-1 , and P3A3-A10 were generated previously [34] and purified by ammonium sulfate precipitation . We presume that the increased sensitivity of these antibodies over the original description [34] is due to purification and increased concentration relative to the unpurified culture supernatants used previously . Aliquots of two of these monoclonal antibodies have been made available through the NIH AIDS Reagent Program as #12155 ( P3A3-A10 ) and 12156 ( P1H6-1 ) . Specificity was demonstrated by immunoblotting lysates from 293T cells transiently transfected with HA-tagged A3A , A3B , A3C , A3D , A3F , A3G , and A3H haplotype II ( Figure S1A ) . Epitopes were mapped by immunoblotting lysates from 293T cells transiently transfected with a panel of chimeric human/cow A3H/A3Z3 constructs ( Figure S1C ) . A polyclonal antibody raised against APOBEC3H residues 45–183 was used according to the manufacturer's instructions ( NBP1-91682 , Novus Biologicals ) . Cells were pelleted , washed , and then directly lysed in 2 . 5X Laemmli sample buffer . Virus containing supernatants were filtered and virus-like particles isolated by centrifugation through a 20% sucrose cushion and resuspended in 2 . 5X Laemmli sample buffer . Lysates were subjected to SDS-PAGE and protein transfer to PVDF using the Criterion system ( Bio-Rad ) . Isolation of total RNA , reverse transcription , and qPCR were performed as described [9] . Briefly , total RNA was isolated from cells using the RNeasy kit ( Qiagen ) . 1 µg of RNA was used to generate cDNA with Transcriptor reverse transcriptase ( Roche ) according to the manufacturer's instructions using random hexameric primers . Quantitative PCR was performed using a LightCycler480 instrument ( Roche ) . All reactions were done in triplicate and A3 levels were normalized to the housekeeping gene Tata Binding Protein ( TBP ) . The procedures to adapt viruses in culture to an APOBEC3 challenge are based on prior reports [54] , [55] . HIV-1 IIIB Vif N48H was generated as described above . The selection experiments were initiated at a MOI of 0 . 05 by infecting 150 , 000 cells , 100% of which were SupT11 expressing low levels of A3H haplotype II in a total volume of 1 ml in one well of a 24 well plate . Infections were monitored by removing supernatants and infecting the reporter cell line CEM-GFP every 3–4 days . The infected cells were split and the media was replenished at 4 days post-infection to prevent overgrowth of the culture . On day 8 of the infection , 250 µl of the virus containing supernatant from the infected wells was passaged to 150 , 000 SupT11 cells expressing higher levels of A3H haplotype II ( 50% low/50% intermediate ) . The virus supernatants were passaged in a stepwise manner 4 additional times into cultures with increasing levels of A3H haplotype II expression: 1∶2∶1 low∶intermediate∶high; 1∶1 int∶high; 1∶3 int∶high; 100% high . To purify emerging adapted viruses , a culture of 100% SupT11 expressing high levels of A3H haplotype II was infected at a MOI of 0 . 05 an additional 3 times . Finally , viruses were used to infect CEM-GFP and genomic DNA was isolated using the Puregene reagents ( Qiagen ) . Proviral DNA encoding a 1287-bp fragment of pol-vif-vpr amplified with primers RSH1438 5′CCC-TAC-AAT-CCC-CAA-AGT-CA and RSH1454 5′ CAA-ACT-TGG-CAA-TGA-AAG-CA . Amplicons were cloned into CloneJet ( ThermoScientific ) and Sanger sequenced using flanking plasmid-specific primers . Vif-proficient and Vif-deficient HIV-1 spreading infections were performed as previously described [54] . Viruses were generated by transfecting 10 µg of proviral expression construct into 293T cells . Titers of the viruses were assessed using the CEM-GFP reporter cell line . SupT11 spreading infections were initiated at a 0 . 01 MOI and the infection was monitored every 2–3 days using the CEM-GFP reporter line [56] . Primary CD4+ T cells were infected at a 0 . 02 MOI and culture supernatants were collected every 2–3 days and analyzed using an in-house p24 ELISA . Supernatants from infected cultures were incubated with for 1 hr on anti-p24 mAb ( 183-H12-5C , NIH ARRRP ) coated 96-well plates ( Nunc ) . Following 4 washes with PBS 0 . 1% Tween 20 ( PBS-T ) , a second 1 hr incubation anti-p24 mAb ( 9725 ) was used to ‘sandwich’ the p24 antigen . Wells were again washed 4 times , p24 was quantified by 0 . 5 hr incubation with an enzyme-linked secondary goat anti-mouse IgG-2A/HRP followed by 4 PBS-T wash steps and incubation with 3 , 3′ , 5 , 5′ tetramethybenzidine ( TMB ) for 6 min . The reaction was stopped upon addition of 1 M H2SO4 and absorbance at 450 nm was quantified on a microplate reader ( Synergy MX , Biotek ) . Peripheral blood mononuclear cells were isolated from whole blood ( Memorial Blood Center , St . Paul , MN ) by ficoll gradient centrifugation as previously described [7] , [9] . Naïve CD4+ T lymphocytes were purified by negative selection according to the manufacturer's instructions ( Miltenyi Biotech ) . Cells were stimulated and maintained in RPMI with 20 U/mL human interleukin-2 ( Miltenyi Biotech ) and 10 µg/ml phytohemagglutinin ( Thermo Scientific ) . Cells were stimulated for 72 hours prior to infection or harvesting for total RNA and protein lysate . Purity ( >95% ) and activation were confirmed by staining with an anti-CD4 antibody or anti-CD25 antibody respectively ( Miltenyi Biotech ) . This study has been reviewed and exempted by the University of Minnesota Institutional Review Board ( #0503E68687 ) . All PBMCs were isolated from healthy and de-identified individuals . Primers and PCR conditions for polymorphisms ( N15/ΔN15A3H , rs140936762; R18/L18 , rs139293; G105/R105 , rs139297; D121/K121 , rs139299 & rs139298; E178/D178 , rs139302 ) genotyping have been described [36] . Amplicons were cloned into CloneJet ( ThermoScientific ) and Sanger sequenced . When possible , linkage of heterozygous polymorphic sites was established by Sanger sequencing of amplicons from cDNA following reverse transcription of total RNA . Healthy donors were assessed for A3H haplotype by preliminary screening of PBMCs by immunoblot for A3H protein expression and a diagnostic PCR for the N15/ΔN15 polymorphism . CD4+ T cells were isolated from the selected donors ( Table S1 ) as described above . To assess global hypermutation semi-quantitatively , 3D-PCR was performed as described [7] . 15 days postinfection virus containing supernatants were used to infect 25 , 000 CEM-GFP cells . 48 hours later , genomic DNA was isolated ( Qiagen ) and an 876 bp region of pol was amplified from proviral DNA . The relative amount of this amplicon in each sample was assessed with qPCR ( Roche ) . Normalized amounts of proviral DNA was then used to generate a smaller 564 bp inner amplicon over a range of denaturation temperatures ( 77 . 0–86 . 2°C ) . The resulting PCR products were fractionated using agarose gels and visualized following ethidium bromide staining . Hypermutation spectra analysis was performed as described [7] , [46] . The 876 bp outer amplicon generated from proviral pol DNA was used to seed a second , inner PCR reaction with a uniform denaturation temperature of 98°C . The resulting 564 bp amplicon was cloned into CloneJet ( ThermoScientific ) . A total of 10 independent clones ( >5 kb ) were Sanger sequenced for each condition . Clones with identical mutations were eliminated . To test for an association between Vif and the A3H haplotype , the relative risk for having the Vif F39 genotype as it depends on the stable versus unstable A3H haplotype was estimated using the data in Tables S2 and S3 , focusing on Asia and Africa and ignoring subjects without these genotypes ( i . e . , the “Other” category in Table S2 ) . As each of these tables only has data for subjects on 2 of the 3 variables ( i . e . , either geographic location and A3H or geographic location and Vif genotypes ) , a data augmentation strategy was employed to estimate the association of interest . The data from both tables were modeled as partially observed multinomial observations with 8 possible outcomes ( since there are 3 dichotomous variables ) . The unknown parameters in this model are the collection of 8 probabilities that determine which outcome was observed and the missing genotype values for either A3H or Vif . Inference was conducted using the Gibbs sampler via a custom C++ program . Ten thousand samples were generated by the Markov chain , the first half of the samples were discarded and every tenth sample was saved to conduct inference . A vague prior distribution was employed for the class probabilities ( a Dirichlet prior with all parameters in the prior set to 1 ) . From the set of sampled class probabilities one can obtain the marginal probabilities by summing over the margins and these can be used to calculate the relative risk and obtain a 95% confidence interval for the relative risk . This interval was ( 1 . 7 , 2 . 4 ) with a median value of 2 indicating that the probability of having Vif F39 , an A3H resistance residue , is twice as likely if one has the stable A3H haplotype . This relative risk was never less than 1 in all samples , leading to the conclusion that the probability that relative risk is less than 1 is <0 . 001 .
|
The APOBEC3 enzymes protect cells by inhibiting the spread of retroelements , including HIV-1 , by blocking reverse transcription and mutating cytosines in single-stranded DNA replication intermediates . HIV-1 Vif counteracts restriction by marking APOBEC3 proteins for proteasomal degradation . APOBEC3H is the most diverse member of this protein family . Humans have seven distinct APOBEC3H haplotypes with three producing stable and four producing unstable proteins upon forced overexpression . Here , we examine the stability phenotype of endogenous APOBEC3H in donors with different haplotypes and address how these stability differences , as well as natural viral diversity , combine to determine HIV-1 infectivity . We found that endogenous APOBEC3H haplotypes yield stable or unstable proteins and that stable APOBEC3H is induced during viral infection and restricts the replication of isolates with naturally occurring hypo-functional but not hyper-functional Vif alleles . We also found that the global distribution of stable APOBEC3H alleles correlates with the prevalence of HIV-1 Vif alleles capable of mediating its degradation , strongly suggesting that the viral Vif protein is capable of adapting to the APOBEC3H restriction potential of an infected individual . Thus , the combination of human APOBEC3H haplotypes and virus Vif alleles may help account for some of the observed disparities in disease progression and virus transmission .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"organismal",
"evolution",
"innate",
"immune",
"system",
"viral",
"immune",
"evasion",
"viral",
"genes",
"microbial",
"evolution",
"virology",
"biology",
"and",
"life",
"sciences",
"immunology",
"microbiology",
"evolutionary",
"biology",
"viral",
"evolution",
"immune",
"system"
] |
2014
|
Natural Polymorphisms in Human APOBEC3H and HIV-1 Vif Combine in Primary T Lymphocytes to Affect Viral G-to-A Mutation Levels and Infectivity
|
A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia . Medium spiny neurons ( MSNs ) in the striatum , receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors , respectively . Recently , it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs . Therefore , to understand striatal function in action selection , it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity . Here , we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs . We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device , indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs . Next , we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons , by the dopamine level , and by the activity of the GPe via pallidostriatal backprojections . We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway . This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease , including increased reaction times , L-Dopa-induced dyskinesia , and deep brain stimulation-induced impulsivity .
The basal ganglia ( BG ) are a set of nuclei , located at the base of the forebrain , which play a crucial role in a variety of motor and cognitive functions . The striatum is the main input stage of the basal ganglia , receiving inputs from widely distributed areas in the cortex [1] , and projecting to the BG output nuclei Globus Pallidus Interna ( GPi ) and Substantia Nigra ( SNr ) via the so-called direct and indirect pathways , respectively [2] . The integration of multi-modal sensory signals with motor and/or cognitive inputs in the striatum sets the stage for action selection . Therefore , to understand the computations performed by the basal ganglia , it is of key importance to characterize the dynamical properties of striatal network activity . Nearly 95% of the neurons in the striatum are inhibitory medium spiny neurons ( MSNs ) . The remaining 5% are inhibitory interneurons , such as fast spiking interneurons ( FSIs ) and tonically active neurons ( TANs ) [3] . The MSNs are classified as D1 and D2 type neurons , depending on their dopamine receptor expression . Interestingly , D1 MSNs project to the GPi and SNr , forming the direct pathway , whereas D2 neurons project to Globus Pallidus Externa ( GPe ) , forming the indirect pathway [2] . Consistent with basal ganglia anatomy and previous models [4–6] , selective activation of D1 MSNs in the rat increases ambulation , whereas selective activation of D2 MSNs increases freezing behavior [7] . However , a complete shutdown of activity in either of the two neuron subpopulations might not occur in awake behaving animals [8] . In such a scenario , though , action selection could still be performed by a relative increase in the activity of one subpopulation compared to the other . Thus far , in computational models of the interactions between direct and indirect pathways [4–6 , 9] , D1 and D2 MSNs have been considered as interchangeable inhibitory neuron subpopulations . In such single population models , the striatal output is controlled by the strength of cortico-striatal synaptic weights . The recurrent inhibition is not strong enough to support winner-take-all dynamics as earlier speculated [10] , but may be sufficient to allow for a winner-less competition [11] and may enhance the saliency of the cortical input representation in the striatum [12] . This , however , is a highly simplistic view of the recurrent inhibition within the striatum and , as will be described below , is inconsistent with experimental data , especially given the recent findings on the recurrent connectivity in the striatum . Recent experiments have shown that D1 and D2 MSNs have quite different anatomical and electrophysiological properties [13] . Moreover , the striatal circuit also shows a highly specific connectivity in terms of the mutual inhibition between the MSN subpopulations [14 , 15] and the feedforward inhibition from FSIs [16] . Paired neuron recordings showed that D2 MSNs make more and stronger connections to D1 MSNs , than vice versa . Furthermore , FSIs preferentially innervate D1 MSNs as compared to D2 MSNs ( Fig 1A ) . The computational role of this specific connectivity within the striatum is not clear and cannot be inferred from previous models , which assumed a single homogeneously connected MSN population in the striatum . Specifically , it is important to identify the effect of the distinct D1 and D2 connectivities on the competition between the direct and indirect pathways . Here we describe the effect of the heterogenous connectivity of D1 and D2 neurons on their mutual interactions using both a reduced firing rate model and numerical simulations of a spiking striatal network model . We show that the firing rates of both D1 and D2 MSNs change in a non-monotonic manner in response to cortical input rates and correlations . Interestingly , higher output rates in D1 than in D2 MSNs , and vice versa , were observed for separate , non-overlapping ranges of cortical input rate and correlation . Correlations in the input can further change the range of cortical inputs for which either D1 or D2 MSNs have the higher firing rate . Thus , we argue that the striatum acts as a threshold device for cortical input rates by changing the magnitude of the difference between firing rates of D1 and D2 MSNs , depending on the level of cortical input rates and correlations . While the main determinant of the striatal threshold is the asymmetric connectivity among the various striatal elements , the threshold is not fixed and can be dynamically adjusted by the dopamine level , by the connectivity and firing rate of fast spiking interneurons ( FSI ) , and by the GPe activity . That is , changes in the striatal threshold could reflect changes in the operating point of the striatum , behavioral context , and learning and reward history . These novel insights concerning the interactions between direct and indirect pathways suggest putative mechanistic explanations for the role of striatum in cognitive deficits such as L-Dopa-induced Dyskinesia ( LID ) , deep brain stimulation ( DBS ) -induced impulsivity and increased reaction times in Parkinson’s disease ( PD ) patients .
Experimental measurements of the mutual connectivity between striatal neurons show that D2 MSNs make more and stronger inhibitory connections on D1 MSNs than vice versa [14 , 15] . In Lhx6-GFP transgenic mice , FSIs preferentially target D1 MSNs as compared to D2 MSNs [16] ( however cf . [15] ) . This leads to the following two inequalities: J12 > J21 , J1F ≥ J2F , with J12 denoting the connection from D2 MSN to D1 MSN , J21 the obverse , J1F the connection from FSI to D1 MSN and J2F likewise to D2 MSN ( cf . Table 1 ) . These inequalities imply that if the two MSN subpopulations receive the same amount of excitatory input , D2 MSNs will always have a higher firing rate . We confirmed this by evaluating the fixed points of the linearized dynamics of the D1 and D2 MSNs in a mean field model ( Eqs 25–27 , Fig 1B—grey and black traces ) . In our spiking network simulations this corresponded to a lower mean firing rate of the D1 population compared to the D2 population . This bias might be considered functionally useful , because for similar input strengths ( i . e . when the cortex does not impose any preference for either direct or indirect pathways ) , the indirect pathway will dominate the striatal network dynamics and the ‘No-Go’ would be the default state of the basal ganglia . Thus , in order for D1 MSN activity ( λD1 ) to exceed D2 MSN activity ( λD2 ) , D1 MSNs must receive either stronger ( JC1 > JC2 ) , higher excitatory inputs ( λctx_d1 > λctx_d2 ) , or more excitatory synapses . Alternatively , D1 MSNs could be more excitable . Indeed , D1 MSNs have more primary dendrites and a larger dendritic arborization [13] , thereby potentially receiving more inputs . Moreover , pyramidal tract neurons have stronger connections to D1 MSNs than to D2 MSNs , while intratelencephalic tract neurons innervate D1 and D2 MSNs equally . [1] . Despite this evidence , current data are not sufficient to determine the exact strength and rate of excitatory inputs to D1 and D2 MSNs . To estimate how much additional excitation would be required for D1 MSNs to have their firing rates exceed over those of D2 MSNs , we systematically varied the drive of cortical inputs to D1 and D2 MSNs and calculated the response firing rates of the two subpopulations , for the firing rate model ( Fig 2 ) . In the following we discuss two different scenarios for systematically varying the cortical excitatory drive to D1 and D2 MSNs . To obtain further insight into the activity balance of D1 and D2 MSNs , we considered a state of the striatum in which the cortico-striatal projections had been strengthened for a particular ‘Go’ task ( JC1 > JC2 , cf . the ‘multiplicative scenario’ for detail ) . This scenario is shown for the firing rate model in Fig 1B and for the spiking neural network in Fig 4 ) . In this state , we estimated the difference between D1 and D2 MSN output rates as a function of cortical input rate . For a constant difference in synaptic strength to D1 and D2 MSNs ( ΔJ = JC1 − JC2 = const ) , the difference between the output firing rates of D1 and D2 MSNs ( ΔMSN ) changes from positive to negative as the cortical input rate increases ( Figs 1B and 4 ) . That is , the response of the striatum to a range of cortical input rates can be divided into two regimes: a regime where D1 firing rate exceeds D2 firing rate and a regime where this situation is reversed . At the transition point λ C T X t r a n , the D1 and D2 firing rates are equal . This behavior can be interpreted as a striatal bias towards the ‘Go’ pathway below a certain cortical input rate ( λ C T X < λ C T X t r a n ) , switching to a bias towards the ‘No-Go’ pathway for higher cortical input rates ( λ C T X > λ C T X t r a n ) ( cf . Figs 4A and 4B and 1B ) . The cortical input rate ( for a constant difference ΔCTX ) at which ΔMSN changes its sign , switching the striatum from a putative ‘Go’ to ‘No-Go’ mode , can be referred to as decision transition threshold ( DTT ) . That is , the striatum acts as a threshold device , indicating the change in the cortical input rates by the sign of the difference between the firing rates of D1 and D2 MSNs—higher D1 activity reflecting lower cortical input rate and higher D2 activity reflecting higher cortical input rate . Therefore , we propose that the asymmetric striatal connectivity allows it to ‘sense’ small changes in the cortical activity reaching the striatum and to modulate the balance between ‘Go’ and ‘No-Go’ states of the basal ganglia accordingly . Here we considered the scenario in which the sign of ΔMSN changes from positive to negative , i . e . going from ‘Go’ to ‘NoGo’ bias . It is possible to tune the strength of FSI inputs such that initially the striatum is biased towards the ‘NoGo’ pathway and a subsequent increase in the cortical input changes the bias to the ‘Go’ pathway ( cf . Fig 3A and 3D ) . Previously , Lo and Wang [18] described the cortico-basal ganglia loop as a DTT for reaction time tasks . However , in their model the striatum was considered a passive unit , the threshold of which could be tuned by the strength of cortico-striatal synapses . In our model , for the first time , we describe the striatum as an active participant in deciding the level of DTT . It should however be noted , that our model explores the DTT under steady state conditions only . In this regime , the striatal network settles into stable fixed points ( cf . Materials and Methods ) and , therefore , the DTT is a stable network state as well . A DTT might also emerge as a transient state in the network dynamics in the presence of additional mechanisms like short term and/or long term plasticity of cortico-striatal and/or striato-striatal synapses and intrinsic plasticity of striatal neurons . Further exploration of such mechanisms is , however , beyond the scope of the present work . The presence of such dynamic DTT suggests several possible control mechanisms , that may set the bias towards either of the two pathways along the basal ganglia downstream nuclei , depending on the animal behavioral state , motivation and acquired learning . In the following sections , we describe three possible mechanisms that can modify the balance of D1 and D2 activity by modulating the level of the DTT , thereby rendering it as a dynamic variable . In a standard feedforward model of the basal ganglia , the GPi activity can indicate the higher activity of D1 ( D2 ) MSNs by decreasing ( increasing ) its activity around the baseline . However , recent data shows that the basal ganglia network is more complicated than the simple feedforward model ( cf . review by [19] ) . In future it would be important to show whether GPi can change its activity according to the D1 and D2 MSNs firing rate differences , in a more updated model of the basal ganglia . In the above , the DTT was demonstrated assuming that the firing rate of the cortical input and the difference between the input to the D1 and D2 MSNs ( ΔCTX ) remaines constant , but in more realistic conditions the input rate and ΔCTX could both be random variables . Therefore , we tested the viability of the concept of DTT when the mean firing rate of the cortical inputs to the D1 and D2 MSNs was chosen from a low-pass filtered noise . In addition , the extra input to the D1 MSNs was chosen from a low-pass filtered noise . To quantify the DTT we measured ΔMSN in pre-DTT and post-DTT regions ( Supplementary S1 Fig ) . It is obvious that when ΔCTX is noisy the difference between the firing rate of the D1 and D2 MSNs around the DTT decreases , however ΔMSN in the pre-DTT and post-DTT remains large enough to be reliably measured ( Supplementary S1 Fig ) . When the standard deviation is increased upto 50% of the mean value of the ΔCTX , both pre-DTT and post-DTT areas decreased only by 20% ( Supplementary S1 Fig ) . Correlations in the spiking is another way of generating transient fluctuations in the input to the D1 and D2 MSNs and the effect of correlation induced fluctuations is described below ( cf . subsection “Effect of cortical spiking activity correlations on the DTT” ) In addition to the input rate fluctuations , the connectivity between the D1 and D2 MSNs could also be different from what we have previously used . Therefore , we tested the robustness of the DTT for variation in the chosen values of striatal network connectivity ( e . g . J11 , J12 etc . ) . There are up to eight network connectivity parameters ( cortical input to the two MSNs populations , mutual and within MSNs connectivity and MSN and FSI connectivity ) which could potentially affect the existence of the DTT . We systematically varied all the parameters ( except the FSI connectivity to the MSNs ) by ±100% around their mean values ( as used throughout this manuscript ) . Visualisation of this high dimensional parameter space is difficult . Therefore , here we checked if the DTT exists when only one of the parameters is varied systematically by ±100% of its mean value , while other parameters can take any value between ±100% of their mean value . A DTT is said to exist if ΔMSN for increasing values of λctx and the given parameter combination changes sign from positive to negative values exactly once . This was detected algorithmically as well as by visual inspection . Indeed , the DTT is a robust to such changes in the network connectivity and for every parameter we tested here ( cortical inputs to the MSNs and within and mutual connectivity of MSNs ) , we can find the DTT ( cf . Supplementary S2A–S2F Fig ) . Only for very small values of the within connectivity of the D1 and D2 MSNs ( J11 and J22 ) , we could not find a solution for the DTT . In addition , we also checked the existence of the DTT for various pairs of the network connectivity parameters ( e . g . J21 vs . J11 ) . While there are some forbidden regions in which DTT does not exist , for a large variations in the striatal connectivity , the DTT can be observed ( Supplementary S2 Fig ) . When the existence is visualised as a scatter diagram as a function of a pair of the network connectivity parameters , the mean of these clusters lie very close to the values we have used throughout this manuscript . Task related activity in the cortex is modulated both in firing rates and in spike correlations [20] . Here we investigated how spike correlations in the cortical inputs to the striatum may affect the balance between D1 and D2 MSNs . Injection of correlated inputs to a population of MSNs requires the separation of input correlations into two categories [12]: correlations arising due to convergence of cortical projections onto individual neurons , i . e correlations within the pool of pre-synaptic neurons seen by individual MSNs ( in the following referred to as ‘within’-correlations—W ) and correlations arising due to divergence of cortical projections , i . e . correlations between the pools as seen by different MSNs , the so-called shared input ( in the following referred to as ‘between’ correlations—B ) ( cf . Materials and Methods and Fig 5A ) . Note that because both B and W correlations arise from convergent and divergent projections of the same pre-synaptic population , between correlations are always smaller than or equal to within correlations ( B ≤ W [21 , 22] , cf . Materials and Methods ) . To understand the effect of input correlations on the balance of D1 and D2 MSN activity , we independently varied both within and between input correlations while maintaining a constant input rate ( MIP model in [23] ) . These simulations were only performed for the spiking neural network model since modelling correlations in a mean field model is non-trivial , especially when post-synaptic neurons are recurrently connected . ( cf . Materials and Methods , Fig 5A ) . As expected , within-correlations affected the individual firing rates in a non-monotonic manner [23] for both D1 and D2 MSNs . For weak input correlations , as D1 MSNs form stronger synapses with cortical afferents , ( JC1 > JC2; scenario II ) , D1 MSNs spike at higher rate than D2 MSNs ( Fig 5B ) . For high values of W the MSNs operated in a so called ‘spike-wasting’ regime , because the average count of coincident spikes ( or the input variance ) exceeded the spike threshold [23] and therefore , W caused no difference in the output firing rates of D1 and D2 MSNs ( Fig 5C ) . That is , the difference in the firing rates of D1 and D2 MSNs ( ΔMSN ) changed non-monotonically as a function of W , reaching its maximal values for W = Wopt ≠ 0 ( Fig 6A and 6B ) . The effect of W on ΔMSN was further modulated by the between-correlations ( B ) . For W < Wopt , increasing B resulted in a monotonic increase in ΔMSN . In this regime , B increased the correlation within D1 and D2 MSNs and , thereby , increased the effective inhibition of one population by the other , and vice versa . Because D1 MSNs received input with a slightly higher strength ( JC1 > JC2; scenario II ) , B induced more synchrony among D1MSNs and , hence , they were better able to inhibit the D2 MSNs ( Fig 5E ) . However , this held only over a smaller parameter regime , because by design B cannot exceed W ( cf . Materials and Methods ) . For W ≥ Wopt , ΔMSN decreased monotonically with increasing B . In this regime , an increase in B enhanced the correlations within D1 and D2 MSNs , hence amounting to a ‘wasting of recurrent inhibition’ [12] . Increased correlations within D1 and D2 MSNs created strong but only short lasting inhibition leaving out longer lasting time windows without any recurrent inhibition ( Fig 5D ) . In the absence of sufficiently long-lasting recurrent inhibition the difference ΔMSN monotonically decreased ( Fig 6A and 6B ) . Previously , we have shown that shared inputs ( B > 0 ) reduce the signal-to-noise ratio and the contrast enhancement in the striatum [12] . Our results here now show that the ‘between’-correlations could play an even more important role in initiating the action-selection process in the striatum . However , whether B correlations improve or impair action selection strongly depends on the ‘within’-correlations . ( Fig 6 ) . With an increase in the cortical input rate , the amount of inhibition experienced by a MSN in the striatal network also increases due to the increase in MSN firing rates . This requires more coincident excitatory inputs ( i . e . higher W ) to overcome the increased inhibition and to drive the output neurons above firing threshold . Thus , Wopt increases with an increase in input rates ( compare Fig 6A and 6B ) . That is , an increase in cortical input rates broadens the dynamic range of the interaction between B and W by increasing Wopt ( Fig 6C , also discussed in [23] ) . These results show that the striatum also acts as a threshold device for cortical input correlations , in the same way we have shown above for cortical input rates . To illustrate this , we plotted ΔMSN as a function of the cortical input rates and within correlations ( W ) for a fixed value of B ( Fig 6C , cf . Supplementary S4 Fig ) . Here , we can define three regions in the space of input firing rates and input correlations for which ΔMSN is positive , negative and zero respectively ( Fig 6C ) , reflecting how the striatum will react to the first and second order statistics of the cortical inputs by activating the direct , the indirect or none of the two pathways . While the direct and indirect pathways reflect the striatal preference for a ‘Go’ and ‘No-Go’ action , respectively , ΔMSN ≈ 0 could reflect a state of high conflict where it is useful to halt the decision making process until more information is available . MSNs express D1 and D2 types dopamine receptors forming the direct and indirect pathways of the BG , respectively . Dopamine affects striatal function by modulating the intrinsic excitability of the MSNs and synaptic weights [24] and synaptic plasticity [25 , 26] of the cortico-striatal projections . Because we are interested in how striatum processes cortical inputs , here we studied the effect of dopamine-induced modulation of the strength of cortico-striatal projections . Dopamine induces long term potentiation ( LTP ) in cortico-striatal synapses onto D1 MSNs and long term depression ( LTD ) in cortico-striatal synapses onto D2 MSNs [24] . This steady state effect of dopamine is often modelled by changing the weight of cortico-striatal synapses for D1 ( JC1 ) and for D2 ( JC2 ) [27] . Therefore , in our model we simulated the effect of dopamine depletion by decreasing JC1 and increasing JC2 , whereas higher than normal dopamine was simulated by increasing JC1 and decreasing JC2 . Thus , in a low dopamine state ( e . g . in PD patients ) , D2 MSNs received much stronger cortical input and , therefore , exerted more inhibition on to the D1 MSNs . This shifted the DTT towards lower cortical input rates ( Fig 7A ) reducing the regime under which the direct pathway could dominate the indirect pathway . Consistent with experimental observations and previous models [4 , 5] , our model suggests that dopamine depletion would ( 1 ) increase the firing rate of D2 MSNs , thereby ( 2 ) introducing a preference for ‘No-Go’ type actions . By contrast , in a high dopamine state ( e . g . as in PD patients with L-Dopa treatment ) D1 MSNs received much stronger cortical input and were able to maintain higher output firing rate than D2 MSNs for a wider range of cortical inputs . That is , high dopamine shifted the DTT towards higher cortical inputs ( Fig 7A ) and increased the regime under which the direct pathway could dominate the indirect pathway . Consistent with experimental observations , our model suggests that an increase in steady state dopamine levels would ( 1 ) increase the firing rate of D1 MSNs [28] , thereby , ( 2 ) introducing a preference for ‘Go’ type actions or dyskinesia . In summary , therefore , synaptic effects of dopamine can be seen as a change in the DTT of the striatum . Approximately 1% of the striatal neuronal population is composed of parvalbumin expressing fast spiking interneurons ( FSIs ) [29] . Despite being only a small portion of the neuronal population in the striatum , they can strongly modulate MSNs activity due to their high degree of convergent and divergent connections to the MSNs . Moreover , they form synaptic connections near or at MSN’s somata as opposed to glutamergic synapses or local MSN axon collaterals , which usually connect to spines or dendritic shafts [30] . Furthermore , recent data suggests that FSIs preferentially inhibit direct pathway MSNs ( D1 ) as compared to indirect pathway MSNs ( D2 ) [16] . This implies that the activity of FSIs could alter the balance between the direct and indirect pathways and may even change the DTT . For instance , Fig 4A showed that for small ΔCTX , D1 MSNs fire at higher rate than D2 MSNs at low inputs , however an increase in FSI firing rate would inhibit D1 MSNs more than D2 MSNs and , hence , reduce the DTT . By contrast , a reduction in the firing rate of FSIs will have an opposite effect . Thus , FSIs could play a vital role in maintaining the default state of the striatal circuit as a ‘No-Go’ state , which could be changed to a ‘Go’ state by exciting D1 MSNs more than D2 MSNs . Alternatively , a depletion of FSIs ( e . g . as seen in patients with Tourette syndrome ) , a reduction in their firing rate or connectivity should reverse this scenario and enforce a ‘Go’ state as the default striatum state leading to impulsivity-related behavioral symptoms . Besides cortical inputs , other factors such as back projections from the GPe could also modulate the effect of FSIs on MSNs by modulating their firing rates ( Fig 7B ) . The effect of GPe on FSIs was modelled in our network model as a constant amount of inhibition on FSI firing rates . An increase in GPe activity ( for instance due to an increase in STN activity ) would effectively release D1 and D2 MSNs from the feedforward inhibition . However , the effect will be further amplified for D1 MSNs because these neurons receive more input from FSIs than D2 MSNs do . This can shift the bias from a ‘No-Go’ to ‘Go’ state for high cortical input rate ( as shown in Fig 7B , dotted lines , for λCTX in the range ( 10–15 Hz ) ) . The same effect of FSI activity can be observed in the Fig 3D . The same effect is shown for mean field model in Supplementary S5 Fig . The temporal dynamics of such a modulation by GPe are shown in Supplementary S6 Fig for a mean field model . Thus , an increase in the activity of the FSIs would result in shifting the DTT to higher cortical rates . We argue that increased firing rates in STN might not only maintain a tight rein on GPi to prevent a premature response in a high conflict decision , as suggested by [5] , but also assist in resolving the conflict in the striatum via pallido-striatal back projections . In the dopamine depleted condition when the DTT is very small , the effect of the GPe back projection might not be able to modulate the DTT any further ( Fig 7C ) . Similarly , tonically active interneuron ( TANs ) could exploit the FSI to D1 and D2 MSNs connectivities by modulating the strength of FSI synapses onto the respective MSNs [31] , thereby , changing the DTT indirectly . Our model is based on in vitro results from Taverna et al . [14] and Gittis et al . [16] . Taverna et al . [14] showed that D2 MSNs inhibit D1 MSNs more strongly in terms of numbers of projections and synaptic strengths as compared to vice versa , whereas Gittis et al . [16] showed that in the Lhx6-GFP transgenic mice , FSI has more projections to D1 than to D2 , while the synaptic strengths of the projections to both MSN types are comparable . While it is unlikely that the preferential connectivity of FSIs to D1 MSNs is specific to the mouse strain ( A . Gittis personal communication ) , another recent study by Planert et al . [15] , while supporting the results of Taverna et al . [14] , did not report any significant preference in the connectivity of FSIs to D1 MSNs . Thus , even though Planert et al . drew their conclusion based on only a small number of neurons , the apparent inconsistency of the data in the two studies requires a reevaluation of the connectivity of FSIs to MSNs . We , therefore , analysed the striatal dynamics for equal innervation of MSNs by FSIs .
Spike correlations in cortical input to the striatum influence the DTT in a complex fashion . To better understand the effect of input correlations we separately studied the role of correlations within and correlations between the pre-synaptic pools of the MSNs . Previously , we have shown that correlations between the pre-synaptic pools of MSNs ( B ) tend to reduce the signal-to-noise ratio of the striatal response to cortical inputs [12] . On the other hand correlations within the pre-synaptic pools of individual MSNs ( W ) affect the signal-to-noise ratio of the striatal response in a non-monotonic manner . Here , we found that W correlations also affect the difference between D1 and D2 MSNs activity ( ΔMSN ) in a non-monotonic fashion ( Fig 6 ) . Interestingly , when input firing rates result in equal output firing rates of D1 and D2 MSNs , a modulation in W could break the symmetry and increase the firing rate of either one of the two MSN subpopulations . The value of within-correlations ( Wopt ) for which ΔMSN is maximal depends on the input firing rates and the value of between-correlations ( B ) ( Fig 6 ) . The effect of between-correlations ( or shared inputs ) depends on the value of the within-correlations . In the regime W ≥ Wopt , an increase in shared input ( B ) decreases the value of ΔMSN . However , in the regime W ≤ Wopt , an increase in B increases the value of ΔMSN . Finally , for high values of input correlation ∣ΔMSN∣ is small and the two MSN populations have comparable firing rates , indicating the failure of the striatum to bias an action-selection process . Such an operating regime could halt the decision making process in high-conflict decision-making tasks . Indeed , we predict that in high conflict choice tasks , there should be an increase of correlation in the cortical activity reaching the striatum . The preferential connectivity of the FSIs to D1 MSNs gives the FSIs , an ability to control both the difference in the firing rate of the D1 and D2 MSNs and , hence , the DTT . For instance , an increase in FSI activity would reduce D1 MSNs activity and decrease the DTT whereas a decrease in FSI activity would tip the bias in favour of D1 , and hence the ‘Go’ pathway . Such a decrease in FSI activity could be due to insufficient excitation from the cortex , or to high input from the pallido-striatal back backprojections . We suggest that selective modulation of the FSI activity , which could shift the DTT in the striatum from ‘No-Go’ to ‘Go’ , could be a mechanism for decision making in a high-conflict situation . This functional concept may also help understand neuronal mechanisms underlying various behavioral phenomena . For instance , the arbitration of FSI via the STN-GPe pathway could explain longer reaction times , required to respond to a high-conflict decision . Similarly , increased impulsivity in PD patients with DBS could be explained by the fact that DBS increases GPe firing rates [33] , which would decrease the activity of FSIs by pallido-striatal backprojections , resulting in lowering the DTT . Being the primary source of feedforward inhibition , FSIs play an important role in both the existence and the actual value of the DTT . For example , we find that a preferential inhibition of D1 MSNs is required for a DTT to be present for ‘multiplicative’ input . By contrast , an ‘additive’ scenario reveals a DTT , irrespective of the asymmetry of FSI projections onto MSNs . We showed that even in the ‘multiplicative’ input mode the striatum can exhibit a DTT , by clamping the FSIs rates , while increasing cortical input rates . We conclude that FSI activity plays a prominent role in determining the DTT in the striatum , underlining once more the functional importance of pallido-striatal backprojections to FSIs . At the synaptic level dopamine has been described to increase ( decrease ) the strength of cortico-striatal synapses onto D1 ( D2 ) MSNs . This differential effect of dopamine provides a powerful mechanism to control the DTT and difference between the activities of the two neuronal subpopulations activities and , hence , the DTT . For instance , a dopamine induced increase in synaptic inputs to D1 MSNs shifts the DTT in the favour of D1 MSNs , and thereby increasing the cortical input regime for a ‘Go’ bias . This , in fact , could be a putative mechanism by which external administration of L-Dopa induces dyskinesia as a prominent side-effect . By contrast , a reduction in dopamine level reduces the regime for a ‘Go’ bias , while increasing the regime for a ‘No-Go’ bias . Thus , low dopamine results in higher firing rates in D2 MSNs , possibly manifesting in akinetic symptoms and inducing oscillations in the GPe-STN network [34] . Recent experimental data suggest that the activation of ‘Go’ and ‘No-Go’ pathways might not be exclusive in vivo awake behaving animals . At least at the population level , both D1 and D2 MSNs have been shown to be co-activated and neither of the two pathways were completely shutdown [8] . Consistent with these experimental findings , both D1 and D2 MSNs in our model are co-activated , however , the two populations differed in their firing rates . We note that here our goal is not to reproduce the experimental results of Cui et al . 2013 [8] which involved behaving mice performing self-paced lever pressing to retrieve the reward . Apart from the obvious simplicity of our network model , we think there might be an additional reason for this . In our model , we considered the striatum as unit representing a single action . Indeed , Cui et al . 2013 [8] suggest that a non-selective global deactivation of D2 MSNs in the striatum would abolish suppression of most unwanted motor programs and , hence , lead to hyperkinesia . It can be argued that our model represents such a global activation and deactivation of D1 and D2 in the striatum . The concept of a DTT , if extended to include multiple action channels , could be used to more closely address the results reported in [8] . Thus far , the striatum has been considered as a homogenous structure , with a weak and homogeneous inter-connectivity . Anatomical evidence , however , suggests that there is an inherent asymmetry in the striatal circuit in terms of mutual connections between D1 and D2 MSNs , as well as in feedforward projections from FSIs . In Fig 2 , we showed that this asymmetry , for an appropriate activity regime , has the ability to reinforce the cortical bias ( bias towards D1 at low cortical rates ) or override it ( bias towards D2 at high cortical rates ) . Taking cortical input correlations into consideration , these dynamics become even more complex , as is summarized in Fig 9 . Low dopamine levels decrease the regime with D1 bias , while increasing the regime with D2 bias . Alternatively , an increase in dopamine levels increase the area for a D1 bias , while decreasing the area for a D2 bias . An increase in between-correlations in cortical input ( B ) , however , pushes the contour towards the top left corner in Fig 9 ( towards higher firing rates and lower values of within-correlation ( W ) ) , thereby enlarging the area of conflict ( RHC ) . We therefore suggest , that the asymmetry in the striatal circuit is useful to cover different regimes of D1 and D2 bias , accommodating characteristic features of cortical input , including rates , input correlations and local dopamine levels . Given the in vitro experimental conditions [14 , 15] , the measurement of the connectivities within and between D1 and D2 MSNs could be biased . In more realistic in vivo conditions , the connectivities of D1 and D2 MSNs may turn out to be different . In particular , if D1 and D2 MSNs would not differ in their connectivities and membrane properties , there is no reason to consider the striatum as a two population network and the role of action selection would need to be performed in the downstream nuclei . Such a single population striatum could initiate this process by ‘winner-less competition’ [11] or by enhancing the contrast of the input [12] . If the D1 and D2 MSNs would only differ in their membrane properties , then our analysis of asymmetric connectivity between the two neuron subpopulations would still be valid , because the effect of different neuron transfer functions could be reduced to a difference in effective connection strengths . In the extreme case , when D2 MSNs would receive more inhibition than D1 MSNs , our analysis remains valid , but we would have to reverse the interpretation . Many disorders in the basal ganglia are related to the imbalance of its functional pathways . PD is thought to be a manifestation of dopamine depletion in the form of a domination of indirect and hyperdirect pathways , while weakening the direct pathway . Dyskinesia , on the other hand , is correlated with hyperactivity of the direct pathway and reduced activity of the indirect pathway [35] . The Tourette syndrome is correlated with decreased activity of FSIs [36] . This ( dis ) balance of striatal activity can be systematically modeled using the concept of the DTT . In fact , our model provides multiple neuronal mechanisms and a mechanistic understanding of several brain disorders by linking their specific pathophysiology to the DTT . Specifically , we explored three factors that may influence the DTT . In summary our results show that the striatum , with its asymmetric connectivity among and between D1 and D2 MSNs , can act as a threshold device , indicating the increase in cortical input firing rates and correlations by increasing the relative firing rates of D1 and D2 MSNs . The DTT ( basically , a threshold on the difference between the two ) is a dynamic variable which may represent behavioral state , learning history and motivation level of the animal . Various mechanisms e . g . feedforward inhibition , dopamine , GPe backprojections , tonically active neurons exist that can modulate the DTT and thereby , provide the striatum with a rich computational repertoire . We arrived at this functional description of the striatum as a DTT by considering the striatal network dynamics emerging from including low-level properties such as synaptic weights and connection probabilities . Moreover , we included several factors that can modulate the DTT level by affecting chemical imbalances and changes in neuronal properties . Taken together , we provide a high-level functional model of the striatum , which can be easily linked to low-level properties . Typically , the striatum is included in large-scale functional models [5] , acting according to winner-take-all dynamics; an idea no longer supported by experiments [10] . Instead , we argue that our functional description of the striatum provides a more biologically realistic basis for large-scale functional models of basal ganglia function .
The mean population dynamics of the striatal circuit for scenario II were modelled using coupled non-linear differential equations: λ D 1 ˙ = - 0 . 01 λ D 1 + S ( J 11 λ D 1 + J 12 λ D 2 + J 1 F λ F S I + J C 1 λ C T X ) ( 21 ) λ D 2 ˙ = - 0 . 01 λ D 2 + S ( J 21 λ D 1 + J 22 λ D 2 + J 2 F λ F S I + J C 2 λ C T X ) ( 22 ) The values of parameters used in Eqs 21 and 22 are listed in the Table 1 . The non-linear transfer function of the neuron S ( z ) has a sigmoidal function of the form: S ( z ) = z z 2 + 1 ( 23 ) The parameters J11 , J12 , J21 , J22 are calculated by considering the effective input received by a neuron in the population . This can be quantified as: J x y = N y × ρ x y × I P S C x y × R x × τ x y ( 24 ) with x and y either 1 or 2 . These parameters are listed in Table 2 . This measure calculates the relative inhibitory strength of projections between D1 and D2: although the self connectivity of D1 is comparable to the number of projections it receives from D2 ( ρ11 = 0 . 26 , ρ12 = 0 . 27 ) , since D2 makes stronger inhibitory connections than D1 , J12 is larger than J11 . The striatal network model was based on the spiking network model of the striatum as described in [12] , except that the network connectivity was not considered to be homogeneous as in [12] . The simulations were carried out in NEST [43] with networks of 4000 MSNs ( 2000 D1 , 2000 D2 ) and 80 Fast Spiking Interneurons ( FSI ) , consistent with the estimated ratio of MSN and FSI neurons [44 , 45] . The neuron model parameters for FSIs and MSNs are listed in Table 3 . D1 MSNs , D2 MSNs and FSIs received independent excitatory Poisson inputs , mimicking the background cortico-striatal inputs . The cortical input parameters are summarized in Table 4 . Connection probabilities for D1 MSNs , D2 MSNs and FSIs were taken from [14 , 16] , also listed in Table 5 . The leaky-integrate-and-fire ( LIF ) neuron model was used to simulate the neurons in the network with the subthreshold dynamics of the membrane potential Vx ( t ) described by: C x V ˙ x ( t ) + G x [ V x ( t ) - V r e s t x ] = I s y n ( t ) ( 28 ) I s y n ( t ) = I D 1 ( t ) + I D 2 ( t ) + I F S I ( t ) + I C t x ( t ) ( 29 ) where x ∈ D1 , D2 , FSI . In the above equations , Isyn ( t ) describes the total cortical excitatory , recurrent and feedforward inhibition to a neuron , Cx is the membrane capacitance , Gx is the leak conductance , and Vrest is the resting membrane potential . When the membrane potential of the neuron reached Vth , a spike was elicited and the membrane potential was reset to Vrest for a refractory duration ( tref = 2 ms . ) An alpha function was used to model the excitatory synaptic input received by D1 , D2 and FSI: g e x c x ( t ) = { J e x c x t τ e x c e 1 - t τ e x c f o r t ≥ 0 0 f o r t < 0 ( 30 ) where x ∈ D1 , D2 , FSI . The rise times for the excitatory inputs τexc was set to be identical for all neurons . In addition to excitatory input from the cortex , D1 and D2 MSNs received ( self and mutual ) recurrent inhibition as well as feedforward inhibition from the FSIs . The corresponding inhibitory conductance changes were modelled as: g i n h x ( t ) = { J i n h x t τ i n h e 1 - t τ i n h f o r t ≥ 0 0 f o r t < 0 ( 31 ) where x ∈ D1 , D2 . The rise times τinh for all inhibitory synaptic conductance transients were set to have identical values . Assuming the synaptic strengths as fixed , the total excitatory conductance G e x c , i x ( t ) in a MSN i was given by G e x c , i x = ∑ m ∈ K i x ∑ n g e x c x ( t - t m n C t x ) ( 32 ) where x ∈ D1 , D2 . The sequence of spikes ( n’s ) impinging on a particular-synapse m are added by the inner sum , while the outer sum runs over all such excitatory synapses m in the set K i x projecting onto neuron i . The set t m n C t x represents the spike times of the excitatory neuron m . The total inhibitory conductance G i n h , i x ( t ) in a D1 or D2 MSN i was given by G i n h , i x = ∑ m ∈ K i F S I ∑ n g i n h F S I ( t - t m n F S I - Δ F S I ) + ∑ m ∈ K i D 1 ∑ n g i n h D 1 ( t - t m n D 1 - Δ D 1 ) + ∑ m ∈ K i D 2 ∑ n g i n h D 2 ( t - t m n D 2 - Δ D 2 ) ( 33 ) where x ∈ D1 , D2 and K i F S I , K i D 1 , K i D 2 refer to the pre-synaptic FSIs , D1 and D2 projecting onto neuron i , respectively . The transmission delays of ΔFSI and ΔD1 , ΔD2 were fixed to 1ms and 2ms , respectively . The total synaptic current to a MSN i was I i x ( t ) = - G e x c , i x ( t ) [ V i x ( t ) - V e x c x ] - G i n h , i x ( t ) [ V i x ( t ) - V i n h x ] ( 34 ) where x ∈ D1 , D2 and V e x c x , V i n h x denote the reversal potentials of excitatory and inhibitory synaptic currents , respectively . Similarly , for a FSI neuron i , the excitatory conductance was: G e x c , i F S I = ∑ m ∈ K i F S I ∑ n g e x c F S I ( t - t m n C t x ) ( 35 ) and the total synaptic current for the FSI neuron i was I i F S I ( t ) = - G e x c , i F S I ( t ) [ V i F S I ( t ) - V e x c F S I ] ( 36 ) The parameter values for both MSNs and FSIs in our network model are summarized in Table 3 . To separately control the correlations within and between the pre-synaptic pools of the striatal neurons , we extended the multiple-interaction process ( MIP ) model of correlated ensemble of Poisson type spike trains [12 , 23] . The MIP model generates correlations by copying spikes from a spike train ( the mother spike train ) with a fixed probability ( the copy probability , which determined the resulting correlation ) to the individual spike trains . This process was implemented in the NEST simulator by introducing a synapse model that transmits spikes with a fixed probability: the lossy synapse . By making many convergent connections using the ‘lossy synapse’ we can mimic the random copying of spikes from the mother spike train to the children process . This way we controlled the correlations within ( W ) the pre-synaptic pool of a neuron . To introduce correlations between ( B ) the pre-synaptic pools of two neurons we created a two-layered MIP ( Fig 10 ) . We started with a spike train M with a firing rate Rin . In the first layer of MIP we generated N spike trains ( C1 , C2 , ⋯ , CN ) with a fixed pair-wise correlation ( B′ ) and firing rate ( Rin B′ ) . In the second layer , each of these spike trains acted as a mother spike train to generate pairwise correlations in the pre-synaptic pools of individual striatal neurons . Each spike train Ci was used to generate correlated spike trains ( Ci0 , Ci1 , ⋯ , Cim ) that acted as the pre-synaptic pool of each striatal neuron with a pairwise correlation W and firing rate Rin B′W ( Fig 10 ) . The effective correlation between the pre-synaptic pools of two neurons is then defined as B = B′W . Thus , in this model we can control the correlations between and within the pre-synaptic pools by changing the copy probabilities in the first and second layer . B refers to the average correlation between the spiking activities of neurons in the pre-synaptic pools of two striatal neurons . The spike trains in each pre-synaptic pool are themselves correlated with a correlation coefficient W . For such pooled random variables , Bedenbaugh and Gerstein [22] derived the following relationship: ρ 1 , 2 = B W + 1 n ( 1 - W ) + 𝓞 1 n where ρ1 , 2 is the correlation coefficient between the two pools of the pre-synaptic neurons , n is the size of each pre-synaptic pool . Because 0 ≤ ρ1 , 2 ≤ 1 , it follows that B ≤ W . Intuitively we can understand this relationship between B and W in the following way: Imagine two pools of identical spike trains , but with individual spikes trains uncorrelated to each other ( i . e . B′ = 1 and W = 0 ) . In this case , the average correlations between the two pools will be small because each spike train has only copy in the other pool , while being uncorrelated with all others . However , when W > 0 , more such pairs with non-zero correlation will occur , increasing the value of B . Because , B ≤ W , the difference between the firing rates of the two MSN neuronal population in Fig 6 is estimated for the lower triangular region . This relationship is explicitly shown also in Fig 10B , where the measured input correlation between the pools ( B ) is shown to be less than W . Our model considers the striatum as a single action unit with ‘Go’ and ‘No-go’ counterparts and , hence , with a single DTT . But ideally , there are multiple channels of competing motor programs which can be envisioned as mutually competing DTT’s . Thus , future work will include the extension of this model to incorporate multiple action channels . Secondly , the effect of input correlations is yet not formulated into the analytical framework . This is a non-trivial problem , due to the non-linear transfer of input correlations into output firing rates , specifically if the output neurons are recurrently interconnected . Thirdly , our model explores the striatal dynamics at steady states , since the model showed stable fixed points . However , it is possible that in the presence of additional phenomena , such as plasticity of cortico-striatal or striato-striatal projections or intrinsic plasticity of the striatal neurons etc . , the model may show interesting transient dynamics . Recent work proposed the existence of a ‘decision threshold’ in a different context [50] . They considered , the ‘decision threshold’ as a function of the RT ( reaction time ) distribution of the rats in a decision task pertaining to the transient dynamics of the basal ganglia . A future extension to this work can explore the effect of DTT due to the asymmetry in the striatal connectivity on the distance to the ‘decision threshold’ in [50] , i . e . the effect of DTT on the RTs of a decision . And lastly , though our model abides by many experimental observations , it contradicts one experimental result , to the best of our knowledge . In [51] , it was observed that FSIs increased their activity around a choice execution . In our model , a choice execution will correspond to the activation of the D1 pathway , which requires reduced activity in the FSIs , in contrast to the recordings in [51] . We think this contradiction is a consequence of our model of the striatum considering only one channel in the ‘Go’ and ‘No-Go’ pathways .
|
The basal ganglia ( BG ) play a crucial role in a variety of cognitive and motor functions . BG dysfunction leads to brain disorders such as Parkinson’s disease . At the main input stage of the BG , the striatum , two competing pathways originate . Neurons projecting on these pathways either express D1 or D2 type dopamine receptors . Because activity of D1 or D2 neurons facilitate go or no-go type decision , it is important to study the balance of the D1 and D2 neuron activity . Contrary to the common assumption thus far , recent data shows an asymmetry in striatal circuit with D1 receiving higher inhibition from D2 and fast-spiking neurons . Here , we studied the functional implications of the asymmetric connectivity between D1 and D2 neurons . Our analysis and simulations show that the asymmetric connectivity between these neurons gives rise to a decision transition threshold ( DTT ) , as a consequence D1 ( D2 ) neurons have higher firing rate at lower ( higher ) average cortical firing rates . Importantly , DTT can be modulated by input correlations , local connectivity , feedforward inhibition and dopamine . Our results suggest that abnormal changes in the DTT could be a plausible mechanism underlying the cognitive and motor deficits associated with brain diseases involving BG malfunction .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Existence and Control of Go/No-Go Decision Transition Threshold in the Striatum
|
While the importance of gene enhancers in transcriptional regulation is well established , the mechanisms and the protein factors that determine enhancers activity have only recently begun to be unravelled . Recent studies have shown that progesterone receptor ( PR ) binds regions that display typical features of gene enhancers . Here , we show by ChIP-seq experiments that the chromatin remodeler CHD8 mostly binds promoters under proliferation conditions . However , upon progestin stimulation , CHD8 re-localizes to PR enhancers also enriched in p300 and H3K4me1 . Consistently , CHD8 depletion severely impairs progestin-dependent gene regulation . CHD8 binding is PR-dependent but independent of the pioneering factor FOXA1 . The SWI/SNF chromatin-remodelling complex is required for PR-dependent gene activation . Interestingly , we show that CHD8 interacts with the SWI/SNF complex and that depletion of BRG1 and BRM , the ATPases of SWI/SNF complex , impairs CHD8 recruitment . We also show that CHD8 is not required for H3K27 acetylation , but contributes to increase accessibility of the enhancer to DNaseI . Furthermore , CHD8 was required for RNAPII recruiting to the enhancers and for transcription of enhancer-derived RNAs ( eRNAs ) . Taken together our data demonstrate that CHD8 is involved in late stages of PR enhancers activation .
During the last decade it has become clear that regulation of gene transcription is accompanied by extensive changes in the chromatin organization of promoters [1] . More recently , efforts have concentrated on elucidating the chromatin dynamics of distal regulatory regions and particularly enhancers [2 , 3 , 4] . Enhancers were originally defined as regulatory sequences that can activate gene expression independently of their proximity or orientation with respect to their target genes [5] . Even though histone modifications signatures ( e . g . , high levels of histone H3 lysine 4 monomethyl ( H3K4me1 ) and H3K27 acetyl ( H3K27ac ) modifications ) have provided insight for the discovery of enhancer-like regions , the mechanisms and the factors that control enhancers activation are not yet well known . Chromatin changes are normally performed by two types of enzymes: enzymes that chemically modify histones or ATP-dependent chromatin remodelers . ATP-dependent remodelling is performed by enzymes of the SNF2 family that use the energy of ATP hydrolysis to destabilize the interaction between DNA and histones [6 , 7] . In humans there are 26 ATPases of this family with specific roles in gene transcription and in other aspects of DNA metabolism . One of these ATPases is CHD8 which , in addition to the ATPase domain , contains two chromodomains in the amino terminus of the protein , and two BRK domains in the carboxy terminus [8] . CHD8 is able to remodel nucleosomes in vitro in an ATP-dependent reaction [9]; however , its in vivo functions are unclear . Inactivation of CHD8 by homologous recombination in mice provokes a strong growth retardation from embryonic day 5 . 5 and developmental arrest accompanied by massive apoptosis [10] . Ishihara et al . reported that CHD8 interacts with CTCF and plays a role in insulation activity [11] . It has also been reported that CHD8 represses beta-catenin target genes , and suppresses p53-dependent activation and apoptosis , by promoting histone H1 recruitment [9 , 12 , 13 , 14] . A role in repression of MLH1 gene , associated to MAFG , has been also shown [15] . In contrast to this repressive role , we have shown that CHD8 is required for E2F-dependent activation of G1/S specific promoters [16 , 17] . Additionally , it has been reported that CHD8 is required for estrogen-dependent induction of Cyclin E2 gene [18] , and for recruitment of androgen receptor ( AR ) and activation of the TMPRSS2 gene [19] . These two studies indicate that CHD8 is also involved in steroid hormone-dependent transcriptional regulation although the mechanisms of this regulation are unknown . In this work , we have investigated the role of CHD8 in progesterone-dependent transcriptional regulation . Progesterone controls transcription through a complex mechanism [20] . On the one hand , progesterone-bound progesterone receptor ( PR ) is able to bind specific DNA sequences in chromatin , called PRE , and to recruit histone modification enzymes and ATP-dependent chromatin remodelers , such as NURF and BAF complexes ( a member of the SWI/SNF complex family ) [21 , 22] . On the other hand , a small fraction of PR is attached to the cytoplasmic side of the cell membrane and , in the presence of hormone , interacts with tyrosine kinases provoking the activation of various kinase cascades , including ERK1/ERK2 [23 , 24] . Activated ERK1/ERK2 phosphorylates PR and the kinase MSK1 , forming a ternary complex that binds to chromatin . Recent genome-wide studies demonstrated that most PR binding sites ( PRbs ) are distal regulatory regions that map at introns and intergenic regions and that display typical histone modifications of enhancer regions [25 , 26 , 27] . Here , we demonstrate that CHD8 is required for the activation of PR enhancers . In proliferating T47D breast carcinoma cells , CHD8 is mostly associated with promoters . However , upon progesterone treatment , CHD8 was quickly recruited to a subset of transcriptionally competent PR enhancers . In agreement with these data , depletion of CHD8 strongly impaired progesterone-regulated gene expression . CHD8 recruitment to the enhancers was dependent of PR but independent of the pioneering factor FOXA1 . Interestingly , depletion of the SWI/SNF complex ATPases , BRG1 and BRM , impaired CHD8 recruitment . Furthermore , we observed that CHD8 interacts with the SWI/SNF complex . CHD8 was not required for H3K27 acetylation , but depletion of CHD8 impaired hormone-dependent RNAPII recruitment at enhancers and synthesis of enhancer RNAs ( eRNAs ) , suggesting that CHD8 is required for enhancer transcription .
To identify the genome-wide distribution of CHD8 in proliferating human breast cancer cells T47D-MTVL [28] , we performed chromatin immunoprecipitation of CHD8 followed by deep sequencing ( ChIP-seq ) . We found 12655 , 4900 and 2500 peaks of CHD8 by using three different confidence threshold values , respectively ( ChIPseeqer threshold level 10–10 , 10–15 and 10–20 ) . In all cases , false discovery rates ( FDR ) were lower than 0 . 03 ( see Materials and Methods ) . At the lowest threshold about 48% of the peaks ( 6257 ) were located within promoters , with a strong enrichment around TSS ( Fig 1A and 1B ) and about 50% of the peaks were distributed between introns ( 2187 ) and intergenic regions ( 3748 ) . Interestingly , if confidence threshold for peaks identification is increased , the percentage of peaks associated with promoters raise to about 78% ( Fig 1A ) . This is due to differences in CHD8 signal intensity . In fact , CHD8 signals at TSSs are higher than at intergenic or intronic regions ( Fig 1C and 1D ) . Therefore , when only highly significant peaks are analyzed most of them map at promoters . Consistently , a strong overlap between CHD8 and RNA polymerase II ( RNAPII ) or H3K4me3 ChIP-seq signals was observed ( Fig 1C and 1E ) . CHD8 occupancy was confirmed by ChIP-qPCR in three selected target promoters ( CCND1 , HDS11B2 and CCNE2 ) using a different anti-CHD8 antibody ( S1A Fig ) . Furthermore , as control , we also verified that knockdown of CHD8 decreased ChIP-qPCR signal ( S1B Fig ) . All these experiments validated our ChIP-seq results . The rest of the analysis was performed using the 12655 CHD8 sites identified at the lowest , but still very significant , threshold . We have previously reported that CHD8 binds 1965 promoters in a ChIP-on-chip analysis of proliferating cervical carcinoma C33 cells [17] . Approximately 60% of the promoters identified as CHD8 targets by ChIP-on-chip ( 1175 ) were also identified by ChIP-seq , despite the different cell lines used . CHD8 bound genes were enriched in Gene Ontology categories related to macromolecular biosynthetic processes ( transcription , mRNA processing ) and cell cycle ( S2A Fig ) . Consistently with our previous data from C33 cells [17] CHD8 target promoters were strongly enriched in E2F ( p-value = 3 . 6 × 10–135 ) , ELK-1 ( p-value = 2 . 2 × 10–122 ) , AP-2 ( p-value = 4 . 7 × 10–100 ) , and SP1 ( p-value = 4 . 2 × 10–80 ) transcription factors binding sites ( S2B Fig ) . Taken together these results indicate that CHD8 is mostly bound to promoters in proliferating T47D-MTVL cells . CHD8 has been suggested to be a nuclear receptor co-activator [18 , 19]; however , very little is known about this function of the protein . To gain insight into its role in hormone dependent transcriptional regulation we have analyzed by ChIP-seq the distribution of CHD8 in progesterone-treated T47D-MTVL cells . For that , cells were subjected to 48 h of serum deprivation and then stimulated during 5 or 45 minutes with the synthetic progestin R5020 ( 10 nM ) or the vehicle ( ethanol ) as control . A very small number of peaks were found in vehicle treated cells , suggesting that serum deprivation strongly decreases the association of CHD8 to the chromatin . These data extend our previous observation about absence of CHD8 in four G1/S transition genes in quiescent cells [17] . However , 1132 and 4532 progestin-induced CHD8 peaks were found at 5 and 45 minutes , respectively , suggesting that CHD8 is quickly recruited to the chromatin after progestin treatment . Most of the sites found after 5 minutes ( 73% ) were also identified after 45 minutes ( Fig 2A ) . Only 18% ( 832 ) of the hormone-specific peaks were also found under proliferating conditions ( Fig 2A ) . A large majority of the hormone specific sites were found at intronic and intergenic regions ( Fig 2B ) and low enrichment was found at TSS ( S3 Fig ) . A representative example of the ChIP-seq data close to four well-known progesterone-dependent genes ( HSD11B2 , FKBP5 , NFE2L3 and IL6ST ) is shown in Fig 2C . De novo analysis of sequence motifs in progestin-dependent CHD8 bound regions identified a highly significant enrichment ( p-value = 4 . 7 × 10–75 ) for the sequence CTGTNC , which is very similar to the consensus sequence of the progesterone receptor binding site TGTYCY [25] ( Fig 2D ) . PR binds 24436 sites in T47D-MTVL cells 60 min after progestin treatment [25] . Interestingly , 83 . 2% of the CHD8 progestin-dependent peaks ( 3770 ) co-localized with PR peaks , indicating that CHD8 is recruited to a subset of PRbs upon hormone induction ( Fig 2E ) . Thus , CHD8 was significantly enriched around PRbs ( Fig 2F ) . Ballaré et al . have recently reported that PRbs present high nucleosome occupancy and that functional PR sites , involved in transcription control , display a high nucleosome-remodelling index ( NRI ) [25] . NRI is the ratio between the nucleosome occupancy before and after hormone administration . Strikingly , we found strong CHD8 occupancy at PRbs with high NRI ( top 10% higher NRI ) and weak CHD8 enrichment at PRbs with low NRI ( top 10% lower NRI ) , suggesting that CHD8 is associated with functional PRbs , where a strong nucleosome remodelling is occurring ( Fig 2G and 2H ) . Similarly to CHD8 binding sites , PRbs are mostly found in introns and intergenic regions [25] . Distal regulatory regions and enhancers are enriched in the histone acetyltransferase p300 [29] . In the presence of R5020 , CHD8 signal was strongly enriched around p300 binding sites ( Fig 2I ) , indicating that CHD8 binding sites display enhancer characteristics . Another typical enhancer feature is the presence of monomethylated histone H3 lysine 4 ( H3K4me1 ) [30] . CHD8 was moderately enriched in all regions with high H3K4me1 ( S4 Fig ) . Most interestingly , we observed strong enrichment of CHD8 around H3K4me1 containing PRbs ( Fig 2J ) . Taken together all these data indicate that CHD8 binds functional PR enhancers in a hormone-dependent manner . It has been reported that CHD8 interacts and cooperates with the insulator factor CTCF ( CCCTC-binding factor ) [11] . Therefore , we have studied the co-localization of both factors under normal growth conditions or after progesterone stimulation . In proliferating T47D-MTVL cells about 16 . 5% ( p-value = 6 . 0 x 10–130 , hypergeometric distribution ) of the CHD8 containing regions were also enriched in CTCF ( S5A Fig ) . However , this was only 4 . 4% of the CTCF sites . CTCF mostly binds to intergenic or intronic regions [31] . Consistently , most of the CTCF-CHD8 co-occupied sites ( 66% ) were also at intergenic and intronic regions ( S5B Fig ) . Upon progesterone stimulation only about 5 . 4% of the CHD8 sites and 0 . 6% of the CTCF sites are co-occupied by both factors ( S5C Fig ) , suggesting that CHD8 and CTCF do not cooperate for progesterone-mediated regulation . To correlate CHD8 binding sites with CHD8-regulated gene expression we performed a transcriptomic analysis of T47D-MTVL cells transfected with a control siRNA or a siRNA specifically targeting CHD8 and stimulated during 6 h with progestin or vehicle ( Fig 3A and 3B ) . We found 1170 genes differentially expressed ( FDR< 0 . 01 and lineal change > 1 . 5 fold ) after progestin treatment of control cells , of which 793 were up-regulated and 377 down-regulated . About 52 . 5% of these genes ( 614 ) were misregulated in CHD8-depleted cells with respect to control cells . Interestingly , CHD8-dependent genes presented lower induction of up-regulated genes and lower repression of down-regulated genes , indicating that CHD8 is required for progesterone-dependent regulation of a subset of genes ( Fig 3C ) . Consistently , around 42% of the CHD8-dependent genes ( 257 ) were found to be close to CHD8 genomic locations ( P = 9 . 96 x 10–103 ) ( Fig 3D ) . Next , we verified by RT-qPCR that CHD8 was required for normal progestin-dependent induction of several well known progesterone dependent genes that contain close hormone-dependent CHD8 binding sites , such as HSD11B2 , DUSP1 , FKBP5 , NFE2L3 and IL6ST genes . Thus , depletion of CHD8 severely impaired accumulation of mRNA , both 45 min and 6 h after progestin treatment ( Fig 3E ) . As a control , we confirmed that a different siRNA that targets CHD8 had similar effects on expression of progesterone dependent genes ( S6 Fig ) . T47D-MTVL cells contain a single copy of the MMTV-Luc transgene integrated in their genome [28] . CHD8 was also strongly enriched in a progestin-dependent manner at the MMTV-Luc transgene promoter ( Fig 3F ) . Furthermore , depletion of CHD8 severely impaired induction of MMTV-Luc ( Fig 3E and S6 Fig ) . In summary , these data demonstrate that CHD8 is necessary for progestin-dependent regulation , at least in a subset of target genes . Next we selected four CHD8 peaks close to HSD11B2 , FKBP5 , NFE2L3 and IL6ST genes that display high ChIP-seq enrichment for CHD8 and PR upon progestin stimulation ( Fig 4A ) . These regions were also enriched for the typical enhancer factor p300 ( Fig 4A ) . Therefore , we called these regions HSD11B2e , FKBP5e , NFE2L3e and IL6STe . All enhancers were located upstream of the corresponding genes ( Fig 2C ) . Then , we decided to investigate how PR affects CHD8 recruitment to these enhancers and vice versa . First we analyzed PR and CHD8 recruitment to CHD8 progestin-dependent peaks in T47D-MTVL cells or in T47D-YV , a PR-negative clonal derivative cell line of T47D [32] ( Fig 4B ) . We verified by western blotting that levels of CHD8 were identical in T47D-MTVL and in T47D-YV ( Fig 4B ) . ChIP experiments performed 45 minutes after progestin stimulation confirmed that high levels of PR are recruited to all analyzed regions in T47D cells but , as expected , not in T47D-YV cells ( Fig 4C ) . Interestingly , CHD8 was also strongly recruited to all analyzed regions in T47D-MTVL cells but not in T47D-YV cells , suggesting that PR is required for hormone-dependent CHD8 recruitment to PRbs ( Fig 4C ) . A significant recruitment of CHD8 to the IL6ST regulatory region was observed in T47D-YV cells suggesting that CHD8 is recruited to this region , at least in part , independently of PR . Residual binding of progestin to other nuclear receptor might be responsible of this effect . Next , we determined whether CHD8 is involved in PR recruitment to PRbs in T47D-MTVL cells . Fig 4D shows that CHD8 depletion ( siCHD8 ) did not affect progestin-dependent PR recruitment to the four analyzed regulatory regions . As a control , we verified that silencing of CHD8 strongly decreased its association with chromatin , validating the ChIP signals . Taken together , these data indicate that PR is required for CHD8 recruitment to progesterone-dependent CHD8 binding sites , but PR does not require CHD8 for binding . FOXA1 is a fork-head family transcription factor able to directly bind to DNA in the surface of a nucleosome [33] . Because of this pioneering ability , FOXA1 is able to facilitate estrogen receptor binding to the chromatin of hormone-dependent enhancers [34 , 35] . Since PR is able to bind directly to nucleosomes [28] , it is unclear whether FOXA1 also cooperates with PR . ChIP-seq data of FOXA1 distribution in unstimulated T47D cells are available from ENCODE . Using these data we have observed that 52% ( 2358 ) of the CHD8 binding sites were also enriched for FOXA1 ( Fig 5A ) . Furthermore , 54% ( 2022 ) of the CHD8-PR co-occupied regions were also occupied by FOXA1 . Given this very significant enrichment ( P = 1 . 17 x 10–173 ) we decided to study the role of FOXA1 in CHD8 and PR recruiting . First , we investigated using ChIP whether FOXA1 binds to the analyzed regulatory regions of HSD11B2 , FKBP5 , NFE2L3 and IL6ST genes . As shown in Fig 5B significant levels of FOXA1 were found at all studied regions . FOXA1 occupancy after progestin treatment at HSD11B2e , NFE2L3e and IL6STe enhancers was similar in T47D-MTVL and T47D-YV cells , suggesting that PR is not involved in FOXA1 recruitment at these regions ( Fig 5B ) . However , a possible role of ligand-bound PR in FOXA1 recruitment was observed at the FKBP5e enhancer ( Fig 5B ) . Next , we determined the effect of FOXA1 silencing ( Fig 5C ) on the hormone-dependent recruitment of CHD8 and PR in T47D-MTVL cells . Surprisingly , depletion of FOXA1 significantly increased the level of occupancy of both PR and CHD8 at the four regulatory regions analysed ( Fig 5D and 5E ) , suggesting that , at least in these progestin-dependent regulatory regions , FOXA1 does not help PR and subsequent CHD8 recruitment . Moreover , our data suggest that FOXA1 might compete with PR for binding to PR-enhancers . PBAF and BAF are closely related chromatin remodelling complexes of the SWI/SNF family , which share multiple protein subunits . The BAF complex is required for progesterone-dependent gene activation [21 , 22 , 25] . Furthermore , BAF250 and BAF57 , two of the subunits of the complex , interact with PR in a hormone-dependent manner [22] . Then , we decided to investigate whether CHD8 interacts with the SWI/SNF complexes . For that , we performed immunoprecipitation using anti-CHD8 antibodies from extracts of T47D-MTVL cells . CHD8 co-precipitated with the core subunits INI1/hSNF5/BAF47 , BAF170 and BAF155 and with the ATPase BRG1 in T47D-MTVL cells ( Fig 6A ) . All these subunits form part of both SWI/SNF complexes: BAF and PBAF . To identify the type of SWI/SNF complex interacting with CHD8 , we also investigated the presence of BAF180 , a PBAF-specific subunit , and BAF250 , a BAF-specific subunit . As shown in Fig 6A both subunits co-precipitated with CHD8 indicating that CHD8 can interact with both complexes . The CHD8-SWI/SNF interaction was found both , in the presence and in the absence of hormone ( S7A Fig ) . Next , we studied whether the SWI/SNF complex is required for CHD8 recruitment to PRbs . First , we verified by ChIP that BAF155 , one of the core subunits of the complex , is recruited to the four analyzed CHD8-bound enhancers and to the MMTV promoter ( Fig 6B ) . Then , we demonstrated that knockdown of BRG1 and BRM ( S7B and S7C Fig ) significantly impaired recruitment of CHD8 to the four analyzed PR enhancers and to the MMTV promoter ( Fig 6C ) . Taken together , these data suggest that CHD8 interacts with the SWI/SNF complex and that this interaction contributes to recruit or to stabilize CHD8 at PRbs . Presence of H3K27Ac distinguishes active enhancer states from those poised for activation [36 , 37 , 38] . H3K27 is acetylated by p300 [39] and we have shown that CHD8 is enriched around p300 binding sites ( Fig 2I ) . To further characterize the role of CHD8 in enhancer activation we investigated whether CHD8 affects the level of H3K27Ac at the four selected CHD8 binding sites . At the FKBP5e and IL6STe regions H3K27 acetylation was enhanced by R5020 ( Fig 7A ) . However , significant levels of H3K27Ac were already observed under un-stimulated conditions at the HSD11B2e and NFE2L3e enhancers , which were not further stimulated by progestin ( Fig 7A ) . Interestingly , CHD8 depletion did not affect the level of H3K27Ac at any of the analyzed regions , suggesting that CHD8 is not involved in p300 recruiting or H3K27 acetylation . Next we investigated whether CHD8 is required to open the chromatin of its target enhancers . For that , we performed quantitative DNase I sensitivity assays in HSD11B2e , FKBP5e , NFE2L3e and IL6STe enhancer regions in the presence of hormone or vehicle , as described in [40] . Interestingly , CHD8 knockdown decreased hormone-dependent accessibility to the enzyme of all the analyzed regions suggesting that CHD8 is involved in the hormone-dependent chromatin remodelling of these regions ( Fig 7B ) . Several studies have shown that many active enhancers present significant occupancy of RNAPII and regulated production of bidirectional RNA , called eRNAs [41 , 42 , 43 , 44 , 45] . We have investigated whether RNAPII is recruited to FKBP5e , NFE2L3e and IL6STe enhancer regions and the order of recruitment with respect to PR and CHD8 . For that , binding of PR , CHD8 and RNAPII at 0 , 2 , 5 , 15 and 30 min after progestin stimulation was determined by ChIP-qPCR ( Fig 8A ) . As previously reported for the MMTV promoter [21] , PR was found at the enhancers as early as 2 min after hormone addition . CHD8 was absent at 2 min but was found at the 5 min time point , remaining in the enhancers during the 15 and 30 min time points . Finally , RNAPII occupancy increased between 15 and 30 min at the FKPB5e and only at 30 min at NFE2L3e and IL6STe enhancers ( Fig 8A ) . These data indicate that RNAPII is recruited to the enhancers after PR and CHD8 and suggest that RNAPII recruitment is a late event during the process of enhancer activation . Then , we investigated the role of CHD8 in RNAPII recruitment . Interestingly , hormone-stimulated RNAPII occupancy was strongly impaired in CHD8-depleted cells ( Fig 8B ) . Next , eRNA synthesis at FKBP5e , NFE2L3e and IL6STe enhancers was evaluated by RT-qPCR both 45 min and 6 h upon R5020 addition ( see Materials and Methods ) . Progestin increased between 2 and 7 fold production of eRNAs already at 45 min and expression was maintained after 6 h ( Fig 8C ) . As a control , we verified that no qPCR signal was observed in the absence of reverse transcriptase . Consistently with the effect of CHD8 depletion in RNAPII occupancy , silencing of CHD8 significantly impaired eRNA synthesis from the three analyzed regions ( Fig 8C ) . These results indicate that CHD8 is required for RNAPII recruitment and enhancer transcription , at least from a subset of PR enhancers .
We have recently reported a promoter ChIP-on-chip analysis demonstrating that , in cervix carcinoma C33 cells , CHD8 binds around 2000 active promoters also enriched in H3K4me2 and H3K4me3 , and that it is required for expression of E2F-dependent G1/S transition genes [17] . In agreement with these results , our genome-wide ChIP-seq analysis reveals that most of CHD8 is bound to promoters also enriched in RNAPII and H3K4me3 in proliferating T47D-MTVL cells . While this paper was in revision another group has also shown that CHD8 binds thousand of active promoters in an iPSC-derived neuronal progenitor cell line [47] . All these data from different cell lines support that CHD8 is a promoter-associated factor . However , upon progestin stimulation CHD8 was rarely found at promoters; instead it was recruited to a number of PRbs mostly located in intergenic and intronic regions enriched in H3K4me1 and p300 , indicating that these sites are progesterone-dependent enhancers . Interestingly , under normal proliferation conditions , CHD8 was found at the promoters of genes close to these enhancers ( p < 0 . 001 ) . While this fact might suggest that CHD8 dynamically move from enhancers to close promoters , further experiments are required to demonstrate this association . Our data demonstrate that CHD8 binds both , promoters and enhancers upon specific stimulation such as progesterone . CHD8 has also been involved in AR-dependent [19] and ER-dependent [18] regulation . It is , therefore , possible that CHD8 also binds AR or ER enhancers in the presence of the appropriated hormonal stimulus . CHD7 is a paralogue of CHD8 and both proteins has been shown to interact [48] . ChIP-seq analysis of mice ES cells has demonstrated that CHD7 is mostly associated with a subset of active enhancers and promoters [49 , 50] . Comparison of CHD8 genomic distribution with that of CHD7 available from ENCODE showed that only 7 . 1% and 5 . 6% of the CHD8 peaks overlap with CHD7 containing regions in K562 or H1 stem cells , respectively . Most enhancers are tissue- and cell line-specific and therefore it is difficult to compare genomic positions between different cell lines . Nevertheless , these data suggest that although CHD7 and CHD8 may cooperate in some genomic locations , they also have specific independent roles . It has been reported that CTCF interacts with the carboxy-terminus of CHD8 and that CHD8 co-occupy several CTCF binding sites [11] . Our genome-wide study demonstrates that , in the absence of progesterone , 16 . 5% of the CHD8 binding sites are enriched for CTCF and 4 . 4% of CTCF sites are occupied by CHD8 . While this percentage is largely higher than expected by chance , it is clear that CHD8 might only be involved in CTCF function in a small percentage of insulators . Since CTCF sites are often located at enhancer regions [51] and enhancer looping activity has been related to CTCF and cohesins [52] , it is possible that the CHD8-CTCF co-occupancy is more related to the role of CHD8 in enhancers than to specific functions in insulation . In the presence of progestin the number of sites co-occupied by CTCF and CHD8 is even lower , suggesting that their association is not related to progesterone dependent transcription . Here we show that CHD8 is not recruited to four selected progesterone enhancers in the absence of PR , indicating that PR is essential for the recruiting . This is consistent with our time-course experiment where PR was found at the enhancers earlier than CHD8 upon hormone addition . However , we have been unable to detect direct PR-CHD8 interaction suggesting that CHD8 may be recruited through interaction with other co-regulators of PR enhancers . Despite the very significant overlapping between the pioneering factor FOXA1 and CHD8 binding sites , we show that FOXA1 silencing even increased CHD8 occupancy at the four analyzed enhancers , indicating that FOXA1 is not required for CHD8 recruitment . The chromatin remodelling BAF complex is recruited to PRbs [25] and is required for progesterone-dependent remodelling of the MMTV promoter [21 , 22] . Interestingly , we have observed that CHD8 interacts with both SWI/SNF complexes: BAF and PBAF . In addition , two high throughput proteomic analysis have identified co-immunoprecipitation of CHD8 and SWI/SNF subunits [53 , 54] . Since PR directly interacts with BAF complex it is possible that CHD8 is recruited to PRbs through interaction with the BAF complex . Consistently with this hypothesis , knocking down of BRG1 and BRM reduced CHD8 recruiting . Interestingly , it has been reported that the CHD8 paralogue , CHD7 , interacts with PBAF , the other human SWI/SNF complex [55] . Whether other CHD8 paralogues , CHD6 and CHD9 , also interact with SWI/SNF complexes is unknown . It is also unclear the extent to which CHD8 and SWI/SNF complexes cooperate and whether they can have common and independent targets . It is worth noting that both , SWI/SNF complexes and CHD8 are required for activation of E2F-dependent genes at the G1/S transition [17] [56] . On the other hand , we have reported that the chromodomains of CHD8 binds dimethylated and trimethylated H3K4 peptides [16] . H3K4me2 modification is typically associated with enhancers [57 , 58 , 59] . In addition , Vicent et al . reported that H3K4 methylation by the ASCOM ( ASC-2 [activating signal cointegrator-2] complex ) is required for the progesterone-dependent induction of the MMTV promoter [21] . Therefore , it is possible that CHD8 chromodomains interaction with methylated H3K4 also contributes to the recruitment or the stabilization of CHD8 at PR enhancers . We have shown that CHD8 depletion does not impair PR binding . Since PR directly interacts with BAF complex it is unlikely that CHD8 is required for BAF recruitment or activity . In fact , we show that BRG1 and BRM are required for normal CHD8 occupancy of PR enhancers suggesting that CHD8 acts downstream of the BAF complex . Another common step of enhancer activation is acetylation of H3K27 by the histone acetyltransferase p300 [29 , 36 , 37 , 38] . Ballare et al . , found that progestin-dependent PR biding sites were enriched in regions that contained p300 under un-stimulated conditions and that , in general , the level of p300 increased upon hormone treatment . We have observed that progestin stimulated H3K27 acetylation at the FKBP5e and IL6STe enhancers but not at the HSD11B2e and NFE2L3e enhancers . Depletion of CHD8 did not affect levels of H3K27Ac at any of the regions , suggesting that CHD8 was not involved in this step of enhancer activation . Numerous reports have evidenced that enhancer activation involves RNAPII recruiting and synthesis of eRNA , monodirectional or bidirectional transcripts of 0 . 5 to 5 kb . Recent results demonstrate that eRNAs are involved in transcriptional activation of neighbouring genes , in enhancer-promoter looping and in directing chromatin-remodelling events at specific promoters [60 , 61 , 62 , 63 , 64] . The time-course experiment shown in Fig 8A indicates that RNAPII is recruited between 15 and 30 minutes after hormone stimulation , while PR and CHD8 reach the enhancers around 2 and 5 min after stimulation , respectively . We show that depletion of CHD8 strongly impairs recruiting of RNAPII and synthesis of eRNAs , demonstrating that CHD8 is required for these late events of progesterone enhancers activation . In Drosophila , the CHD8 orthologous Kismet is found at transcriptionally active genes in polytene chromosomes [65] . Kismet mutations reduce the level of phosphorylated elongating RNAPII but not the level of initiating RNAPII , suggesting that Kismet is involved in transcription elongation . Human CHD8 interacts with elongating RNAPII [16] . Furthermore , CHD8-depleted cells are hypersensitive to drugs that inhibit phosphorylation of serine 2 of the carboxy-terminal domain ( CTD ) of POLR2A , the largest subunit of RNAPII ( DRB and flavopiridol ) , an early step of the transcription cycle [16] . These data suggest that , as Kismet , CHD8 may also be involved in elongation . Kaikkonen et al . , have recently reported that eRNA synthesis is sensitive to flavopiridol [58] , suggesting that eRNA synthesis also requires CTD serine 2 phosphorylation . Therefore , it is possible that CHD8 is required for transcription elongation of eRNAs at PR enhancers . We also show that CHD8 is required to increase DNase I sensitivity at enhancers upon hormone treatment , suggesting that CHD8 may be involved in the hormone-dependent nucleosomal remodeling that occurs at a subset of PRbs [25] . It is well known that transcription increases DNase I sensitivity of gene bodies [66 , 67] . Therefore , it is also possible that CHD8 effect on DNase I sensitivity is caused by its role in enhancer transcription . Vicent et al . demonstrated that BAF and NURF chromatin remodelling machines are required for PR-dependent activation of the MMTV promoter and other PR enhancers [22 , 25] . Now we add a third actor , CHD8 , to the list of remodelers required for progesterone-dependent activation . However , CHD8 seems to be recruited only to at subset of PRbs , including the MMTV promoter . Future experiments will be required to find what determines CHD8 recruitment to some PRbs and not to others . It is worth noting that it has been reported that CHD9 interacts in vitro with nuclear receptors such as PPARA ( PPARα ) , NR1I3 ( CAR ) , NR3C1 , ESR1 ( ERα ) and RXRA [68 , 69] . So , it is tempting to speculate that other CHD8 paralogues may also regulate , redundantly or non-redundantly with CHD8 , hormone-dependent enhancers activation .
T47D-MTVL human breast cancer cells carrying one stably integrated copy of the luciferase reporter gene under the control of the MMTV promoter [28] and T47D-YV cells ( PR-negative clonal derivative cell line of T47D [32 , 70] ) were routinely grown in RPMI 1640 medium supplemented with 10% FBS , 2 mM L-glutamine , 100 U/ml penicillin and 100 mg/ml streptomycin . Cells were grown exponentially ( in 10% FBS ) or subjected to serum-free conditions in RPMI medium without phenol red during 48 h . After serum starvation , cells were incubated with 10 nM R5020 or vehicle ( ethanol; EtOH ) for the indicated times . ChIP assays were performed as described [71] using anti-CHD8 ( A301-224A ) from Bethyl Laboratories or home-made rabbit anti-CHD8 [16] , anti-RNAPII ( N-20 ) ( sc-899 ) , anti-PR ( H-190 ) ( sc-7208 ) , anti-BAF155 ( R-18 ) ( sc-9746 ) and anti-FOXA1 ( H-120 ) ( sc-22841 ) from Santa Cruz Biotechnology , and anti-H3K27Ac ( ab4729 ) from Abcam . Chromatin was sonicated to an average fragment size of 400 to 500 bp using the Diagenode Bioruptor . Rabbit IgG ( Sigma ) was used as a control for non-specific interactions . Input was prepared with 10% of the chromatin material used for immunoprecipitation . Input material was diluted 1:10 before PCR amplification . Quantification of immunoprecipitated DNA was performed by real-time PCR ( qPCR ) with the Applied Biosystems 7500 FAST real-time PCR system , using Applied Biosystems Power SYBR green master mix . Sample quantifications by qPCR were performed in triplicate . Sequences of all oligonucleotides are available upon request . Data are the average of at least three independent experiments . ChIP was performed as described above using anti-CHD8 ( A301-224A , Bethyl Laboratories ) . ChIP-DNA was purified and subjected to deep sequencing using the Solexa Genome Analyzer ( Illumina ) . The sequence reads were aligned to the human genome reference ( assembly hg19 ) . ChIP-seq peak calling , genomic annotation of peaks and comparison between ChIP-seq and ENCODE datasets were performed using ChIPseeqer ( v . 2 . 1 ) [72] . An empirical approach was followed to estimate the FDR , which involves using control data set as ChIP-seq data and the ChIP-seq data as the pseudo-control data and running peak detection . The FDR is defined as the ratio of the number of peaks detected in this pseudo-control analysis , to the number of peaks detected in the real ChIP-seq experiment . Motif analysis was performed using FIRE algorithm [73] , included in the ChIPseeqer framework , MEME suite [74] , Weeder PScan [75] and TRANSFAC database [76] . CHD8 ChIP-seq data are available from the GEO database ( accession number GSE49134 ) . RNAPII , H3K4me3 and PR ChIP-seq data in T47D were previously reported [25] . GEO accession number for FOXA1 and CTCF ChIP-seq data are GMS803409 , GSM803348 . All siRNAs were transfected using Oligofectamine ( Invitrogen ) according to the manufacturer’s instructions , with following siRNA sequences: for siCHD8 , 5′-GAGCAAGCUCAACACCAUC-3′; siFOXA1 , 5′-GAGAGAAAAAAUCAACAGC-3′; siCt , 5′-CGUACGCGGAAUACUUCGA-3′; siBRG1 , 5′-GCGACUCACUGACGGAGAA-3′; and siBRM , 5′-GAAAGGAGGUGCUAAGACA-3′ . After transfection , medium was replaced by serum-free fresh medium without phenol red . After 48 h in serum-free conditions , cells were treated with 10 nM R5020 or vehicle ( EtOH ) for the indicated times . The down-regulation of CHD8 , FOXA1 , BRG1 and BRM was confirmed by RT-PCR and Western blotting , respectively . T47D-MTVL cells were grown in serum-free conditions , as explained previously , during 48 h and treated with 10 nM R5020 or vehicle ( EtOH ) during 6 h . Total RNA was isolated in triplicate from cells using RNeasy Mini Kit ( Qiagen ) . Purity and quality of isolated RNA were assessed by RNA 6000 Nano assay on a 2100 Bioanalyzer ( Agilent Technologies , Santa 6 Clara , CA ) . RNA ( 100 ng ) was used for production of end-labelled biotinylated ssDNA . Labelled ssDNA was hybridized to the GeneChip human Gene 1 . 0 ST array oligonucleotide microarray ( Affymetrix , Santa Clara , CA ) according to manufacturer’s recommendations . The arrays were scanned using the GeneChip Scanner 3000 7G ( Affymetrix ) , and raw data were extracted from the scanned images and analyzed with the Affymetrix GeneChip Command Console Software ( Affymetrix ) . The raw array data were pre-processed and normalized using the Robust Multichip Average ( RMA ) method [77] . Data were further processed using oneChannelGUI [78] . The log2 intensities for each probe were used for further analysis . Genes where considered as hormone induced when change of gene expression was >1 . 5 ( linear fold change ) and p-value < 0 . 01 . Gene expression was considered as CHD8-dependent when [hormone-dependent change in siCHD8]/[hormone-dependent change in siCt] was higher than 1 . 20 or lower than 0 . 8 . Microarray data are available from the GEO database ( accession number GSE62257 ) . DNase I assay was performed as previously described [40] . Briefly , 2 μg of crosslinked chromatin were treated with 0 . 5 , 1 , and 2 units of DNase I ( Roche ) for 3 min at 37°C . Control samples were incubated in the absence of DNaseI . Reactions were stopped by adding EDTA and the crosslinking was reversed by incubating the samples at 65°C . DNA was then isolated , quantified and used as template for qPCR reactions using specific primers . Total RNA was prepared by using the RNeasy Kit ( Qiagen ) , as described in the manufacturer’s instructions; note that the step of DNase I digestion was included to avoid potential DNA contamination . cDNA was generated from 800 ng of total RNA ( for progesterone-dependent mRNA induction analysis ) using Superscript First Strand Synthesis System ( Invitrogen ) . For progesterone-dependent eRNA induction analysis 2 μg of total RNA was used . In the case of FKBP5e and HSD11B2e eRNA determination , strand-specific oligonucleotides were used for RT , in order to avoid expression form close promoters . cDNA ( 2 μl ) was used as a template for qPCR . Gene products were quantified by real-time PCR with the Applied Biosystems 7500 FAST real-time PCR system , using Applied Biosystems Power SYBR green master mix . Sequences of all oligonucleotides are available upon request . Values were normalized to the expression of the 28S housekeeping gene . Each experiment was performed at least in duplicate , and qPCR quantifications were performed in triplicate . Co-immunoprecipitations were performed as described in [17] using the anti-CHD8 antibody ( A301-224A ) from Bethyl Laboratories . Rabbit or mouse purified IgG ( Sigma-Aldrich ) were used as a control . 3% Input and precipitated proteins were separated by SDS/PAGE , and visualized by Western blotting with the indicated antibodies using ECL Plus ( GE Healthcare ) , according to the manufacturer’s instructions . Antibodies used for western blotting were: anti-BRG1 ( H88 , sc-10768 ) , anti-BAF155 ( R-18 , sc-9746 ) , anti-BAF170 ( E-6 , sc-17838 ) , anti-PR ( H-190 ) ( sc-7208 ) and anti-FOXA1 ( H-120 ) ( sc-22841 ) , and anti-hSNF5 ( C20 , sc-16189 ) from Santa Cruz Biotechnology; anti-CHD8 ( A301-224A ) and anti-BAF180 ( A301-590A ) from Bethyl; anti-BAF250 ( 04–080 ) from Millipore; α-tubulin antibody ( DM1A , T9026 ) from Sigma Aldrich and anti-BRM ( ab15597 ) from Abcam .
|
A lot of research has been devoted during the last decades to understand the mechanisms that control gene promoters activity , however , much less is known about enhancers . Only recently , the use of genome-wide chromatin immunoprecipitation techniques has revealed the existence of more than 400 , 000 enhancers in the human genome . We are starting to understand the importance of these regulatory elements and how they are activated or repressed . In this work we discover that the chromatin remodeler CHD8 is recruited to Progesteron Receptor-dependent enhancers upon hormone treatment . CHD8 is required for late steps in the activation of these enhancers , including transcription of the enhancers and synthesis of eRNA ( long noncoding RNAs derived form the enhancers ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
The Chromatin Remodeler CHD8 Is Required for Activation of Progesterone Receptor-Dependent Enhancers
|
Voluntary movements are widely considered to be planned before they are executed . Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an ‘initial condition’ which seeds the proceeding neural dynamics . Here , we studied these initial conditions in detail by investigating 1 ) the organization of neural states for different reaches and 2 ) the variance of these neural states from trial to trial . We examined population-level responses in macaque premotor cortex ( PMd ) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations . We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach . Further , we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space . We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces ( BMIs ) . We found that decoding of angle between reaches is dependent on reach distance , while decoding of arc-length is independent . Thus , it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them . Lastly , we show that in contrast to the common notion that direction can better be decoded than distance , their decoding capabilities are comparable . These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs .
A central issue in the study of motor preparation has been identifying which aspects of a movement are specified prior to execution and how precisely these aspects are encoded [1–3] . Decades of research have addressed this question with a variety of approaches . Many studies have carefully analyzed aspects of movement execution as a proxy for the preparatory process , for example measuring changes in reaction time to estimate the time it takes to specify different parameters during preparation [1] , measuring error patterns in movements made to memorized targets [4–8] , or assessing the necessity of preparation in forming motor memories [9] . A potential limitation of using behavioral metrics to measure the preparatory process is the difficulty in delineating which aspects of behavior are related to solely preparation versus movement execution itself [8] . Another prevalent approach for studying motor preparation has been to record neural activity in motor cortex prior to movement execution . Several studies on the neural basis of preparation have used an ‘instructed-delay paradigm’ , where a short delay period separates the movement instruction from execution . By determining which aspects of movement are encoded in neural activity during the delay period , the key factors that constitute the preparation process can be inferred . Several studies have shown that the activity of individual neurons in primary motor cortex ( M1 ) and dorsal premotor cortex ( PMd ) during the delay period correlates with reach direction and distance [10–16] , reaction time ( the elapsed time between movement instruction and movement onset [17 , 18] ) , maximum speed [16] , location of the reach target in visual space [19] , target location relative to the eye and hand [20] , and many other aspects of the reach . While single-neuron studies have been instrumental in identifying several response properties in delay activity , they also have several limitations ( see [21] for review ) . Most critically , recording neurons one-by-one precludes understanding how neurons covary with one another trial-by-trial , a crucial feature of how neural populations encode information [22] . Over the past two decades , advances in recording technology have enabled the study of populations of simultaneously recorded neurons [23] . Population-level analyses have yielded several advances in the characterization of delay activity , including relating single-trial population activity to reaction time [24] , relating responses during the delay period to responses during movement execution [25 , 26] , and assessing the necessity of the preparatory state [27] . One viewpoint that has emerged from the study of population-level delay activity is the ‘initial condition hypothesis’ of motor preparation , which posits that delay activity sets a preparatory state which seeds movement-related dynamics [18 , 24 , 28 , 29] . While the dynamics of population activity during movement have been studied extensively [26 , 29–31] , the structure of the neural states that seed these dynamics has not been fully characterized . In this paper , we seek to understand how the structure and variability of population activity during motor preparation give rise to the upcoming movement . To shed light on this issue , we used multielectrode array recordings from monkeys during an instructed-delay reaching task with a dense target configuration . We first characterized the low-dimensional structure of neural activity in PMd during the delay period by describing how this structure is related to the upcoming reach . Then , we investigated the single-trial variability of that structure and found that it resembles the behavioral variability found in previous studies [6 , 7] . Last , we assessed the implication of the neural structure and variability on decoding reach endpoint for brain-machine interfaces .
All procedures and experiments were approved by the Stanford University Institutional Animal Care and Use Committee . To minimize any potential suffering surgeries were performed under isoflurane anesthesia with carefully monitored post-operative analgesia . In this study , we analyzed neural activity from an instructed-delay reaching task ( Fig 1 ) . We trained two monkeys ( J and R ) to perform center-out-and-back reaches on four different target configurations . In all configurations the target acceptance window was a 2 cm box centered on the target location and the required hold time was 500 ms . Delays were distributed uniformly from 300 to 700 ms for J and 400 to 900 ms for R . To encourge the animals to attend to the stimulus during the minimum delay period , 10% of the trials had no delay . Non-delayed trials were removed from the analysis . Subjects failed the trial if it took longer than 5 seconds to reach the target . This time limit was to stop trials in which the monkey was not attentive to the task . The average reach time of successful trials was J: 582 ms , R: 482 ms and the average reaction time was J: 346 ms , R: 311 ms . Also , the reaction time was negatively correlated ( p < 1e-4 for both J and R ) with the length of the delay period as was shown in previous studies [18] . Each configuration included a high target count to enable accurate quantification of our ability to predict movement endpoint and to have a dense sample of the neural states . When comparing two different distributions , we used a two-sided Student t-test ( assuming unequal variances ) with a confidence level of p = 0 . 05 unless otherwise indicated . In figures , we denote * for p ≤ 0 . 05 , ** for p ≤ 0 . 01 , *** for p ≤ 0 . 001 and **** for p ≤ 0 . 0001; n . s . indicates no significant difference . All target configurations are presented in Fig 1b . The first target configuration ( ‘ring’ configuration ) varied only in direction . Targets were evenly spaced in a circular formation with a radius of 8 cm; 24 targets for J ( 6161 trials over four sessions: 2015-10-09 , 2015-10-15 , 2015-10-26 , 2015-10-27 ) and 36 for R ( 3577 over two sessions: 2017-01-24 , 2017-01-25 ) . The ‘horizontal’ and ‘line’ configurations varied distances and direction; 24 targets were arranged in a line with 12 targets both sides of the center spaced 1 cm apart . For horizontal configuration: 5017 trials over four sessions for J ( 2015-09-24 , 2015-09-25 , 2015-09-29 , 2015-09-30 ) , 3699 trials over two sessions for R ( 2017-01-17 , 2017-01-18 ) . For vertical: 6110 trials over four sessions ( 2015-10-01 , 2015-10-02 , 2015-10-07 , 2015-10-08 ) for J and 4040 trials over two sessions for R ( 2017-01-15 , 2017-01-16 ) . The ‘3-rings’ configuration varied both in direction and in distance , but with more directions and fewer distances ( 7722 trials over four sessions for J: 2016-01-27 , 2016-01-28 , 2016-01-29 , 2016-02-02 ) and 912 trials from one session for R: 2017-01-19 ) . Targets were arranged in three concentric rings with radii of 4 , 8 , and 12 cm , each with 16 targets . Monkeys J and R were each implanted with two 96-electrodes Utah arrays ( Blackrock Microsystems , Inc . ) , using standard neurosurgical techniques [32] 74 ( J ) and 64 ( R ) months prior to this study . The arrays were implanted into the left cortical hemisphere in the dorsal premotor cortex ( PMd ) and primary motor cortex ( M1 ) as estimated visually from local anatomical landmarks . Monkey R’s array quality is inferior to monkey J’s , and this difference manifests in the results of our study as well as previous studies ( e . g . [33 , 34] ) . At the time of the study , monkey R’s M1 array was too poor to use; consequently , in our analyses , we only used the recordings from PMd for both monkeys . We combined recordings from multiple days for all of our analyses . Previous reports have provided evidence for the stability of chronic arrays over days and months [35 , 36] . While we do not formally quantify unit stability in the present study , non-stability of the arrays would only add more noise to the data and any unit turnover would end up yielding a null result instead of the results that are presented here . To account for changes in baseline firing rates from day to day , we subtracted from each channel’s instantaneous firing rate its average rate ( for that day ) . Voltage signals from each of the electrodes were bandpass filtered from 250 to 7500 Hz . Multiunit threshold crossings events were then detected whenever the voltage crossed below a threshold set at the beginning of each day ( at −4 . 5 and −4 . 0 x Root Mean Square ( RMS ) voltage for J and R , respectively ) . Contralateral hand position was measured with an infrared reflective bead tracking system ( Polaris , Northern Digital ) polling at 60 frames/s . All analyses were performed offline after data collection . Threshold crossings recorded on the array’s 96 electrodes ( i . e . , channels ) were binned during the 200 ms prior to the go cue . To characterize the quality of each channel , we fit a regression from each channel separately to the task ( x , y location of the target ) . Of the 96 electrodes , we found that 74 for J and 80 for R had significant correlation ( p < . 05 , with Bonferroni correction where n = 96 ) with the task . In this work , we used dimensionality reduction to analyze patterns across the neural population that are not apparent at the level of individual neurons . Our overall aim is to identify prominent structure in population activity and see how this structure relates to the task and movement . Our data begins as an order-3 tensor N ∈ R n × t × k where n is the number of recorded units , t is the number of timesteps recorded , and k is the total number of trials across all tasks and experimental sessions . We first binned the data by taking the average firing rate in a 200 ms time window before the go cue ( during the delay period ) , leaving a matrix N d e l a y ∈ R n × k . To focus our investigation on the dimensions that capture the majority of the variance in our data , we used principal components analysis ( PCA ) , an unsupervised technique . In order to find dimensions that capture variability related to differences between conditions rather than within-condition variability , we performed PCA using the condition-averaged data . That is , we performed PCA using N condition-average ∈ R n × c , where c is the number of distinct reach conditions ( i . e . targets ) across all tasks ( J: 120 , R: 132 ) , mean-centering across conditions before applying PCA . This yields a linear transformation A ∈ R d × n where d is the number of reduced dimensions . The top 6 PCs explained the following cumulative percentages of the variance: 58% 82% 88% 92% 94% 95% ( J ) ; 81% 89% 92% 94% 95% 96% ( R ) . We kept the top 6 , 5 ( J , R ) principal components in order to retain 95% percent of the variance . Though we performed PCA using the condition-averaged data , we used the resulting A transformation to reduce the dimensionality of individual trials ( i . e . ANdelay ) . After reducing dimensionality with PCA , we rotated and scaled ( using regression ) the principal components to find the subspace that best explains the x and y position of the target . Concretely , we found βx , y [ x n e u r a l y n e u r a l ] = β x , y A N d e l a y ( 1 ) where βx , y minimizes the squared error between [ x n e u r a l y n e u r a l ] and the target location [ x y ] . In order to compare potential coordinate systems , we also regressed to direction ( represented as a unit vector in the target direction ) and distance separately . Additionally , we found a projection that best explains the maximum speed achieved during each reach . Unlike in the regression to target location , for the speed projection , we regressed directly from the neural activity rather than the reduced-dimensional data . Since the PCs were derived using condition-averages of the neural data , trial-to-trial variability related to speed was averaged over; repeating PCA on condition-averages for speed would have required explicit conditions for different speeds . Thus , the equations defining the speed regression: [ s p e e d n e u r a l ] = β s p e e d N d e l a y ( 2 ) where βspeed is chosen to minimize squared error between [speedneural] and the true maximum speed on each trial . In order to maintain independent dimensions ( as in PCA ) , we orthogonalized the coefficients of the linear mapping for each variable ( x , y and speed ) . This was done by sequentially fitting the regression coefficients ( for x , then y , then speed ) , but between each fit , we projected the neural data into the null-space of the previous regression coefficients . In practice , this process did not change the projections much , as the projections found without orthogonalization were also nearly orthogonal . For classification , we used support vector machines ( SVMs ) . SVMs are classifiers that construct a separating hyperplane between data points belonging to different classes , making a decision boundary with the greatest distance between the boundary and the data points . Multiclass classification was handled with one-vs-one classification . We used the scikit learn library implementations of SVMs [37] . We found the best kernel for the SVM to be a radial-basis function . All hyperparameter searches were performed on a held-out dataset of 2237 trials from the 3-rings task configuration . All reported classification accuracies are the mean accuracy across 10-fold cross validation , and errors are the standard error of the mean ( SEM ) across folds .
To study the structure of delay activity in PMd across different reaching conditions , we represented population responses during the delay period of a trial ( the average activity in the last 200 ms ) as a point in the 96-dimensional neural state space . We used PCA on the combined datasets ( tasks and sessions ) to identify primary patterns of activity in an unsupervised fashion . Projecting the condition-averaged neural activity to the top principal components revealed a clear 3D structure between neural states for different reaches ( top 3 PCs for monkey J shown in Fig 2a , top row ) . In one plane the condition-average neural states appeared to be organized by their target position , and in an orthogonal dimension by the distance of the upcoming reach . These apparent dimensions were mixed among the top principal components . In order to reason about this structure quantitatively , we would like to define axes that are aligned with these dimensions . To do so , we simply rotated the space defined by the top PCs using regression to task variables ( see Methods ) . However , it is not entirely clear which aspects of movement the neural states are organized by , and thus which variables to regress to . In particular , we cannot infer from the condition averages alone whether the structure is related to task/visual parameters ( e . g . location or distance of the target ) or a plan for the upcoming movement ( reach endpoint , speed ) . To disambiguate between these possibilities , we 1 ) compared different candidate coordinate systems and 2 ) show that the single-trial neural state correlates with aspects of the reach , suggesting that the structure is related to the plan for the upcoming movement . We first oriented the space with respect to the x , y position of the target by defining a ‘spatial plane’ . To define the ‘spatial plane’ ( Fig 2a bottom row , gray plane ) which reflects the x , y position of the target , we used linear regression on the top PCs ( 6 for J , 5 for R; see Methods ) to yield two orthogonal axes , xneural , and yneural . Thus , xneural , and yneural are each just a linear combination of the top PCs ( Fig 2b ) . The spatial plane captures a large percentage of the overall variance of the trial-averaged neural responses for the ring , horizontal , vertical , and 3-rings tasks: 66 . 8% , 64 . 4% , 47 . 7% , and 68 . 2% ( monkey J ) , 33 . 7% , 30 . 7% , 31 . 0% , 30 . 1% ( monkey R ) for each task , respectively ( p < 1e-4 for both monkeys ) . The difference between the monkeys is likely a result of the array quality difference ( see Methods ) . Interestingly , at target onset , the neural states projected onto this plane start at the center of this plane , start to diverge after 50 ms ( R: 75 ms ) , and converge to the steady neural states after J: 300 ms , R: 400ms ( Fig 2c ) . It is worth noting that reach direction and distance are encoded together as x , y coordinates , rather than in orthogonal subspaces . This joint encoding was not simply a consequence of the method of constructing this subspace ( regressing to x , y ) ; even when the population activity is regressed only to a unit vector in the target direction ( without distance information ) , the resulting neural states are structured nearly identically to the structure in Fig 2a . To show that the neural state on the spatial plane is predictive of the upcoming reach and is not merely a result of the visual stimuli , we examined the relationship between the neural states and reaches on individual trials . In particular , we performed a trial-by-trial correlation between on-/off-axis variability of the neural state and on-/off-axis kinematic variability . Rather than measure kinematic variability at the endpoint of the reach ( which is influenced by visual feedback ) , we measured variation at the early , largely feedforward , part of the reach . Previous work demonstrated that the spatial position of maximum speed is predictive of reach endpoint in the absence of visual feedback [8] , so we compared the deviations in the neural state space to deviations in the spatial position of maximum speed ( Fig 2d ) . By deviations , we mean the displacement of a trial from their condition average: the average neural state or the average position of maximum speed for reaches to a given target . To disambiguate deviations in distance from deviations in direction , we decomposed the deviations into two components: ‘on-axis’ deviations , which measures variation in distance , and ‘off-axis’ deviations , which measure variation in distance and direction , respectively [6 , 7] . Both on-axis ( p = 1e-3 for J , 4e-3 for R ) and off-axis ( p = 4e-4 for J , 2e-4 for R ) deviations in the neural state were positively correlated with on-axis and off-axis deviations in the position of max-speed for both monkeys . Thus , the neural state in the spatial plane is predictive of the maximum-speed position ( and the planned end-point position ) of the upcoming reach . Next , we investigated the third dimension , on which the neural states are organized based on the ( absolute ) target distance . During natural reaches , reach distance and maximum speed are known to be highly correlated [6 , 38]; thus , it is unclear from the structure seen in the condition-averaged neural states whether this third dimension encodes target distance or reach speed . We resolved this ambiguity by testing whether the neural state on single trials carries speed-specific information . For reaches to a given target distance , there is trial-to-trial variability of maximum speed . If speed is encoded in delay activity , the neural state on individual trials should covary with speed even for reaches to the same target distance . Thus , we controlled for the correlation with distance by examining whether the neural state on single trials carries information related to speed . We first defined a 1-dimensional projection of the neural activity ( similar to the construction of the spatial plane ) by regressing between the high-dimensional neural state and the single-trial maximum speed . We then tested whether the neural state in this dimension varied with speed for reaches to the same target , and found that for almost all distances it did ( J: 11/12 distances in the vertical and horizontal tasks , R: 11/12 , p < 0 . 05 ) , confirming that this axis encodes speed independently of target distance ( Fig 2f ) . However , this does not preclude the existence of ( speed-independent ) distance related information; indeed , we found that after regressing out speed from the neural activity , there is still a positive correlation ( p < 1e-4 for J and R ) with target distance . While both speed and distance dimensions exist in the neural activity , the structure observed in the top principal components ( Fig 2a ) more strongly reflects the speed dimension . That is , when projecting the speed and distance dimensions onto the top PCs , the distance projection is approximately half compared to the projection of the speed dimension . We note that this test was possible without instructing slow/fast reach conditions as in [16] because the simultaneous recording of many neurons afforded us the statistical power to estimate speed from neural activity . However , due to the lack of reach-speed conditions , we cannot report the percentage of variance this dimension captures for condition-averaged neural states ( as we did for the spatial plane ) . This dimension explains J: 2% , R: 1% of the variance in the neural state across all trials; for reference , the spatial plane explains J: 20% , R: 10% for single trials , lower than the condition averages due to within-condition variability . Previous behavioral studies have provided detailed quantification of the variability of motor preparation by using reaches made in the absence of visual feedback as a proxy for the prepared movement [6 , 7] . Their main findings were that the variability ‘on-axis’ ( distance ) was larger than ‘off-axis’ ( direction ) , and that these variabilities decreased ( relative to reach distance ) as reach distance increased . In the present study , we can observe the trial-by-trial neural state during preparation directly , and measure variance within the spatial plane described in the previous section . In the following section , we test whether the properties of preparation variability observed behaviorally are also observed in delay activity by performing analyses parallel to the referenced behavioral studies but in neural state space . We characterized ‘spatial’ distributions in neural state space in a similar manner to how [6] and [7] measure endpoint distributions . First , we used PCA to determine the axis of maximal variance for each reach condition . We visualized the distribution for each condition as an ellipse whose major and minor axes correspond to the first and second principal components , respectively; distributions for the 3-ring task are shown in Fig 3a . The axes of the ellipses are scaled relative such that , on average , 95% of the neural states fall within the ellipse . Much like the endpoint distributions of reaches made in the absence of visual feedback in previous behavioral studies [6–8] , we found that the ellipses for most conditions were oriented in the direction of the mean neural state from the origin . To quantify the difference in distance and direction variability , we decomposed the deviations of single trials into their ‘on-axis’ ( distance ) and ‘off-axis’ ( direction ) components ( as in the previous section ) and calculated the overall variance of these components separately . The mean absolute on-axis deviation for each target and for each monkey was systematically greater than the mean absolute off-axis deviations in the tasks ( p = 9e-7 ( ring ) , 4e-7 ( horizontal ) , 1e-8 ( vertical ) , and 3e-14 ( 3-rings ) for J , 8e-3 , 4e-7 , 2e-7 , 0 . 19 for R ) . We did not observe a significant result in monkey R 3-ring task likely due to the significantly smaller trial count . In sum , deviations in distance were larger than deviations in direction . In addition to showing that the spatial distributions of reach endpoints are elliptical , [6] and [7] also analyzed the influence of movement distance on variability in both direction and distance . In both studies , they computed the relative error , meaning the deviation normalized by reach distance . They found that the relative errors in both direction and distance decreased as reach distance increased . To assess whether the neural state variability is likewise influenced by reach distance , we performed similar analyses by testing whether on-axis and off-axis deviations in the neural state were affected by reach distance . Similar to those studies , we found that the relative deviations in both distance and direction in the neural state decreased as a function of reach distance ( Fig 3b and 3d ) . Thus , several properties of the variance of reach endpoint [6 , 7] are likewise observed in the neural state preceding reach . In the previous sections , we looked at the structure and variance of delay activity via dimensionality reduction . We next sought to understand how this structure and variance affects the accuracy of decoding cued target location from delay activity , as in discrete brain-machine interfaces ( BMIs ) [39–41] ) . Most studies describe distance and direction as the main two factors that are prepared for the upcoming reach . Previous single-neuron studies have suggested that neurons have weaker tuning for distance compared to direction [14 , 16] . Following this observation , BMI studies have typically used radial target layouts to maximize correct predictions ( and communication rate , e . g . [40] ) . However , our results on the structure and variance of delay activity imply that measuring decoding accuracy for direction and distance separately may be misguided; for example , from Fig 3a it seems that distinguishing between two targets with the same angular separation would be easier at further distances . If we compare decoding accuracy for direction v . s . distance , the results can be hard to interpret or misleading . For example , when we compared the classification accuracies for classifying the ring and horizontal/vertical datasets ( using SVMs—see Methods ) , direction was J:32 . 2% ± 0 . 6% accuracy for 24 targets ( R:16% ± . 5% for 36 targets ) while distance classification for only 12 targets was lower for J ( 18 . 9% ± . 1% ) and comparable for R ( 21 . 4% ± . 2% for R ) . As classification accuracy depends on the number of targets , we can instead compare the distribution of classification errors , which gives a more complete description of the classifier’s performance than overall accuracy and is less sensitive to the total number of targets . In comparing the error distributions ( Fig 4a and 4b ) , distance classification appears to suffer from more large-magnitude errors than direction . However , from Fig 3 it seems that distinguishing between two targets with the same angular separation would be easier at further distances . Indeed , classifying direction yields higher classification accuracy at further distances ( Fig 4c ) ; when classifying targets in 3-rings separately for each distance , accuracy increases with ring distance ( 23% , 36% , 43% for J; 22% , 28% , 29% for R ) . Thus , quantifying decoding performance in terms of direction and distance is misleading , as classification accuracy for direction decoding depends on reach distance . Two factors without interaction would be a better choice from the point of view of quantifying decoder performance , and could be useful for designing decoders or target interfaces . Surprisingly , we found that quantifying directional decoding performance in terms of arc-length error provides a measure that is independent of target distance . When presenting the decoding accuracy in term of angular error , mean error decreases as a function target distance ( Fig 4c ) . In contrast , when presenting the errors as arc-length ( scaling the angle based on the radius ) , the arc-length error distributions are not significantly different ( F test; p > . 05 for both J and R ) , i . e . the arc-length errors are distance independent . Thus , arc-length provides a measure of decoder performance that is independent of distance . Another benefit of presenting the directional decoding accuracy as arc-length is that it has the same unit as distance ( i . e . cm ) permitting direct comparison between the two . We plotted the mean error from the distance classification in Fig 4d . Surprisingly , we found that the mean classification error for distance classification was significantly ( albeit only slightly ) lower than the errors for directional classification . In contrast to previous claims [40] , this result suggests that decoding distance from delay activity is at least as accurate as decoding direction . This is surprising given the difference in variance between distance and direction in Fig 3 , but may be explained by the presence of information related to speed during preparation [16] which is correlated with distance during natural reaches [6 , 38] . To test this , we projected the delay activity to the spatial plane ( shown in Fig 2 ) prior to classification to remove speed information . The resulting classification error rate ( Fig 4d , blue bars ) is significantly greater than classification using the full dimensional neural activity ( 3 . 03 cm v . s . 2 . 64 cm J , 2 . 78 cm v . s . 3 . 27 cm R; p < . 05 for J and R ) , and is not significantly different than the directional classification errors ( p > . 05 for each distance in J and R ) . This finding is odd given the structure of the variances of the previous section . The SVM algorithm is likely unable to take advantage of the albeit modest differences between the variances . Perhaps future work that employs non-linear methods that specifically take advantage of this difference might show more changes .
Reaching to a visual target necessarily involves a series of visuomotor transformations beginning in the coordinate system of the retina and resulting in an appropriate motor response . The details of when and where visual information is transformed into abstract kinematics , action selection , and movement specification have yet to be settled . Several observations have been consistent with PMd encoding ‘high-level’ aspects of reaching not directly related to movement specification: neurons in PMd respond selectively for the location of the target in visual space [19] , correlate with upcoming reaches with the ipsilateral arm [42] , and represent movement in a variety of reference frames [20] . Other studies have shown that PMd activity during movement changes when reach kinematics are fixed but muscle activity differs , suggesting that neurons in PMd support ‘low-level’ movement specification [43] . While we describe population activity with reference to kinematic variables , it is not the aim of our study to provide evidence for whether PMd encodes abstract kinematics or muscle-like commands . Instead , the primary goal of this work is to determine the structure of population-level delay activity; we use the language of abstract kinematic variables ( x , y position of reach endpoint and maximum speed ) to describe the subspace of delay activity , but we note that these abstract variables are correlated with lower-level aspects of movement [44 , 45] . To better assess whether the structure observed in delay activity more strongly resembles abstract kinematics or muscle commands , future work could employ a similar strategy to [43] whereby subjects make reaches with similar kinematics ( e . g . hand paths ) but with different muscle forces ( e . g . arm postures ) . If the population responses retain the structure in Fig 2a despite different muscle output , this would support the notion that delay activity encodes abstract kinematic variables . If , instead , the organization changes or delay activity occupies a different subspace when movement specification changes , then delay activity is likely more related to the particular muscle activations or joint trajectories required for movement execution . The issue of whether direction and distance are ‘independent’ quantities during motor planning has long been a subject of debate in both behavioral and neurophysiological studies . Our study provides further details about the relationship between direction and distance by examining their representations at the population level . First , as is discussed in the results , the neural states for reaches to different targets are organized according to reach endpoint ( in Cartesian coordinates ) , rather than distance and direction separately . Indeed , when we searched for a subspace related to solely direction ( without distance information ) , we could not find such a subspace , suggesting that direction is only encoded in conjunction with distance . However , it is clear from the distribution of neural states within these dimensions ( Fig 3 ) that direction and distance have different noise properties—The encoding of distance in the neural state is more variable than the encoding for direction . Thus , there is still a notion of direction and distance as separate quantities in the population . In sum , our results suggest that the relationship between direction and distance encoding in planning activity is not simply ‘independence’ or ‘dependence’ , but a combination of the two . The ‘initial condition hypothesis’ of motor preparation posits that the preparatory state seeds movement-related dynamics [18 , 24 , 28 , 29] . One way to view the present work is as an extension of the initial condition hypothesis: we describe how the initial conditions for different reaches are organized with respect to one another . The radial organization of neural states had been previously reported in studies using center-out reaches that varied solely in direction [26 , 34 , 46] . By using target layouts that varied in both direction and distance , we observed further organization of the neural states by 1 ) the x , y location of the reach endpoint and 2 ) maximum speed . Though the organization of these initial conditions provides a signature of the dynamical system , much work remains before the dynamics of movement planning are fully characterized . In particular , while we have described a few primary components of delay activity , a comprehensive model needs to fully describe the relationship between other aspects of movement ( e . g . reaction time [24] , posture [47] ) and the structure described here . Additionally , we hope that future studies will investigate the computational purpose of the organization of delay activity . For example , future computational experiments could assess under which task conditions this structure is observed by e . g . training a recurrent neural network to perform similar tasks [26 , 31 , 48] . In our work we sought to understand a simple 2D center-out reach task . We found that the main three parameters encoded in PMd during delay activity are x , y position of reach endpoint and maximum speed , which are a sufficient set of parameters to specify a ballistic reach . The low-dimensionality of the neural space might be a result of the low dimensionality of the task . We believe that the characterization of how neural states are distributed within these dimensions is of value , both for insights into how behavioral variability is generated and for improving decoding for BMIs . In more complex tasks such as reaching in high dimensional space , reaching with obstacles , movement in a maze , or a sequence of reaches , a larger set of parameters are required to define the upcoming reach ( such as the curvature or additional dimensions for the spatial subspace ) . In those tasks , we might expect to encounter neural states during the delay period with a greater number of dimensions than observed in the present study [49] , and we expect that using similar approaches to those employed in our study with higher dimensional tasks will lead to further insights . We , and many others , have observed correlations between neural activity during the delay period and aspects of the subsequent reach . In particular , we have shown that neural states during the delay are predictive of the upcoming movement on single trials and that ‘spatial’ variability of these states is similar to the variability observed in behavioral studies in the absence of visual feedback [6–8] . The resemblance between the variability of the neural state and the variability of movement suggests that the movement variability is a consequence of the preparatory process rather than e . g . properties of movement execution . That said , our results do not provide a causal link between the delay state and the following movement; indeed , recent studies have questioned the necessity of delay activity for motor preparation [27] . To concretely demonstrate that movement execution is determined by neural activity during the delay , future studies could perturb delay activity along specific dimensions in the neural state ( Fig 2a ) and observe whether the following reach is similarly perturbed . Our results show that direction and distance are decodable with comparable accuracy during movement preparation , suggesting that discrete BMI performance is likely affected by the total spatial distance between targets . Discrete decoders , which use neural activity to classify the most likely movement endpoint from a discrete set of possible endpoints , show promising results for communication [39–41 , 50] . We emphasize that just because decoding accuracy depends on the total spatial distance between targets does not imply that activity in PMd solely encodes reach endpoint . Instead , we propose that the encoding of distance is more variable than direction ( Fig 3 ) , but the speed dimension ( Fig 2a ) provides additional information which makes up for this disparity . We confirm this by demonstrating that when speed information is removed , distance decoding worsens ( Fig 4d ) . It follows that this should only be the case because of the correlation between reach distance and speed during natural reaches . If future work did similar experiments but with different instructed speeds ( e . g . requiring slow movement to further targets and quick movement to nearby targets , thus reversing the typical correlation between speed and distance ) , we would predict that direction would indeed be more decodable than distance . We also acknowledge that prior studies [51] have compared direction and speed decoding from neural activity during movement execution and concluded that direction is decodable with higher accuracy than speed . Thus , our finding that direction and distance are decodable with comparable accuracy may be specific to delay activity , and thus may be more applicable for discrete BMIs . Nonetheless , it would be interesting for future studies to attempt to better understand why these differences exists between movement preparation and execution . In prior work on target-layout optimization for BMIs [52] , gains in decoder performance were proposed by optimizing target placement based on the tuning properties scaled by distance of the recorded neurons . Based on our results , a model that also includes a separate distance term ( which can be indirectly influenced from planned max velocity ) , in addition to the x , y location of the target , might increase decoding performance and better utilize a given workspace for communication . Future experiments with different target layouts could verify the optimal target layout .
|
Early studies of premotor cortex explored how individual neurons directly encode aspects of an upcoming movement during preparation . Recent developments have proposed that the dynamics of populations of neurons underlie motor control , and that neural activity during preparation serves to set up these dynamics . While the dynamics of motor control have been studied extensively , several aspects of preparatory activity remain unresolved . Here , we ask how the patterns of neural activity during preparation for different reaches are related to one another . We found that the neural activity during preparation for reaches to different targets has a clear ‘structure’ . Additionally , we found that the activity on a given trial was predictive of the initial trajectory of the reach . Lastly , we assessed the implications of our findings for predicting upcoming movements from neural activity , as in brain-machine interfaces .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"ellipses",
"classical",
"mechanics",
"reaction",
"time",
"statistics",
"social",
"sciences",
"vertebrates",
"neuroscience",
"animals",
"mammals",
"geometry",
"primates",
"multivariate",
"analysis",
"cognitive",
"neuroscience",
"mathematics",
"artificial",
"intelligence",
"vision",
"kinematics",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"monkeys",
"animal",
"cells",
"mathematical",
"and",
"statistical",
"techniques",
"principal",
"component",
"analysis",
"support",
"vector",
"machines",
"physics",
"machine",
"learning",
"psychology",
"cellular",
"neuroscience",
"eukaryota",
"cell",
"biology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"sensory",
"perception",
"physical",
"sciences",
"cognitive",
"science",
"amniotes",
"statistical",
"methods",
"organisms"
] |
2019
|
Structure and variability of delay activity in premotor cortex
|
The prevalence of Wuchereria bancrofti , which causes lymphatic filariasis ( LF ) in The Gambia was among the highest in Africa in the 1950s . However , surveys conducted in 1975 and 1976 revealed a dramatic decline in LF endemicity in the absence of mass drug administration ( MDA ) . The decline in prevalence was partly attributed to a significant reduction in mosquito density through the widespread use of insecticidal nets . Based on findings elsewhere that vector control alone can interrupt LF , we asked the question in 2013 whether the rapid scale up in the use of insecticidal nets in The Gambia had interrupted LF transmission . We present here the results of three independently designed filariasis surveys conducted over a period of 17 years ( 1997–2013 ) , and involving over 6000 subjects in 21 districts across all administrative divisions in The Gambia . An immunochromatographic ( ICT ) test was used to detect W . bancrofti antigen during all three surveys . In 2001 , tests performed on stored samples collected between 1997 and 2000 , in three divisions , failed to show positive individuals from two divisions that were previously highly endemic for LF , suggesting a decline towards extinction in some areas . Results of the second survey conducted in 2003 showed that LF was no longer endemic in 16 of 21 districts surveyed . The 2013 survey used a WHO recommended LF transmission verification tool involving 3180 6–7 year-olds attending 60 schools across the country . We demonstrated that transmission of W . bancrofti has been interrupted in all 21 districts . We conclude that LF transmission may have been interrupted in The Gambia through the extensive use of insecticidal nets for malaria control for decades . The growing evidence for the impact of malaria vector control activities on parasite transmission has been endorsed by WHO through a position statement in 2011 on integrated vector management to control malaria and LF .
The Gambia is among 73 countries currently considered endemic for lymphatic filariasis ( LF ) by the World Health Organization [1] . LF , a neglected tropical disease ( NTD ) , is a debilitating mosquito-borne nematode infection that affects 120 million people in low and middle income countries where 1 . 4 billion people are exposed to the parasites [1] . Wuchereria bancrofti , the causative agent of LF in The Gambia , is responsible for over 90% of the LF infections worldwide; Brugia malayi and Brugia timori account for the remaining infections and have a distribution restricted to the southeast Asian region [2] . The LF parasites are carried by various species of mosquito vectors from the genera Anopheles , Aedes , Culex and Mansonia but in sub-Saharan Africa , Anopheles species are the principal vectors[3] . There is no evidence that Culex species play any significant role in West Africa where the malaria vectors , An . gambiae s . l and An . funestus , are also the vectors of W . bancrofti [3–9] . In 1997 , LF was targeted for elimination when the World Health Assembly adopted Resolution WHA 50 . 29 calling for the elimination of the disease as a public health problem globally [10] . In 2000 , WHO , in collaboration with pharmaceutical companies and implementing partners launched the Global Programme to Eliminate LF ( GPELF ) as a disease specific intervention initiative to interrupt transmission and alleviate morbidity[11] . The GPELF has two strategic objectives for achieving this goal: 1 ) interruption of parasite transmission through mass drug administration ( MDA ) using albendazole in combination with either ivermectin or diethylcarbamazine citrate ( DEC ) and 2 ) morbidity management and disability prevention ( MMDP ) by providing access to care for those who suffer clinical manifestations of LF in endemic areas . By 2012 , 56 of the 73 countries where LF is considered endemic had started implementing MDA to eliminate the disease [1] . Among countries implementing MDA , only 13 countries had completed at least five rounds of MDA and moved to post-MDA surveillance phase [1] . Nevertheless , the scale up in the use of insecticidal nets and vector control in Africa , where 17 countries including The Gambia are yet to start MDA , has significantly reduced mosquito densities in many of these countries and contributed to a significant reduction in malaria prevalence [12 , 13] and a possible decline in filariasis endemicity [3 , 7 , 14 , 15] . In Solomon Islands where LF was transmitted by Anopheles mosquitoes , anti-mosquito measures to control malaria resulted in the interruption of LF transmission in the absence of MDA [16–18] . The Gambia had historically high prevalence of LF as described by Hawking [4 , 5] in his historical reviews of the distribution of filariasis in Africa where he stated that the prevalence of W . bancrofti among adults in the Gambia in the 1950s [19 , 20] was about 50%—one of the highest in the world . Prevalence surveys conducted in 17 villages across the country in 1975 and 1976 reported village specific microfilaraemia ( MF ) rates for people ≥15 years ranging from 2 . 9% to 26 . 9%; and the apparent decline after 25 years was mainly attributed to a reduction in mosquito density[21] . Nevertheless , MF positive children ( <15 years ) were present in many villages in the Upper River , Western and Lower River divisions where LF was still highly endemic . WHO estimates that 1 . 2 million people require preventive chemotherapy against LF in The Gambia [22] . In line with the growing momentum to shrink the map of LF endemicity , we performed transmission assessment surveys ( TAS ) in The Gambia in May and June 2013 to determine if the widespread use of insecticidal bed nets and other vector control efforts over the past decades had eliminated transmission of W . bancrofti in the absence of MDA [12 , 14] . The increasing momentum to take the “neglected” out of NTDs is driving public and private partners including drug companies , donors , and governments committed to what is now referred to as the 2012 London Declaration to shrink the NTD map and eliminate or eradicate by 2020 ten NTDs including LF [23] . The commitments made to date have been centred on ensuring the supply of drugs needed to implement preventive chemotherapy with limited emphasis on alternative intervention strategies such as vector control through the use of long lasting insecticidal nets ( LLINs ) or indoor residual spraying . Demonstrating the interruption of active transmission of W . bancrofti in The Gambia , in the absence of MDA , could have wide ranging public health and policy implications with regard to alternative strategies and synergies between malaria and NTD control programmes . In this paper we report the application of a new WHO methodology , the Transmission Assessment Survey ( TAS ) , to validate the interruption of transmission in the absence of MDA against the disease in The Gambia after two mapping surveys carried out 15 years earlier suggested a dramatic decline in LF endemicity following a rapid decline in transmission and the possibility that elimination in the country could be achieved . The results presented here were collected over a period of 17 years ( 1997–2013 ) , from three independently designed surveys , involving over 6000 subjects in 21 districts across all administrative divisions in the country .
The Gambia is a country in West Africa situated on either side of the Gambia River which flows through the country's centre and empties into the Atlantic Ocean . It lies between latitudes 13° and 14°N , and longitudes 13° and 17°W , and stretches inland for approximately 400 km . The Gambia is less than 48 . 2 km wide at its widest point , with a total area of 11 , 295 km2 and is surrounded by Senegal . It is the smallest country on mainland Africa with a population of 1 , 882 , 450 . The climate is typically sub-Sahelian with one short rainy season from June to October . The country is presently divided into eight administrative divisions , including the national capital , Banjul . Prior to 2000 , there were only 6 divisions in the country: Banjul , Western , North Bank , Lower River , Central River and Upper River . To retain geographic context with regard to the historical distribution of LF in The Gambia we will adhere , in this paper , with the former administrative boundaries demarcating 6 administrative divisions as shown in Fig . 1 . This is an observational study comparing historical prevalence from studies conducted in the 1950’s and 1970’s ( Fig . 2 ) with 3 cross sectional surveys conducted in 2001 , 2003 and 2013 examining LF infection through the distribution of circulating filarial antigen ( CFA ) positive individuals , involving over 6000 children and adults in 21 districts across all administrative divisions in The Gambia . The rapid Immunochromatographic tests ( ICT ) were used to test samples collected during the three surveys described above . At the point of care , the ICT card test was performed on whole blood or serum as described in the WHO monitoring and impact assessment manual [24] and following manufacturer’s instructions . The ICT cards used in 2001 and 2003 were produced by AMRAD ICT , New South Wales , Australia[28] while the cards used for the TAS surveys in 2013 were manufactured by Binax NOW Filariasis ICT card test ( Alere Inc . , Scarborough , ME ) . Both the AMRAD and Binax NOW ICTs were based on the same reagents and required the same quantify of sample volume ( 100 μl blood/serum ) for testing . A positive [28]antigen control was used to test the validity of the ICT cards before the start of the survey in 2013 . Data generated before 2013 was managed through a simple spread sheet that calculated percentages for comparative analysis . The TAS data was managed through a Microsoft Access based data management system specifically developed by the Centre for Neglected Tropical Diseases ( CNTD ) in the Liverpool School of Tropical Medicine ( LSTM ) to support the cluster survey . Data entry was carried out by two independent clerks using a double entry system that automatically compared the two entries to detect and reconcile any discrepancies . The TAS critical cut-off value represents the threshold of infected individuals below which transmission is expected to be no longer sustainable , even in the absence of interventions . If the total number of positive cases is at or below the critical cut-off , the EU ‘passes’ the survey and it is considered that transmission will no longer be sustainable . If the total number of positive cases is above the critical cut-off transmission is still ongoing in the evaluated unit[24] . TAS sample sizes and critical cut-off values are powered so that the EU has at least a 75% chance of passing if the true antigen prevalence is half the threshold level ( 2% for Culex , Anopheles , and Mansonia vector areas , and 1% for Aedes vector areas ) . In addition , there is no more than a 5% chance of passing if the true prevalence is greater than or equal to the threshold level . Ethical clearance for the study was granted by The Medical Research Council ( MRC ) Laboratories Gambia Scientific Coordinating Committee , the LSTM Research Ethics Committee ( Research Protocol ( 11 . 89R ) and the Gambian Epidemiology and Disease Control Unit ( EDC ) , of the MOH ( correspondence 23 August 2012 ) . After approval for TAS from the Education Ministries and school authorities , the MOHSW team met with the Head Master of each school to obtain permission to conduct the survey and to schedule a date for meeting with the parents . The survey team explained the purpose of the surveys and received oral consent from teachers and parents . Non-consenting parents or non-assenting children were not included in the survey . All individuals could drop out from the study any time during the study .
The results of the surveys described here are best presented in the context of the historical distribution and endemicity of LF that informed the design of the surveys carried out to determine the pattern of LF burden and distribution in the absence of MDA . Fig . 2 , adapted from the results of Knight [21] shows the burden and distribution of MF in 15 villages across five of the 6 divisions involved in this study carried out in the 1970s . Villages with MF rates of 18% or higher were found only in the Upper River and Western divisions . The MF rates in study villages in the other divisions were lower than 10% . The initial assessment of LF endemicity was carried out in the Upper River and Western Divisions historically known to be highly endemic and the North bank where the MRC malaria project was based . In 2001 a total of 268 stored serum samples collected between 1997 and 2000 from people ≥12 years residing in three villages ( Basse , Brefet , Farafenni ) in the three divisions , were tested with ICT for the presence of CFA . CFA positive individuals ( n = 8 ) were only found in Basse village in the Upper River Division where 100 samples were tested . All 68 samples from Brefet ( Western Division ) and 100 from Farafenni ( North Bank Division were negative for CFA . Results are shown as stars in Fig . 3 . The red star indicates the location of the village where CFA positives were found in the Upper River Division . The 8% CFA rate observed for the endemic division corresponds to between 2% and 4% MF rate [29] indicating 89–91% reduction in MF rate over 20 years in the absence of MDA in comparison to the 21% ( 32/148 ) MF rates observed in the same area in 1976 by Knight [21] . The MF rates in the Western division decreased from 15% ( 66/439 ) in 1976 to 0% ( 0/68 ) in 2000 . The results of the national filariasis mapping survey conducted in 2003 are presented in Fig . 3 with circles showing the location and endemicity status of the 30 villages tested in 21 Districts across all 6 divisions . Altogether , 3113 individuals age ≥15 years were tested from the different divisions: Central River ( 1156 ) , Lower River ( 391 ) , North Bank ( 682 ) , Upper River ( 423 ) and Western ( 561 ) . CFA positive individuals were found only in 9 villages located in 5 districts outside the Central River Division where historically the MF rates had been low ( Fig . 2 ) . Among the 9 villages with CFA positives , 6 were located in the Western and Upper River Divisions where the highest MF rates were found during the 1975 and 1976 surveys ( Figs 2 and 3 ) . The remaining three villages with CFA positives were located in the North River and Lower River divisions not included in the historical surveys . In 2013 , TAS was conducted in 60 randomly selected schools in all Divisions in The Gambia , 30 in each of the two EUs , to assess if active transmission of W . bancrofti was ongoing . In total , 3180 6–7 year-old children ( 1509 boys and 1671 girls ) were tested using ICT and none was found to be CFA positive . 1516 children were tested in EU1 and 1664 on EU2 . Fig . 4 shows the location of schools tested during the TAS . The changing pattern of LF infection rates determined by night blood and ICT surveys across the 6 administrative divisions in the Gambia between 1951 and 2013 is presented in Table 1 .
The TAS performed in this study verified the absence of transmission in all administrative divisions that were previously highly endemic for LF . The prevalence of W . bancrofti in The Gambia was among the highest in Africa , based on historical data [20 , 21 , 30–32] and reported in the first global atlas of LF compiled by Michael and Bundy in 1997 [33] and the African LF risk map of Lindsay and Thomas [30] . Village specific MF rates reported in studies carried out in the 1950s , among people 10 years or older , varied between 24 . 1% and 48 . 4% [20 , 31 , 32] and Hawking[4 , 5] later reported that the prevalence of LF among adults during the early 1950s was about 50% . Based on our current knowledge of the relationship between MF rates and antigen prevalence such figures suggest that the majority of adults were infected with the parasite in the 1950’s . Night blood surveys conducted in 15 villages in 1975 and 1976 revealed MF rates between 2 . 9% and 26 . 9% among adults ≥15 years [21] . The corresponding high prevalence of LF morbidity observed in children and adults in The Gambia in the 1970s confirmed that transmission had continued in the twenty years since the first surveys in the 1950s [21] . In adults older than 15 years , morbidity presented in the form of adenolymphangitis ( 13 . 8%–20 . 0% ) , lymphoedema ( 5 . 3%–10 . 0% ) and hydrocele ( 11 . 7%–35 . 3% ) [21] . Late filarial disease in the form of elephantiasis and hydrocoele was also observed in children younger than 15 years [21] suggesting early exposure and high levels of transmission . A multi country operational research study undertaken in 11 EUs in Africa and Asia in 2011 and 2012 , to test the value and practicality of the TAS tool , concluded that it was a practical and effective tool for evaluating interruption of transmission [27] . In 10 of 11 EUs surveyed during the study , the number of CFA positive cases was below the critical cut-offs of 18 , leading to a cessation of MDA in these communities . The low CFA rates ( ≤3% ) observed in the previously highly endemic divisions , during the 2003 mapping surveys , therefore implies a significant reduction in MF rates in patent infection in The Gambia between 1976 and 2003 . A 9% CFA rate observed by Gass and colleagues [29] in a highly endemic area in Ghana corresponded to a 2% MF rate . Also , previous studies have shown that stored sera can remain antigen positive even after 10 years in storage [34] . In 2013 , all 3180 children tested during the TAS surveys were found to be negative for CFA suggesting an interruption of the transmission of W . bancrofti in the absence of MDA . Results of the mapping surveys conducted in 2003 indicated that MDA for LF was required for 5 districts where CFA rates were 1% or higher [24] . No ‘endemic’ districts were found in the Central Division where the intensity of transmission was historically lower [17] . MDA was however not implemented by the MOHSW [1] . LF is a chronic infection perpetuated by the continuous production of microfilaria by lymphatic dwelling adult worms that can live for 4–6 years [35] . If competent mosquito vectors are present in adequate numbers , transmission will continue for as long as the MF density is maintained above a certain threshold . The host-parasite relationship in W . bancrofti transmission dynamics has been subjected to analysis using mathematical models and two infection patterns , limitation and facilitation , have emerged depending on the vector species involved [36–39] . Limitation , due to culicine transmitted filariasis , and facilitation , due to anopheline vectors have been discussed in detail [3 , 40–43] . The models predict that in culicine transmitted-LF ( limitation ) the number of ingested microfilariae is always greater than the constant predicted by a straight line proportionality relationship so that even at low MF densities parasite uptake by mosquitoes occurs . Conversely , with anopheline-transmitted filariasis ( facilitation ) , as in The Gambia , a lower critical threshold exists below which the number of ingested microfilariae decreases to the constant ratio . Under these conditions , transmission is not sustained and the infection will be eliminated . The critical threshold below which Anopheles-transmitted LF will be eliminated can be achieved either by reducing the density of microfilaria or the density of mosquitoes [44] . In The Gambia , where onchocerciasis is non endemic [45] , there was never a systematic community-wide filariasis treatment that would have resulted in the reduction of W . bancrofti MF density [21] . The most likely explanation for our observation that transmission has been interrupted is the reduction of vector density through the widespread use of insecticide treated bednets that scaled up dramatically after 2003 and became more effective with the introduction of LLINs [12 , 46] for malaria control . A Sahelian drought in neighbouring countries resulted in a significant reduction in rainfall in the Gambia that probably affected mosquito numbers and LF transmission in the late 1960s and 1970s [6 , 21] . Nevertheless LF was still highly endemic in the Western and Upper River divisions during the late 1970s [6 , 21] . The vectors of malaria in The Gambia are also involved in the transmission of LF with Anopheles arabiensis , An . gambiae s . s . , An . melas and An . funestus acting as the principal carriers of W . bancrofti . Culex mosquitoes probably play no role in the transmission of LF in The Gambia as is the case in other countries in West Africa where only Anopheles mosquitoes act as vectors of W . bancrofti . Despite the early elucidation of the biology and behaviour of Anopheles mosquitoes in The Gambia , there were no systematic vector control activities in the country until insecticide treated bednets were introduced in the 1990s [21] . The report of the Liverpool School of Tropical Medicine expedition to The Gambia in 1901 recommended measures to prevent mosquito breeding in the capital , Bathurst [47] . In 1904 , an ordinance covering the prevention of mosquito breeding and establishing posts for a sanitary inspector and a team of labourers to implement vector control was introduced . However , outside Bathurst no systematic attempts were made to control mosquitoes until insecticide treated bednets were introduced in the 1990s and scaled up gradually [47] . The main vectors of LF in the Gambia , are highly susceptible to insecticidal nets and indoor residual spray ( IRS ) . The use of IRS to control malaria in the 1970s and 1980s interrupted LF transmission in Solomon Islands , parts of Papua New Guinea , Indonesia and Togo [3] . Community-wide use of long lasting insecticidal nets recently interrupted the transmission of LF in parts of Nigeria[48] and Papua New Guinea [49] . Improvements in living standards and housing characteristics probably contributed to the elimination of LF in Seychelles , Cape Verde , and Mauritius where Culex mosquitoes were the main vectors [50] . There is however little evidence that improvement in living standards have significantly impacted LF transmission in rural areas in Sierra Leone where LF was endemic in all districts before MDA commenced in 1998 [51] . Knight [21] who performed MF surveys in 1975 and 1976 attributed the decline in MF rates between 1950 and 1976 to a reduction in human-mosquito contact rates through decreased rainfall , improved standard of living and vector control against malaria and nuisance mosquitoes through bednets , at that time not impregnated with insecticide . Vector control in the form of IRS with DDT conducted during the malaria eradication campaign in Solomon Islands from 1974 to 1977 decreased the MF prevalence from 22% to zero without the use of anti-filarial drugs over a period of four years [17 , 18] . As a result of Global Fund and PMI funding for bednet implementation , a rapid scale up in ITN coverage has occurred in many countries in Africa since 2003 when only Eritrea , Malawi , The Gambia , and Sao Tome and Principe had ITN ownership coverage greater than 20%[12] . Analysing data from suppliers of ITN to countries , National Malaria Control Program ( NMCP ) reports of ITNs distributed to health facilities and implementation partners and household survey data , Flaxman and co-workers[12] calculated insecticidal net ownership coverage for 44 countries in Africa between 2003 and 2008 . By the middle of 2008 , 8 countries including The Gambia had ITN ownership coverage of 60% or greater . There were substantial increases from 2003 in the delivery of interventions against malaria , including distribution of insecticidal nets , in The Gambia [52] . Investigating the changes in malaria indices in the country , and the causes and public-health significance of these changes , Ceesay and collaborators [13] reported that the proportions of malaria-positive slides decreased by 82% and concluded that a large proportion of the malaria burden has been alleviated in The Gambia . This remarkable reduction in malaria parasite positive individuals would confirm our views given that malaria and W . bancrofti have common Anopheles vectors , the absence of any detectable W . bancrofti in our sample of over 3000 children throughout the country is similarly due to the widespread use of insecticide impregnated nets and now LLINs . Previous trials of chemoprophylaxis and insecticidal nets against malaria in The Gambia have shown reductions in all-cause morbidity and mortality greater than that directly attributable to malaria [13] . Improved treatment against malaria contributed to the reduction in malaria transmission but according to Ceesay and colleagues [13] the most significant factor was the increase of coverage of insecticidal nets because of support from the Global Fund—awarded in 2003 and implemented from 2004 onwards—for free distribution of ITNs to pregnant women and mothers of children younger than 5 years , which according to Ceesay and co-workers accounted for 55% of the population . A nationwide survey carried out to investigate the use of ITNs in rural areas of the Gambia in 1991 , showed that 58% of beds had a net [46] . Bednet usage was higher in the Central Region ( 76% ) , than in the Western and Eastern Regions ( both 51% ) [46] . This could partly explain the difference in LF endemicity between the Central and Western divisions observed during this study . In Kenya , one of the few African countries where the coverage of insecticidal nets exceeded 40% in 2007[12] , LF prevalence continued to decrease even when MDA was interrupted for two years [53] . In Sierra Leone , where bednet usage was traditionally low [54] and scaling up of insecticide-treated nets was among the slowest in Africa [12] , LF remains endemic in many parts of the country after 10 years of MDA with ivermectin for onchocerciasis and more recently for LF [45 , 55] . Onchocerciasis is not endemic in The Gambia and hence mass treatment with the ivermectin has never been implemented [45] . Comprehensive reviews on the advances in knowledge of vector ecology , vector-parasite relationships , and both empirical and theoretical evidence regarding vector management to determine the role of vector control in the GPELF have concluded that MDA activities can be synergised with vector control and LF elimination will be easier to achieve [3 , 7 , 14 , 15 , 56] . Recent studies on the impact of insecticide treated nets on LF transmission in PNG [49] and Nigeria[48 , 57] showed that using insecticidal nets alone in the absence of MDA interrupted the transmission of LF in highly endemic areas with MF rates over 50% . Failure to find evidence of transmission to children of W . bancrofti in The Gambia in 2013 over a 6–7 year period suggests that transmission has been interrupted and the most likely cause was the extensive use of insecticide treated nets for malaria control over the last two decades built upon the causes of the decline observed [17] between the 1950 and 1970s . The growing evidence for the impact of malaria vector control activities on LF transmission was endorsed by WHO through a position statement in 2011 on integrated vector management ( IVM ) to control malaria and lymphatic filariasis [58] . IVM is promoted by WHO to strengthen partnerships and cross sector approaches to the control of mosquito-borne diseases like malaria and LF [15 , 58] . In 2014 , the Nigerian government converted these evidence based approaches into policy by launching a coordinated plan to eliminate malaria and lymphatic filariasis through the use of long lasting insecticidal nets ( http://www . afro . who . int/en/nigeria/press-materials/item/6286-nigeria-launches-the-malaria-and-lymphatic-filariasis-co-implementation-guidelines . html ) . MDA may not be required to achieve elimination of LF in The Gambia but surveillance processes prescribed for countries during the post MDA phase , including morbidity management and disability prevention will be necessary , to acquire a non-endemic status that demands verification . The Gambia achieving non-endemic status for LF will represents huge progress in the global efforts to shrink the filariasis endemicity map and demonstrates the value of cross sector approaches in disease control . One more country will be removed from the list of LF endemic countries and 1 . 2 million people will be declared to be no longer at risk for the disease
|
The prevalence of lymphatic filariasis ( LF ) , in The Gambia was among the highest in Africa in the 1950s when about 50% of the adult population was positive for microfilaraemia . However , surveys conducted in 1975 and 1976 revealed a dramatic decline in LF endemicity in the absence of systematic treatment with anti-filaria medicines . This decline in LF prevalence in all villages was partly attributed to a significant drop in human-mosquito contact through a sustained reduction in rainfall in the 1960s and 1970s and the widespread use of insecticidal nets to protect against malaria . We asked the question in 2013 whether the rapid scale up in the use of insecticidal nets for malaria control in Gambia had resulted in the interruption of LF transmission . In this paper we present the results of three independently designed filariasis surveys conducted over a period of 17 years ( 1997–2013 ) , and involving over 6000 subjects in 21 districts across all administrative divisions in the country . In 2001 , tests performed to detect circulating filarial antigens ( CFA ) in serum samples collected between 1997 and 2000 , in three divisions , failed to show positive individuals from two that were previously highly endemic for the LF . Results of the second survey conducted in 2003 indicated that five of the 21 districts were slightly endemic for LF with CFA rates of 1% or 2% but MDA was never implemented in The Gambia . The results of our final survey conducted in 2013 were unequivocal in confirming the absence of transmission of LF in all 21 districts surveyed using WHO recommended statistically robust and validated tool known as transmission assessment survey ( TAS ) . The Gambia achieving a non-endemic status for LF represents a significant step in the efforts to shrink the filariasis endemicity map and demonstrates the value of cross sector approaches in disease control .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Elimination of Lymphatic Filariasis in The Gambia
|
Toxocara canis and T . cati are parasites of dogs and cats , respectively , that infect humans and cause human toxocariasis . Infection may cause asthma-like symptoms but is often asymptomatic and is associated with a marked eosinophilia . Previous epidemiological studies indicate that T . canis infection may be associated with the development of atopy and asthma . To investigate possible associations between Toxocara spp . seropositivity and atopy and childhood wheezing in a population of children living in non-affluent areas of a large Latin American city . The study was conducted in the city of Salvador , Brazil . Data on wheezing symptoms were collected by questionnaire , and atopy was measured by the presence of aeroallergen-specific IgE ( sIgE ) . Skin prick test ( SPT ) , total IgE and peripheral eosinophilia were measured . Toxocara seropositivity was determined by the presence of anti-Toxocara IgG antibodies , and intestinal helminth infections were determined by stool microscopy . Children aged 4 to 11 years were studied , of whom 47% were seropositive for anti-Toxocara IgG; eosinophilia >4% occurred in 74 . 2% and >10% in 25 . 4%; 59 . 6% had elevated levels of total IgE; 36 . 8% had sIgE≥0 . 70 kU/L and 30 . 4% had SPT for at least one aeroallergen; 22 . 4% had current wheezing symptoms . Anti-Toxocara IgG was positively associated with elevated eosinophils counts , total IgE and the presence of specific IgE to aeroallergens but was inversely associated with skin prick test reactivity . The prevalence of Toxocara seropositivity was high in the studied population of children living in conditions of poverty in urban Brazil . Toxocara infection , although associated with total IgE , sIgE and eosinophilia , may prevent the development of skin hypersensitivity to aeroallergens , possibly through increased polyclonal IgE and the induction of a modified Th2 immune reaction .
There is evidence that the prevalence of allergic diseases has increased worldwide in recent decades , especially among populations living in large cities and living a Western lifestyle [1] . A better understanding of the causes and risk factors associated with this epidemic is important to identify novel preventive strategies against these diseases [2] . Epidemiological studies conducted in various geographic locations have shown that helminth infections are , under different circumstances , associated with a reduced or increased prevalence of atopy and allergic diseases [3] , [4] , [5] , [6] . Toxocara canis and T . cati are intestinal roundworms found in dogs and cats , respectively , which may infect humans when exposed to their eggs in the environment . Humans serve as paratenic hosts in whom the parasites are unable to develop beyond the larval stage . Migratory Toxocara larvae may cause diseases in the liver , eyes , brain and lungs . Pulmonary toxocariasis has been reported to be associated with asthma-like symptoms [7] . Although several helminth infections of humans , such as Trichuris trichiura [8] , [9] , Schistosoma mansoni [10] , [11] , and Ascaris lumbricoides [12] , [13] , have been associated with a reduced prevalence of allergen skin test reactivity and asthma , human infection by Toxocara spp . has been associated with an increase in the prevalence of atopy and asthma symptoms [14] . Chan and collaborators [15] have previously shown that toxocariasis may increase predisposition to the development of allergic diseases , especially in children . It has also been demonstrated that toxocariasis is associated with elevated levels of specific IgE against aeroallergens ( sIgE ) , serum total IgE , eosinophil counts [16] , increased skin sensitivity to aeroallergens [17] , atopic asthma in children [18] , [19] and decreased lung function [20] . However , not all data support these associations: Zacharasiewicz and collaborators [21] were unable to show an association between Toxocara spp . seropositivity and allergen skin test reactivity , and they and others [18] , [21] , [22] did not observe an association between Toxocara infection and asthma . The diagnosis of human toxocariasis is problematic because obtaining the excretory-secretory products of Toxocara larvae required for serologic assays is highly labour-intensive and time-consuming . Most serologic studies of human and animal toxocariasis use the excretory-secretory antigens of T . canis larvae ( TcESLA ) because T . canis females are easier to obtain from puppies . Due to the considerable antigenic cross-reactivity between the Toxocara larvae of both species , the detection of antibodies using the T . canis antigen does not discriminate between the two infections [23] . Because of the conflicting findings in the literature on the effects of Toxocara infection on atopy and asthma , we investigated this association in children living in poor urban neighbourhoods in Latin America where there is a high seroprevalence of specific IgG to Toxocara spp [24] . This study was carried out in the context of other chronic helminth infections of childhood that have also been associated with atopy and asthma [6] . After controlling for potential confounding factors , including intestinal helminths , we found that children who were seropositive for anti-Toxocara IgG had more eosinophilia and elevated levels of total and allergen specific IgE , which is consistent with the findings of previous studies [16] , [17] , [18] , [19] . However , we also reported , for the first time in the literature , that Toxocara seropositivity was associated with a reduced prevalence of skin prick test ( SPT ) reactivity to common aeroallergens and that it may play an important role as an effect modifier in the association between sIgE and SPT .
This study was performed in the city of Salvador in Northeast Brazil , which has a population of 2 , 800 , 000 . The study was performed with a cohort of 1 , 445 children aged 4 to 11 years who lived in non-affluent neighbourhoods and were chosen to represent areas of the city without sanitation . The cohort was chosen as part of a study conducted between 1997 and 2001 to assess the impact of a sanitation program on the occurrence of diarrhoea [25] . The children were resurveyed in 2005 to collect data on risk factors for wheezing [26] . The legal guardian of each child filled out an ISAAC Phase II-based questionnaire . Other social , demographic and environmental data were collected using validated questionnaires . Informed consent was obtained from the parents or guardians of the children , and ethical approval was granted by the Instituto de Saúde Coletiva da Universidade Federal da Bahia and the National Commission on Ethics in Research ( CONEP ) , Brazil . Because the prevalence of sIgE for each of the studied allergens was greater than the SPT and the frequencies of SPT positivity among those without sIgE was very low [fungi ( 0 . 5% ) dog epithelium ( 1 . 1% ) and cat epithelium ( 0 . 9% ) ] , atopy was defined as the presence of at least one positive test of the serum for anti-aeroallergen IgE≥0 . 70 kU/L ( anti- Dermatophagoides pteronyssinus , Blomia tropicalis , Blattella germanica and Periplaneta americana ) , irrespective of SPT results . Children were classified as currently wheezing if parents reported wheezing in the previous 12 months and the children had at least one of the following: ( i ) diagnosis of asthma ever , ( ii ) wheezing with exercise in the last 12 months , ( iii ) ≥4 episodes of wheezing in the last 12 months or ( iv ) waking up at night because of wheezing in the last 12 months . These questions were included to increase the specificity for current wheezing as a marker for asthma disease . All other children were classified as non-wheeziers . Atopic and non-atopic wheezings were defined as symptoms of wheezing in the presence or absence , respectively , of serum IgE ≥0 . 70 kU/L for any of the tested aeroallergens . Two stool samples were collected from each child two days apart and analysed using the gravitational sedimentation [27] and Kato-Katz techniques [28] to detect eggs of Ascaris lumbricoides , Trichuris trichiura , hookworms and Schistosoma mansoni . Because hookworms and Enterobius vermicularis eggs were rarely observed ( 0 . 2% and 1 . 4% , respectively ) , these infections were excluded from the analyses . No S . mansoni eggs were observed in the stool samples . The children were evaluated in a mobile clinic in each of the study neighbourhoods , where they were evaluated by a medical team ( doctor , nurse and laboratory technician ) , blood was collected ( into EDTA-treated tubes ) , and skin prick testing for seven relevant aeroallergens was performed . At this time , the results of the stool examinations were provided to the parents , and appropriate treatment for parasite infections was given . Blood was taken to obtain differential blood cell counts ( using an automated counter; Counter Electronics , Hialeah , FL , USA ) and to measure total IgE , allergen-specific IgE and IgG to Toxocara spp . in plasma . SPTs were performed on the right forearm of each child using extracts ( ALK-Abello , São Paulo , Brazil ) of Dermatophagoides pteronyssinus , Blomia tropicalis , Blatella germanica , Periplaneta americana , fungi , and cat and dog dander . Saline and 10 mg/mL histamine solution were used as negative and positive controls , respectively . Reactions were read after 15 minutes , and a mean wheal size of at least 3 mm greater than the negative control was considered positive . The measurement of total IgE was performed as described previously [29] . Briefly , high binding microassay plates ( Costar , Cambridge , ME , USA ) were coated with 4 µg/mL of an anti-human IgE antibody ( Pharmingen , San Diego , CA , USA ) overnight at 4°C . Plates were blocked overnight at 4°C with PBS containing 10% foetal bovine serum ( FBS ) and 0 . 05% Tween 20 . Samples were diluted 1∶10 in PBS containing 2 . 5% FBS and 0 . 05% Tween 20 and incubated overnight at 4°C . Plates were incubated sequentially with biotinylated anti-human IgE ( Sigma Aldrich , San Louis , MO , USA ) , streptavidin/peroxidase ( Pharmigen , San Jose , CA , USA ) and substrate ( a mixture of hydrogen peroxide and o-phenylenediamine; Sigma Aldrich , St Louis , MO , USA ) . Between all steps , the plates were washed three times with PBS containing 0 . 05% Tween 20 ( PBS-T ) and once with PBS . All incubations were for one hour at room temperature , except for the streptavidin-peroxidase and substrate steps , which were 30 minutes . A pool of sera from parasite-infected subjects was used as the positive control . Umbilical cord serum from a newborn of a non-atopic and non-parasitised mother was used as the negative control . The cut-off for elevated levels of total IgE was defined as 0 . 2 µg/ml , which represented the median plus the half the interquartile range for 54 negative control sera ( from children with 3 consecutive stool samples that were negative for parasites , allergen-specific IgE levels of <0 . 35 kU/L , and <2% peripheral blood eosinophilia ) [29] . Measurement of the levels of specific IgE to B . tropicalis , D . pteronyssinus , P . americana , B . germanica and A . lumbricoides was performed using the ImmunoCAP assay ( Phadia Diagnostics AB , Uppsala , Sweden ) . These four specific mite and cockroach allergens were chosen to measure atopy based on the findings of skin prick test against a panel of seven relevant aeroallergens , which showed these to be the most relevant allergens in our study population . We used two cut-off points for aeroallergen-specific concentrations ( ≥0 . 35 kU/L and ≥0 . 70 kU/L ) to investigate their association with Toxocara seropositivity; however , only the higher cut-off point was used to define atopy . Excretory/secretory products of T . canis larvae ( TcESLA ) were obtained as described previously [30] with appropriate modifications [31] . Briefly , puppies from parasite-infected bitches were treated with piperazine ( 100 mg/kg ) and mineral oil . The uteri of adult T . canis females were dissected , the eggs removed and incubated in 2% formalin until embryonation . The egg membranes were disrupted using glass beads , and the released larvae were purified using a 15-µm pore polystyrene membrane filter . The larvae were cultured in RPMI medium ( Sigma Chemical Co . , St . Louis , USA ) at 37°C in a CO2 incubator , and the culture supernatants containing TcESLA were cryopreserved at 70°C in the presence of 1 mM phenylmethylsulfonyl fluoride ( PMSF; Sigma Chemical Co . , St . Louis , USA ) until use . TcESLA was concentrated using Amicon filters ( Millipore Corporate , Billerica , MO , USA ) with pores permeable to molecules of 3000 kDa and subsequently dialysed against phosphate buffered saline ( PBS ) , pH 7 . 4 . The protein content of the samples was determined using the Lowry technique ( 1951 ) [32] , and the antigen was aliquoted and stored at 70°C , until further use . To eliminate cross-reactive antigens shared by the ascarid worms A . lumbricoides and Toxocara spp . , human sera were absorbed with somatic antigens from A . lumbricoides before the measurement of anti-Toxocara IgG . A . lumbricoides antigen was prepared from adult worms obtained from children infected and treated with albendazole and 5 mg bisacodyl ( Dulcolax ) . The worms were washed in saline and crushed in an electric grinder ( Bead-Beart , Biospec , USA ) in the presence of PBS that contained protease inhibitors [1 mM phenylmethylsulfonyl fluoride ( PMSF ) , 2 mM ethylenediamine tetra-acetic acid ( EDTA ) , 2 mM tosyl phenylalanyl chloromethyl ketone ( TPCK ) , and 50 µM p-tosyl-L-lysine chloromethyl ketone ( TLCK ) ( Sigma Chemical Co . , St . Louis , USA ) ] . The suspension was centrifuged , and the soluble fraction stored at −70°C after determining the protein concentration using the Lowry method [32] . For absorption of sera , 100 µL of each serum was incubated with 250 µL of a solution containing: 4 . 0 mg/mL A . lumbricoides antigen , 100 µL polyethylene glycol ( Sigma Chemical Co . , St . Louis , USA ) and 50 µL PBS . After incubation for 30 minutes at room temperature under agitation , the material was centrifuged for 10 minutes . The supernatant was collected , re-absorbed with A . lumbricoides antigen and kept at −70°C until assayed . The second absorption was performed because some cross-reactive antibodies remained in the sera after the first absorption . Because 10 . 7% of the children were infected with T . trichiura , a sample of the studied sera was also absorbed with this parasite extract and compared to the same sera absorbed with A . lumbricoides alone or with both parasites . Because absorption with A . lumbricoides alone or with both parasites provided comparable titers of anti-Toxocara IgG , the remaining sera were absorbed with A . lumbricoides antigen alone . The detection of anti-Toxocara IgG antibodies was carried out as previously described by de Savigny with modifications [33] . Briefly , 96-well plate wells were incubated overnight at 4°C with 3 . 2 µg/mL of TcESLA in pH 9 . 6 carbonate/bicarbonate buffer . The plates were blocked with 0 . 15 M phosphate-buffered saline , pH 7 . 4 ( PBS ) , containing 10% FBS ( Sigma Aldrich , St Louis , MO , USA ) . Sera that had been pre-absorbed with A . lumbricoides extract and diluted at 1∶1000 in PBS containing 0 . 05% Tween 20 and 2 . 5% FBS ( PBS-T-FBS ) were added to the plates . After incubation , a solution of biotinylated anti-human IgG ( BD , Pharmigen , San Jose , CA , USA ) was added , followed by incubations with streptavidin-peroxidase ( BD , Pharmigen , San Jose , CA , USA ) , and substrate ( Sigma Aldrich , St Louis , MO , USA ) . Washings , incubations and reading were performed as described above for the measurement of total serum IgE . The reaction was blocked with 2 N sulphuric acid and read using a spectrophotometer at 490 nm ( Biotek EL-800 , CA , USA ) . The cut-off for the assay was obtained using the mean plus three standard deviations of the anti-Toxocara IgG assay from the negative control sera , which were obtained from 20 children without contact with dogs and cats . Because this assay does not discriminate infection by T . canis or T . cati , we used the results of this assay as marker of past or present infection by both Toxocara species [23] . For the associations of Toxocara infection with eosinophilia , total IgE , aeroallergen-specific IgE , SPT and asthma , univariate and multivariate analyses were performed using logistic regression . Atopic and non-atopic wheezing were defined as current wheezing in the presence or absence , respectively , of ≥0 . 70 kU/L specific IgE to at least one aeroallergen . A priori confounders for the association between Toxocara seropositivity and outcomes were gender and age . The following potential confounders were considered: body mass index ( BMI ) , maternal educational level , parental asthma , parental smoking , household connection to the municipal sewage system , living on a paved street , frequency of garbage collection , number of siblings , the presence of cat ( s ) and/or dog ( s ) in the house , the presence of mould or dampness on the walls of the house ( by inspection ) , the presence of cockroach and rodents at home , attendance at day-care centre and period of attendance , and presence of A . lumbricoides and T . trichiura infections in stool samples . These variables were selected because they were associated with seropositivity to Toxocara or atopy or asthma in univariate analyses ( Table 1 ) or because they had been identified as confounders in a previous analysis using data from these children [34] . To build multivariate logistic regression models , we used a procedure in which step-wise forward selection of variables was performed . Significant variables from the univariate analysis were included , and each non-significant variable was included sequentially; if a variable became significant , it was kept in the model , but if it remained non-significant , it was discarded . The interaction of Toxocara seropositivity with the association between SPT and sIgE was analysed by univariate regression analysis , and the statistical significance of the interaction was provided by the Breslow-Day's test for odds ratio homogeneity . To analyse the association of Toxocara seropositivity with wheeze phenotypes ( atopic x non-atopic ) , multivariate logistic regression analyses were performed as described previously [35] . Thus , non-atopic wheeziers were compared with non-atopic non-wheeziers ( to estimate the risk of wheezing associated with toxocariasis among non-atopic children ) , while atopic wheeziers were compared separately with two groups to demonstrate the importance of choosing the appropriated reference group and the differences generated when different comparison groups are chosen: group 1 - non-atopic and non-wheeziers ( to estimate risk of wheezing associated with toxocariasis among atopic children uncontrolled by the effect of atopy ) ; and group 2 - atopic and non-wheeziers ( to estimate risk of wheezing associated with toxocariasis among atopic children controlled by the effect of atopy ) . We used multinomial logistic regression because it treats the categories of the polytomy ( atopic wheeziers , non-atopic wheeziers , atopic non-wheeziers , and non-atopic non wheeziers ) in a non-arbitrary order and also addresses several sets of log-odds that correspond to different dichotomies .
Of the 1 , 445 children enrolled in the study , complete data were obtained from 1 , 148 , all of which were included in the analysis . No statistically significant differences were observed in the prevalence of the outcomes when excluded children ( n = 297 ) were compared with those included in the analysis ( data not shown ) . Table 1 shows the distribution of study variables and outcomes among the study children and the associations with Toxocara seropositivity , as analysed by univariate analysis . Seroprevalence of Toxocara IgG increased with age and was greater among children with A . lumbricoides and T . trichiura infections and among those without a household connection to the sewage system . Toxocara seropositivity was positively associated with eosinophilia and with high levels of total and specific IgE ( ≥0 . 35 and ≥0 . 70 kU/L ) and was negatively associated with skin prick test ( SPT ) reactivity . Tables 2 and 3 show the multivariate logistic analysis of the association between Toxocara seropositivity with the study outcomes . Models were adjusted for the following confounders: gender , age , maternal schooling , parental asthma , presence of mould , sewage access and infections with A . lumbricoides and T . trichiura . Positive associations were observed between the Toxocara seropositivity and total IgE and eosinophilia ( at cut-offs of >4% and >10% ) ( Table 2 ) . The presence of sIgE , defined using cut-offs of ≥0 . 35 and ≥0 . 70 kU/L for at least one tested allergen , was also positively and significantly associated with Toxocara seropositivity . A statistically significant inverse association was observed between Toxocara seropositivity and SPT ( Table 3 ) . When anti-Toxocara IgG was stratified by optical density ( to represent levels of anti-Toxocara IgG ) , dose-response associations were observed , such that greater optical densities were associated with a greater prevalence of all study outcomes ( Tables 2 and 3 ) , with the exception of SPT , in which higher optical densities were associated with a reduced prevalence of SPT . The effect of Toxocara seropositivity on the association between sIgE and SPT positivity was analysed . We found that the association of sIgE with SPT increased with an increase in the sIgE levels and that this association was weaker in Toxocara seropositive children ( Table 4 ) . We evaluated the associations between Toxocara seropositivity and the levels of anti-Toxocara IgG antibodies with wheezing phenotypes . The results are shown in Table 5 . A positive and weak association ( statistically non-significant , but borderline ) was found between high levels of anti-Toxocara IgG and non-atopic asthma . Although a positive association between anti-Toxocara IgG seropositivity and atopic wheezing was found when non-atopic non-asthmatic children were used as reference group , this association disappeared when we used the appropriate reference group ( atopic non-asthmatic children ) , as recommended previously by Barreto and colleagues [35] .
Human infections with Toxocara spp . are generally difficult to diagnose because the parasites are inaccessible and most infections are asymptomatic . Thus , the prevalence of toxocariasis is grossly underestimated , posing a significant challenge to investigators interested in evaluating the public health impact of this infection [36] . Previous estimates of Toxocara seroprevalence in Latin America are highly variable , ranging from 4% to 52% in Brazil [37] . The latter prevalence was reported among adults in a village in the Amazon region [38] . Prevalences of 38% and 32% have been reported in children from Argentina [39] and Peru [40] , respectively . A previous study in Salvador , where the present study was performed , estimated a prevalence of 46% among blood donors with eosinophilia but no evidence of intestinal helminth infection [24] , which was similar to the high seroprevalence of 47% that was observed in the present study among children living in poor urban neighbourhoods and who were not selected by eosinophilia or helminth-infection status . Our data show that Toxocara seroprevalence was associated with increasing age , low levels of maternal educational , a lack of household access to the municipal sewage system , and co-infections with intestinal helminths . Such factors are likely markers of poverty and poor hygiene , under which circumstances children are at greater risk of acquiring a number of infections [6] , including from their pets or other neighbourhood animals , which are rarely , if ever , dewormed . Toxocara seropositivity was found associated with both cats and dogs at home in the studied population ( submitted data ) . A previous analysis in the same studied children of eight taxonomically distinct pathogen exposures , including viral , bacterial , protozoal and helminth ( A . lumbricoides and T . trichiura ) infections , showed strong positive associations between multiple infections , indicating shared risk factors [6] . Risk factors related to poverty and poor hygiene are common in toxocariasis [41] . The association between Toxocara seropositivity and intestinal helminth infections could also be explained by immunological cross-reactivity , although we believe that any false positive serologic reactions associated with intestinal helminths would have been minimised by the extensive absorption of sera with A . lumbricoides antigens carried out before measurement of Toxocara antibodies , as well as by the high serum dilution used in this assay . The present study investigated the relationship between Toxocara seropositivity and several markers of allergic-type responses and allergic disease , including eosinophilia , total IgE , markers of atopy , and atopic and non-atopic wheezing . Typically , helminth infections , such as by Toxocara spp . , stimulate Th2 immune responses , a type of immune response that is considered to be central to the development of atopy and allergy . Experimental infections of mice with T . canis have been associated with increased inflammatory activity , intense migration of eosinophils to the lungs and increased plasma levels of pro-inflammatory cytokines such as IL-6 and IFN-γ and eosinophil-associated chemokines such as eotaxin and RANTES [42] . Our findings demonstrate that Toxocara seropositivity was associated with high levels of total IgE and eosinophilia , even after adjustment for co-infections with intestinal helminths , confirming that Toxocara infection is a strong inducer of IgE and eosinophilia . Previous studies have demonstrated that eosinophilia is present in up to 87% of individuals with toxocariasis [40] , [43] . Toxocara seropositivity was also associated with the presence of specific IgE to mite and cockroach allergens . There is evidence from several studies of extensive cross-reactivity between mites and helminth parasites: 1 ) Johansson and collaborators ( 2001 ) [44] reported cross-reactivity of IgE antibodies between a fish nematode ( Anisakis simplex ) and mites ( Acarus siro , Lepidoglyphus destructor , Tyrophagus putrescentiae and D . pteronyssinus ) , 2 ) Ponte and collaborators ( 2011 ) [45] reported a high frequency of cross-reactive IgE antibodies between B . tropicalis and A . lumbricoides ( an ascarid worm that is closely related to Toxocara spp . ) , and 3 ) Acevedo and collaborators ( 2009 ) [46] reported the presence of multiple antigens that were cross-reactive between A . lumbricoides and B . tropicalis , including tropomyosin and glutathione-S-transferase . Despite using a more stringent cut-off for positivity for allergen-specific IgE ( ≥0 . 70 kU/L rather than that usually recommended for the definition of atopy , ≥0 . 35 kU/L ) in the present study to minimise the problem of cross-reactivity of low-affinity IgE , we observed a positive association between sIgE and Toxocara seropositivity . This observation could be explained by cross-reactivity between arthropod allergens and Toxocara antigens as described above [45] , [46] by inducing production of allergen-specific IgE by plasma cells through polyclonal signals associated with helminth infections such as IL-4 or by the fact that children who develop strong Th2 responses to Toxocara may be more ‘atopic’ in the sense that they are more likely to develop IgE responses to environmental allergens . Although we observed a positive association between Toxocara seropositivity and the presence of sIgE , Toxocara seropositivity was inversely associated with SPT to the same aeroallergens , and it had a strong modulator effect on the association between sIgE and SPT . The absence of allergen-specific skin reactivity , despite high sIgE values in the same individuals , has several possible explanations , including the following: 1 ) ‘mast cell saturation’ - the presence of high levels of parasite-induced polyclonal IgE ‘saturates’ high-affinity FcεR1 receptors on mast cells , thus reducing the probability that allergen cross-linking of specific IgE will lead to cell activation [47]; 2 ) IgG blocking antibodies , particularly those of the IgG4 class , may bind allergen epitopes , thus preventing access of such epitopes to specific IgE antibodies bound to the mast cell [47]; 3 ) cross-reactive carbohydrate determinants - cross-reactive IgE antibodies , which are reactive to common carbohydrates shared by mites and helminths such as phosphorylcholine-modified glycans or glycans containing Galb1-4 ( Fuca1-3 ) GlcNAc- ( Lewis X , LeX ) , have low affinity to the allergen epitopes , and weak binding may reduce the chance of cross-linking of the IgE bound to FcεR1 receptors on the mast cell surface [48] ( this phenomenon has been described for plants and pollen allergens [49] ) ; and 4 ) the downmodulation of SPT has been attributed to the so-called “modified Th2 response” [3] , [50] , [51] , in which helminth infection induces regulatory populations of T cells that produce immune regulatory cytokines such as IL-10 , which may increase the threshold for mast cell activation [52] . Therefore , the negative association between anti-Toxocara antibody seropositivity and SPT found in this work could be due to at least two different phenomena . First , the infection by Toxocara could modulate the immune system so that the ability of sIgE to mediate SPT is reduced ( e . g . , by competition with T . canis-elicited polyclonal/cross-reactive IgE ) . This phenomenon is consistent with the findings that the whole anti-Toxocara antibody seropositive sub-group had higher sIgE levels than the anti-Toxocara antibody seronegative sub-group ( Table 3 ) . Second , immunological phenomena , such as IL-10 production , that is induced by the Toxocara infection could break the association between sIgE and SPT . In this case , one would expect a weaker association between sIgE levels and SPT reactivity , as found in anti-Toxocara antibody seropositive children ( Table 4 ) . While Toxocara seropositivity was strongly associated with sIgE in our study population , it was not associated with atopic wheezing . In contrast , previous studies have observed associations between T . canis seropositivity and increased skin sensitivity to allergens [20] and atopic asthma [7] . However , such studies were performed in different populations from different geographic regions and included adults . We found a weak positive and statistically non-significant but borderline association between Toxocara seropositivity and non-atopic wheezing among children with the highest levels of anti-Toxocara IgG . However , a limitation of our study was the lack of power to show a statistically significant association of this finding . This finding may be explained by lung infestations with Toxocara larvae , which are known to cause asthma-like symptoms . Further limitations of our study were the cross-sectional study design , which did not allow us to distinguish exposure ( presumed to be Toxocara infections ) from our study outcomes ( allergic and atopic markers and wheezing ) . We identified Toxocara infection using the presence of specific IgG antibodies - the presence of antibodies does not distinguish present from past infections . Similarly , we cannot preclude confounding by other helminth infections . For example , we did not measure pinworm infections , which are universal and require specific detection methods . There is extensive cross-reactivity between different helminth infections , but we tried to reduce false-positive reactions by pre-absorption of sera with A . lumbricoides antigens and using sera with the highest dilution described in the literature . This step was an important strength of our study . Other strengths were the use of a large sample of children , two markers for atopy ( sIgE and SPT ) and distinct control groups that allowed us to distinguish more clearly the effects of Toxocara seropositivity on atopy and wheezing . The apparent protective effects of chronic helminth infections against atopy and the clinical manifestation of allergy and autoimmune diseases are counterbalanced by the adverse effects of these infections on childhood growth and nutrition and possible adverse effects on the immune response to vaccines [53] . Given that the prevalence of intestinal helminths and schistosomiasis has declined dramatically in Latin American countries such as Brazil over recent years , Toxocara infection is now likely to assume greater importance as a neglected public health problem and the most common endemic helminth infection in these countries . The data from the present study revealed that almost half the children aged between 4 and 11 years living in poor neighbourhoods in a large city in the tropical region of Brazil have evidence of infection with Toxocara . Although this infection was associated with enhanced inflammatory markers ( eosinophilia , total IgE ) and an increased prevalence of sIgE , it appeared to be protective against immediate hypersensitivity reactions in the skin induced by common aeroallergens .
|
Toxocara canis and T . cati are roundworms found in dogs and cats , respectively , that can also infect humans and cause several clinical features , including asthma-like symptoms . Human infections with T . canis have been associated with an increased prevalence of atopy and asthma . In the present study , we investigated the associations between Toxocara seropositivity with eosinophilia , total IgE , specific IgE and skin prick test reactivity to aeroallergens , as well as atopic and non-atopic wheezing . Toxocara seropositivity was associated with elevated eosinophil counts and total and aeroallergen-specific IgE but was also associated with a decreased prevalence of skin prick test . Toxocara seropositivity was not associated with atopic wheezing . In conclusion , our data show that human toxocariasis , although associated with eosinophilia and raised levels of total and allergen-specific IgE , may play a role in the modulation of allergic effector responses in the skin .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"immunology",
"parasitic",
"diseases",
"pulmonology",
"neglected",
"tropical",
"diseases",
"asthma",
"infectious",
"diseases",
"toxocariasis",
"epidemiology",
"biology",
"child",
"health",
"public",
"health",
"pediatric",
"epidemiology",
"clinical",
"immunology",
"respiratory",
"medicine"
] |
2012
|
Toxocara Seropositivity, Atopy and Wheezing in Children Living in Poor Neighbourhoods in Urban Latin American
|
Recognition of peptidoglycan ( PGN ) is paramount for insect antibacterial defenses . In the fruit fly Drosophila melanogaster , the transmembrane PGN Recognition Protein LC ( PGRP-LC ) is a receptor of the Imd signaling pathway that is activated after infection with bacteria , mainly Gram-negative ( Gram− ) . Here we demonstrate that bacterial infections of the malaria mosquito Anopheles gambiae are sensed by the orthologous PGRPLC protein which then activates a signaling pathway that involves the Rel/NF-κB transcription factor REL2 . PGRPLC signaling leads to transcriptional induction of antimicrobial peptides at early stages of hemolymph infections with the Gram-positive ( Gram+ ) bacterium Staphylococcus aureus , but a different signaling pathway might be used in infections with the Gram− bacterium Escherichia coli . The size of mosquito symbiotic bacteria populations and their dramatic proliferation after a bloodmeal , as well as intestinal bacterial infections , are also controlled by PGRPLC signaling . We show that this defense response modulates mosquito infection intensities with malaria parasites , both the rodent model parasite , Plasmodium berghei , and field isolates of the human parasite , Plasmodium falciparum . We propose that the tripartite interaction between mosquito microbial communities , PGRPLC-mediated antibacterial defense and infections with Plasmodium can be exploited in future interventions aiming to control malaria transmission . Molecular analysis and structural modeling provided mechanistic insights for the function of PGRPLC . Alternative splicing of PGRPLC transcripts produces three main isoforms , of which PGRPLC3 appears to have a key role in the resistance to bacteria and modulation of Plasmodium infections . Structural modeling indicates that PGRPLC3 is capable of binding monomeric PGN muropeptides but unable to initiate dimerization with other isoforms . A dual role of this isoform is hypothesized: it sequesters monomeric PGN dampening weak signals and locks other PGRPLC isoforms in binary immunostimulatory complexes further enhancing strong signals .
Immune signaling is triggered by recognition of molecular patterns that are common in microbes but absent from the host . PGN is a cell wall component of Gram+ and Gram− bacteria and bacilli , but its amount , sub-cellular localization and specific composition vary between different bacteria , and may set the basis for specific recognition by PGN recognition proteins such as PGRPs . These proteins share a conserved PGRP domain that is similar to the T7 lysozyme . The Drosophila melanogaster PGRP-SA [1] and PGRP-SD [2] are essential for activation of Toll signaling . In contrast , PGRP-LC [3] , [4] and PGRP-LE [5] trigger Imd pathway activation . The PGRP-LC gene encodes three PGRP ectodomains , each of which fuses by alternative splicing to an invariant part , generating three distinct isoforms: PGRP-LCx , -LCy and -LCa . The intracellular invariant part encompasses an IMD interaction domain and a receptor-interacting protein homotypic interaction motif ( RHIM ) -like motif , which mediate contact with the IMD receptor-adaptor protein [6] and perhaps an unknown factor , respectively [5] , to initiate signal transduction . Several studies have provided novel , important insights into the structural basis of PGN recognition by PGRPs . Crystal structures have been determined for six Drosophila PGRPs [7] , [8] , [9] , [10] , [11] , [12] , [13] , including PGRP-LE and the heterodimer PGRP-LCx/LCa in complex with monomeric meso-diaminopimelic acid ( DAP ) -type PGN , which is released mostly from Gram− bacteria during PGN turnover and is known as tracheal cytotoxin ( TCT ) . These structures suggest that PGRP-LCx is sufficient for Imd pathway activation by polymeric DAP-type PGN , whereas heterodimerization with PGRP-LCa is required for response to monomeric PGN [14] , [15] . PGRP-LCa itself is unable to bind PGN and its suggested role is to “lock” PGRP-LCx in a monomeric PGN binding mode . Anopheles gambiae , the major mosquito vector of human malaria in Africa , encodes seven PGRPs , five of which ( LA , LB , LC , LD and S1 ) are orthologous to Drosophila PGRPs [16] . Similar to its fly ortholog , PGRPLC encompasses three PGRP domains ( LC1 , LC2 and LC3 ) that are utilized via alternative splicing for production of three main protein isoforms [16] , [17] . Here , we investigate the role of PGRPLC in mosquito infections with bacteria and malaria parasites . Theoretical structural modeling indicates that PGRPLC can recognize PGN from both Gram+ Staphylococcus aureus and Gram− Escherichia coli bacteria , and experimental results demonstrate that indeed PGRPLC mediates resistance against such infections . PGRPLC3 is a key modulator of these reactions . The structural modeling data suggest that , upon monomeric PGN binding , PGRPLC3 may lock other PGRPLC isoforms in binary immunostimulatory complexes , through a mechanism that differs significantly from that employed by Drosophila PGRP-LCa . PGRPLC3 can also sequester monomeric PGN perhaps to prevent unnecessary immune activation during low infections . Importantly , PGRPLC signaling modulates the intensity of mosquito infections with human and rodent malaria parasites . We also demonstrate that PGRPLC initiates responses against microbiota and bacterial infections of the midgut . In female mosquitoes , the size of the midgut bacterial communities substantially increase after a bloodmeal , causing further activation of PGRPLC signaling that appears to consequently affect the parasite infection intensities .
We injected dsRNA into newly emerged adult female A . gambiae to silence by RNAi the expression of corresponding PGRP genes . Four days later , the mosquitoes were infected with E . coli or S . aureus , two bacteria species with different types of PGN: DAP and Lysine ( Lys ) -type PGN , respectively . The survival of these mosquitoes was monitored daily and compared to the survival of GFP dsRNA-injected controls using Log-rank and Gehan-Breslow-Wilcoxon tests of survival curves . PGRPLC silencing had a pronounced effect ( P<0 . 001 with both tests ) on survival after infections with either bacterium ( Figure 1A ) . E . coli infections killed 50% of the PGRPLC knockdown ( kd ) mosquitoes by day 4 and subsided thereafter . S . aureus infections killed 50% of the kd mosquitoes by day 2 and almost all mosquitoes by day 6 . Interestingly , PGRPLA2 silencing had a weak but significant ( P<0 . 05 ) protective effect against S . aureus infections . PGRPS1 and PGRPS2/3 kds also exerted minor protective effects that were statistically significant ( P<0 . 05 ) only with one of the two tests ( PGRPS2 and S3 were silenced simultaneously as their sequences are almost identical ) . Injection with dsGFP or saline alone ( Figure S1 ) did not cause mosquito mortality , indicating that the documented phenotypes were indeed due to the combination of specific PGRP gene silencing and exogenous bacterial infections . These data indicated a potential difference in the PGRPLC-mediated antibacterial defense between Anopheles and Drosophila , which we further investigated by subjecting D . melanogaster PGRP-LC− mutants [3] to our infection assays . PGRP-SAseml [1] and white mutant strains were used as controls . Two to three day-old flies were injected with E . coli or S . aureus and their mortality rates were recorded daily and compared ( Figure 1B ) . PGRP-LC− flies displayed pronounced mortality ( P<0 . 001 ) following E . coli infection , which reached 50% by day 3 . Mortality of PGRP-SAseml flies following E . coli infection was markedly less but also significant ( P<0 . 01 ) . Similarly , both PGRP-LC− and PGRP-SAseml flies showed a significant drop ( P<0 . 001 ) in survival when infected with S . aureus , which reached 50% at day 3 and over 90% at day 5 . Importantly , the survival curves suggested that the presence of PGRP-LC sufficed to contain S . aureus infection in PGRP-SAseml mutants in the first 3 days after the infection , but thereafter PGRP-SA was indispensable . PGRP mutant and control white flies survived equally well when injected with saline alone ( data not shown ) . We investigated the complex architecture of the PGRPLC gene , by mapping on the A . gambiae genome the sequences of all available related ESTs and various genomic PCR or RT-PCR reactions . The relative positions of the primers used in these reactions are shown in Figure S2 and their sequences are listed in Table S1 . As shown previously [16] , [17] , PGRPLC encodes three main protein isoforms ( LC1 , LC2 and LC3 ) , each having a different PGRP domain and an optional 75-nucleotide cassette at the 3′ end of the common exon 3 ( Figure 2A ) . Thus , each isoform exists in two versions; the long one ( -L ) is 25 amino acids ( aa ) longer than the short version ( -S ) , which utilizes a cryptic splice acceptor in exon 3 . Sequence alignment of the Anopheles ( Ag ) and Drosophila ( Dm ) PGRPLCs ( Figure S3 ) and mapping of available related ESTs on the Drosophila genome revealed an equivalent albeit shorter ( 57 nucleotide ) optional cassette in the DmPGRP-LC gene . In both insects , these cassettes are extracellular and located immediately downstream of the putative transmembrane domain . Use of the DisEMBL Intrinsic Protein Disorder Prediction algorithm ( http://dis . embl . de/ ) predicted that they encode a hot loop region , a flexible structure that could be important in protein interactions . Each PGRP domain of AgPGRPLC is encoded by three exons: the common exon 4 and two variable exons . The introns separating these variable exons are at identical relative positions suggesting that additional hybrid domains could be generated through alternative splicing . We tested this hypothesis by RT-PCR using combinations of primers distributed along the PGRPLC gene ( Figure S2 and Table S1 ) . Indeed , we detected a novel transcript encoding a hybrid PGRPLC2/3 domain , also showing optional association with the 25-aa cassette ( Figure 2A ) . Additional exon combinations were detected ( Figure S4 ) , but they exhibited frameshifts leading to premature stop codons or presumably non-functional domains . In addition , we detected non-random transcripts encompassing unspliced versions of PGRPLC1 and LC2 ( Figure S4 ) . In contrast , the most abundantly expressed PGRPLC3 transcript showed no unspliced versions in either the EST databases or the cDNA products . We examined the contribution of each of the three main AgPGRPLC isoforms to antibacterial defense by silencing each one independently in adult Anopheles mosquitoes that were infected 4 days later with E . coli or S . aureus . Quantitative real time-PCR ( qRT-PCR ) showed isoform-specific silencing that varied quantitatively between isoforms: 65% for LC1 , and 30% for LC2 and LC3 ( Figure S5 ) . These levels were comparable with those obtained after silencing the entire PGRPLC gene by targeting common exons 2–4 as above . The survival of mosquitoes after bacterial infections was recorded daily for 9 days and referenced to the survival of infected control mosquitoes injected with GFP dsRNA ( Figure 2B ) . E . coli infections significantly reduced the survival of LC3 ( P<0 . 001; 50% at day 2 ) and to a lesser extent LC1 or LC2 ( 40–50% at day 6; P<0 . 05 and P<0 . 001 , respectively ) kd mosquitoes . Susceptibility of mosquitoes to S . aureus infections was significantly reduced ( P<0 . 001 ) after silencing LC3 ( 50% at day 1 ) and LC1 ( 50% at day 5 ) ; silencing LC2 appeared to have a minor effect on mosquito survival , which was not statistically significant . These data in conjunction with the silencing efficiency indicated that PGRPLC3 might be the most important isoform in the antibacterial defense . Initial experiments indicated that mosquito antimicrobial peptide ( AMP ) genes are transcriptionally induced as early as 3 h after E . coli or S . aureus infections but not after saline injections alone ( data not shown ) . We examined whether PGRPLC is involved in this response by comparing the levels of CEC1 and DEF1 transcripts in PGRPLC kd and dsGFP-treated control mosquitoes , 3 h after injection with bacteria or saline ( Figure 3 ) . Uninfected mosquitoes exhibited basal expression levels of both AMPs , which were slightly reduced after silencing PGRPLC . Infections with either bacterium induced the expression of both CEC1 and DEF1 , 4–5 and 2–3 fold , respectively . However , this induction was PGRPLC-mediated only in S . aureus infections , not in E . coli . Silencing separately the three main isoforms did not reproduce the effect of silencing the entire gene although a previous study showed that overexpression of PGRPLC1 or LC3 in cultured cells leads to induction of CEC1 expression [17] . Maybe this was partly due to the low level silencing of the individual isoforms . Previous studies have shown that Drosophila and Anopheles PGRPLCs act as phagocytic receptors of E . coli [18] , [19] . Since the transcriptional induction of CEC1 or DEF1 at early phases of E . coli infections was PGRPLC-independent , we examined whether PGRPLC confers resistance to E . coli by phagocytosis . Adult female mosquitoes were injected with polystyrene beads and re-injected 24 h later with fixed , fluorescently labeled E . coli or S . aureus . Microscopic observations showed that the capacity of hemocytes to engulf bacteria was drastically reduced in bead-injected compared to control mock-treated mosquitoes ( Figure S6A ) . However , this blockade of phagocytosis affected only mildly the survival of mosquitoes infected with live bacteria ( Figure S6B ) , as compared to the effect observed with PGRPLC silencing . Furthermore , the effect was statistically significant only in S . aureus and not in E . coli infections , suggesting that phagocytosis of E . coli may not ( or only partly ) explain the PGRPLC-mediated resistance to infections with this bacterium . We assessed by RNAi the potential role of PGRPLC in mosquito infection with malaria parasites . PGRPLC kd and dsGFP-injected control mosquitoes were fed on mice infected with GFP-expressing P . berghei [20] , [21] and their midguts were dissected 7 days later to determine the levels of infection . Silencing the entire PGRPLC gene resulted in a significant 4 . 4-fold increase of the median oocyst numbers relative to dsGFP-injected controls ( P<0 . 001; Figure 4A ) . A very significant ( P<0 . 05 ) increase in the prevalence of melanized ookinetes was also detected in PGRPLC kd mosquitoes ( Figure 4B ) . Silencing each of the three isoforms independently revealed a significant effect of PGRPLC3 kd on the median oocyst numbers ( P<0 . 05 ) , the infection prevalence ( P<0 . 05 ) and the melanized ookinete prevalence ( P<0 . 001 ) . PGRPLC2 kd also had a significant ( P<0 . 01 ) effect on the prevalence of melanized ookinetes . These data prompted us to examine the effect of PGRPLC kd in A . gambiae infections with the human malaria parasite Plasmodium falciparum in experiments performed in a high malaria transmission locale in Cameroon . Blood samples donated by P . falciparum gametocyte carriers were used to membrane feed laboratory-reared mosquitoes injected with either PGRPLC or control LacZ dsRNA . Five independent infections were performed , each using blood from a different gametocyte carrier , and oocyst intensities were determined 8 days later . The pooled data revealed a statistically significant ( P<0 . 005 ) increase of the median oocyst intensity in PGRPLC kd mosquitoes compared to controls ( Figure 4C ) . The infection prevalence also increased from 41% to 52% ( Fisher's exact test P<0 . 005 ) . Melanized P . falciparum ookinetes were not detected . Bacterial populations are common in the mosquito gut , and their size increases rapidly and substantially after a bloodmeal [22] . We examined whether PGRPLC signals against microbiota or opportunistic bacterial infections , which may interfere with mosquito infections with Plasmodium . PGRPLC kd and control dsGFP-treated 5-day-old adult females were sampled either after continuous sugar feeding or 24 h after blood feeding on mice infected with the rodent malaria parasite , P . berghei . DNA was extracted from surface-sterilized mosquitoes and used in quantitative genomic PCR reactions to assess the abundance of bacterial 16S ribosomal DNA ( rDNA ) . Oligonucleotide primers were selected to match highly conserved regions of prokaryotic 16S rDNA [23] , ensuring detection and quantification of most bacteria populations . Silencing PGRPLC led to a significant ( P<0 . 001 ) 2-fold increase of the bacterial load in sugar-fed mosquitoes ( Figure 5A ) . Following a bloodmeal , the increase of bacteria was approximately 4-fold in control and 6-fold in PGRPLC kd mosquitoes . This increase coincided with complete absence ( P<0 . 001 ) of CEC1 upregulation in PGRPLC kd mosquitoes compared to the 3-fold induction in their respective dsGFP-injected controls ( Figure 5B ) . CEC1 transcript levels were similar between mosquitoes fed on Plasmodium-infected and uninfected blood . To investigate whether the effect of PGRPLC on Plasmodium is related to the presence of the midgut microbiota , we treated adult mosquitoes for 5 consecutive days after hatching from the pupal stage with the antibiotic gentamycin and then allowed them to blood feed on P . berghei-infected mice . The results revealed a highly significant ( P<0 . 001 ) 3-fold increase of the oocyst numbers that developed in the mosquito midguts ( Figure 5C ) . We then performed the same experiment using PGRPLC kd and control dsLacZ-injected mosquitoes and counted the number of oocysts or melanized parasites 7 days post infection . The numbers of oocysts in the gentamycin treated or untreated PGRPLC kd mosquitoes were similar between them and with those in gentamycin-treated dsLacZ-injected controls ( Figure 5D ) . These data further supported our hypothesis that the effect of PGRPLC on Plasmodium survival is directly related to the bacteria residing in the mosquito midgut . Furthermore , the number and prevalence of melanized parasites in PGRPLC kd mosquitoes dropped to control levels when bacteria were depleted , suggesting that the parasite melanization phenotype was also related to the presence of bacteria in the mosquito midgut . Next we performed the reverse experiment by feeding adult mosquitoes with bacteria for 2 consecutive days before they were blood fed on P . berghei-infected mice . We used 3 different bacteria species , S . aureus , E . coli and Enterobacter cloacae ( Gram− ) , all of which led to a significant decrease of the number of oocysts in the mosquito midguts compared to control ( Figure S7 ) . Infection with E . cloacae had the biggest impact ( P<0 . 001 ) on the numbers of oocysts ( Figure 5F ) . We combined oral mosquito infections with E . cloacae with silencing PGRPLC or REL2 before infection with P . berghei . Both gene KDs reversed the effect of E . cloacae infection , leading to a significant increase of the oocyst numbers ( Figure 5G ) . In addition , silencing PGRPLC and REL2 substantially increased the numbers of melanized ookinetes , especially the latter ( Figure 5H ) . Our data suggested that PGRPLC3 might play a key modulatory role in the defense against bacteria , which in turn modulates infections with Plasmodium . To investigate this , we built homology models of the three main PGRPLC isoforms based on the crystal structure of the Drosophila PGRP-LCx-TCT-LCa heterodimer complex and structural alignments between Anopheles and Drosophila PGRPLCs ( Figure S8 ) . DmPGRP-LCx choice as template was further justified by the structural rigidity of the PGN binding cleft , as revealed in 3D-superpositions ( data not shown ) . None of the AgPGRPLC isoforms exhibit the two-residue insertions ( IN and DF; Figure S8 ) that occlude the TCT ( found in E . coli ) binding groove in DmPGRP-LCa [10] making this isoform deficient for PGN binding [15] . We used the TCT position in the DmPGRP-LCx-TCT-LCa complex as the initial docking position and modeled the ability of AgPGRPLCs to bind TCT ( Figure S9 ) . Docked TCT formed an extensive network of interactions with residues lining the binding groove of all AgPGRPLC isoforms ( Figure S9 , Text S1 , Table S2 , Table S3 and Table S4 ) . Most of these are identical between Ag and DmPGRPLCs and some are isoform-specific . The Arg-mediated recognition of DAP [11] , [12] is mediated by R82 of AgPGRPLC1 and LC2 and R84 of AgPGRPLC3 . A polar interaction between Y56 of AgPGRPLC1and O6 of TCT provides direct recognition of the 1 , 6-anhydro bond that is essential for immunostimulatory activity [14] , [24] . We also examined the potential of AgPGRPLCs to bind Lys-type PGN ( found in S . aureus ) , using as template the human PGRP-Ia-MTP ( muramyl tripeptide ) complex [25] , [26] . A two-residue pattern ( NY/F ) at the rim of the PGN binding cleft that is thought to mediate specificity to Lys-type PGN [27] exists in all AgPGRPLCs . In all the AgPGRPLC-MTP models , docked MTP is indeed cradled by a combination of contacts conserved in hPGRP-Ia-MTP ( Figure S9 , Text S1 , Table S2 , Table S3 and Table S4 ) . These data suggested no difference in the potential of PGRPLC3 and PGRPLC1 or LC2 to bind DAP- or Lys-type PGN . Crystal structures of DmPGRP-LCx-TCT-LCa and DmPGRP-LE-TCT-LE and functional studies [14] , [24] have shown that monomeric PGN induces functional dimerization of DmPGRPs . We modeled AgPGRPLC dimerization based on the DmPGRP-LCx-TCT-LCa heterodimer . AgPGRPLC-TCT models were placed at the position of DmPGRP-LCx-TCT in the complex ( position /x ) and additional AgPGRPLC models were placed at the position of DmPGRP-LCa ( position /a ) . The local structure at the dimer interface was maintained to account for induced-fit upon dimerization [11] . The predicted interactions between AgPGRPLC1/x or LC2/x and LC1/2/3/a revealed a pattern of contacts at the dimer interface consistent with the DmPGRPLCx-TCT-LCa complex , no steric clashes and good stereochemistry . Molecular contact analysis suggested that binary complexes can be formed between AgPGRPLC1 or LC2 at position /x and any of the three AgPGRPLCs at position /a ( Figure 6A–D and Text S1 ) . When attempting to place PGN-liganded AgPGRPLC3 at position /x , we noticed a unique PD-insert immediately after helix α2 at position 60 of this isoform , which lies at a tight junction with helix α2 of AgPGRPLCs/a ( Figure 6E ) . As proline residues are helix terminators , the length of helix α2 is likely identical with DmPGRP-LCx and the PD-insert is part of the PD-loop connecting the α2 and β2 helices . To explore the PD-loop conformation we used a conserved aromatic residue at position 65 of the α2/β2 loop ( F65 of AgPGRPLC3 ) , which is packed firmly with DAP- or Lys-type PGN in all liganded structures , as the anchor point and specifically modeled the PD flanking sequence S59-PDSR-N64 using MODELLER [28] . In the resulting structure , R63 occupied a conserved binding position to TCT or Lys-PGN , equivalent to K61 in LC2/x ( Text S1 and Table S3 ) . Additionally , we used a simulated annealing protocol to allow for broad sampling of conformational space with accelerated energy barrier crossing , which produced an ensemble of ten lower-energy loop conformers ( Figure 6F ) . While the PD-loop adopted a more relaxed conformation compared to the MODELLER structure , R63 and F65 were positioned in similar orientations , corroborating PGN-binding . We sampled alternative and wider conformational space for the PD-loop using Robetta [29] , a method that combines de novo and template-based protocols . Of the three resulting models , one showed PD-loop conformation similar to that of the simulated annealing and consistent with PGN binding ( Figure 6F ) ; the other two were inconsistent with PGN binding due to steric bumps or deformities in the L-shaped PGN-binding floor ( data not shown ) . However , when the resulting AgPGRPLC3/x-PGN models were positioned in heterodimers with LC1/a or LC2/a , D61 and S62 of the PD-loop clashed severely with residues at the inter-helical interface in the beginning of α2 helix/a , mainly at positions 42 and 44 . These clashes could not be relieved by manual adjustment or energy minimization as they involved main-chain and Cβ atoms , suggesting that AgPGRPLC3/x heterodimers are highly unlikely .
Recognition of conserved microbial structures is essential for activation of insect innate immunity . Sensing bacterial cell wall PGN by Drosophila PGRPs triggers one of two signaling pathways . Circulating PGRP-SA and PGRP-SD sense primarily Lys-type PGN that is mostly found in Gram+ bacteria , activating the Toll pathway that culminates with nuclear translocation of Dif or Dorsal . DAP-type PGN found mainly in Gram− bacteria and bacilli is principally sensed by the transmembrane PGRP-LC . This binding induces PGRP-LC dimerization and downstream initiation of Imd signaling that triggers activation and nuclear translocation of the Rel/NF-κB transcription factor Relish . The A . gambiae ortholog of Relish , REL2 , is required for resistance to infections with bacteria , both the Gram− E . coli and the Gram+ S . aureus [30] . Acting in concert with the IMD adaptor protein , REL2 confers resistance to S . aureus infections , but resistance to E . coli infections appears to be IMD-independent . Here we show that PGRPLC is essential in both these reactions , possibly acting as a common recognition receptor of these two branches of the REL2 antibacterial pathway . The first pathway resembles the conventional Imd pathway of Drosophila , consisting of PGRPLC , IMD and REL2-F , a form of REL2 that has an inhibitory ankyrin domain and a death domain . The second pathway appears to involve PGRPLC and REL2-S , a form of REL2 that lacks the ankyrin and death domains . The latter resembles a pathway proposed by Kaneko and colleagues [5] , where IMD is dispensable for Imd pathway activation in response to DAP-type PGN . It has been speculated that an unknown factor interacts with an intracellular domain of PGRPLC other than the IMD interacting domain . Henceforth we will distinguish between these two REL2 pathways as IMD-dependent and IMD-independent , respectively . Research in Drosophila has mainly used infection-dependent transcriptional induction of AMPs as diagnostic readout for defining pathways . Here , using survival of infected insects as the readout , we demonstrate the importance of Drosophila PGRP-LC in the defense against S . aureus , especially in the first days after infection; thereafter PGRP-SA becomes indispensable . These data indicate that the two pathways may act simultaneously and/or consecutively to maximize effectiveness: PGRP-LC and its downstream Imd signaling could effectively control low S . aureus infections , but persisting infections require PGRP-SA and Toll pathway responses . Vice versa , Drosophila PGRP-SA appears to also play a role in the defense against E . coli infections , but PGRP-LC is the principal receptor . This direct comparison of Drosophila and Anopheles indicates a putative divergence in PGRP-mediated antibacterial defenses between these two insects , as the mosquito PGRPLC seems to be the main receptor for both E . coli and S . aureus . This is consistent with our working hypothesis that adaptation of the evolutionary conserved signaling pathways is enabled by differences in recognition [31] . The Anopheles PGRPLC-mediated response to E . coli may not be the only defense against this bacterium . In contrast to the response to S . aureus , early transcriptional induction of AMPs in response to E . coli infections is not regulated by PGRPLC . An as yet unidentified PGRPLC-independent pathway may mediate this early response , but we cannot exclude that AMPs other than those tested here are induced via the IMD-independent REL2 pathway . The possibility that the PGRPLC requirement in E . coli infections relates to phagocytosis is unlikely , as blockade of phagocytosis appears not to have an effect equivalent to that of PGRPLC on mosquito survival following infections with this bacterium; however , further research is needed before this possibility can be entirely ruled out . The mosquito gut is habitat to large and diverse microbial communities . Several species of bacteria have been identified in the gut of field-collected anophelines , mostly Gram− proteobacteria and enterobacteria [32] , [33] , [34] . Our data reveal that PGRPLC signaling controls the size of symbiotic bacteria populations and intestinal bacterial infections in laboratory reared A . gambiae . It also controls the proliferation of gut bacteria after a mosquito bloodmeal , which can be as drastic as several thousand folds [22] and coincides with robust PGRPLC-mediated AMP induction . The importance of this reaction extends beyond mechanistic understanding of the mosquito immune system . Anopheles mosquitoes are vectors of Plasmodium parasites that cause malaria , a disease that infects 300–500 million people annually killing over one million of them . Within 24 h after a mosquito bloodmeal , a period that coincides with the proliferation of gut bacteria , Plasmodium gametocytes ingested with the blood undergo sexual development and reproduction , and the resulting zygotes become motile ookinetes that cross the midgut epithelium . The PGRPLC response against proliferating bacteria appears to be responsible , at least in part , for the drastic drop in parasite infection intensities during that period . Importantly , we demonstrate that PGRPLC controls the intensity of A . gambiae infections by P . falciparum gametocytes found in the blood of children in sub-Saharan Africa . This is the first time that a gene of the classical mosquito immune system is shown to have an effect on P . falciparum field isolates . A similar effect is detected in infections with the common laboratory model parasite , P . berghei . These findings lead us to postulate that the tripartite interaction between symbiotic bacteria , the mosquito immune system and invading Plasmodium parasites may play an important role in the mosquito susceptibility vs . resistance to infection with malaria parasites . Since the various mosquito habitats or laboratory rearing conditions can have a direct impact on the mosquito gut microbiota , we speculate that the mosquito responses to Plasmodium may vary between different settings . The combination of genetic , molecular and structural modeling data revealed a history of convergent evolution between the fruit fly and mosquito PGRPLC . Like its Drosophila ortholog , Anopheles PGRPLC gene produces three main isoforms via alternative splicing . However , these isoforms have evolved independently in the two lineages , via exon re-duplications [16] , [31] . All isoforms share a common first part of the PGRP ectodomain; the remainder is distinct and encoded by two isoform-specific exons , separated by an intron at identical relative positions . This modular architecture allows generation via alternative splicing of a PGRPLC2/3 hybrid domain isoform , which is expressed at low levels and thus may have a modulatory function or be expressed in specific cell types or conditions . An intriguing finding is that isoforms exist as short and long versions that differ by an alternative 25-aa cassette , laying immediately after the transmembrane domain and before the PGRP domain , an observation also made independently by Lin and colleagues [17] . A similar 17-aa cassette exists in Drosophila PGRP-LC . Both cassettes encode flexible structures and could be envisaged as providing long “necks” to membrane-bound PGRPLCs and hence flexibility to interact with ligands inaccessible to the rigid short-necked isoforms , e . g . PGN polymers shed by Gram+ bacteria . An alternative hypothesis derives from a study showing Drosophila PGRP-LC down regulation in response to live bacteria and suggesting its proteolytic cleavage by bacterial proteases as a sensing mechanism [35] . We propose that this cassette may be targeted for cleavage by host or pathogen proteases , resulting in immune response activation , down-regulation or evasion . Our data suggest that PGRPLC3 is the most important of the three main isoforms in the response against bacterial infections . PGRPLC3 is essential for resistance to E . coli , whereas PGRPLC1 and LC2 play less important roles . PGRPLC3 is also central in defense to S . aureus , as is PGRPLC1 albeit less important . These two isoforms have been previously implicated in CEC1 expression in cultured cells [17] . The specificity of PGRPs to these two bacteria is thought to be determined largely by the third aminoacid in the peptide bridge connecting their PGN glycan strands: DAP in E . coli and Lys in S . aureus . Drosophila PGRP-LCx is shown to bind DAP-type PGN , while PGRP-LCa is deficient for PGN binding [10] , [15] . Our structural models reveal a potential of all Anopheles PGRPLC isoforms to bind both DAP- and Lys-type PGNs . These data suggest a remarkable structural flexibility of PGRPLCs in PGN recognition and provide mechanistic support to genetic data reported herein and in Drosophila studies , where PGRP-LC mediates resistance to Lys-type PGN Gram+ bacteria [36] . However , they do not explain the observations that Anopheles PGRPLC3 is the most important isoform in antibacterial defense . Our PGRPLC dimerization models have provided important insights into this putatively unique role of PGRPLC3 . Molecular contact analysis suggests that binary complexes , such as those formed between Drosophila PGRP-LCx and LCa upon TCT binding on the former [11] , can exist between AgPGRPLC1 or LC2 at the /x position and any of the three AgPGRPLCs at the /a position . However , AgPGRPLC3 cannot initiate dimerization with other isoforms , as a two-residue insertion obstructs its binding surface . Moreover , unspliced PGRPLC3 transcripts are never detected , as opposed to all other splice variants , and PGRPLC3 is the dominant isoform in EST and cDNA sequences . These data could indicate a unique dual role of AgPGRPLC3 ( Figure S10 ) : ( a ) it locks PGRPLC complexes induced by monomeric PGN binding on the other two isoforms in binary or oligomeric modes , preventing the formation of multimers and adopting a role similar to DmPGRP-LCa; ( b ) it down regulates the response by removing from the environment monomeric PGN , since it cannot initiate formation of immunostimulatory dimers . The former role would maximize the response during high PGN concentrations , whereas the latter role would serve in dampening the immune signal in low PGN concentrations . This may justify the absence of PGRPLC-mediated induction of AMPs in early stages of E . coli infection and could also suggest a mechanism of immune tolerance to symbiotic bacteria .
Research involving humans was approved by the WHO and Cameroon National Ethics Committees . Collection and analysis of data was conducted anonymously . Informed written consent forms were collected . Research involving animals was approved by the United Kingdom Home Office and Imperial College Review Board . The A . gambiae G3 and N'gousso strains were reared as described in [37] . N'gousso is a laboratory-strain colonized in 2006 from field mosquitoes collected around Yaoundé , Cameroon . The D . melanogaster white mutant strain was obtained from Blades Biological Ltd , UK and the PGRP-SA ( semmelweiss ) [1] and PGRP-LC/TM6B [3] mutant strains were a kind gift of J . Royet . P . berghei was passaged through CD1 or Balb/C mice and mosquito infections were performed using standard procedures [38] . Total RNA was isolated from 10 female mosquitoes using TRIzol ( Invitrogen ) and treated with DNaseI . First strand cDNA synthesis was performed with 5 µg total RNA , using oligo-d ( T ) primers ( Invitrogen ) and Superscript Reverse Transcriptase II ( Invitrogen ) according to the manufacturer's instructions . Amplifications were performed with SYBR Green PCR mastermix and analyzed using the ABI PRISM 7700 sequence detection system following the manufacturer's instructions . Expression levels were calculated by the relative standard curve method , as described in Technical Bulletin #2 of the ABI Prism 7700 Manual ( Applied Biosystems ) , using S7 as endogenous control . Oligonucleotide primers flanked by T7 sequence ( GAATTAATACGACTCACTATAGGG ) were utilized in genomic PCR reactions for production of dsRNA . PCR products were cleaned up with the QIAquick PCR Purification kit ( QIAGEN ) and verified by sequencing . DsRNA was synthesized with the MEGAscript T7 Kit ( Ambion ) treated with DNaseI and cleaned up with the RNeasy kit ( QIAGEN ) . Its concentration was adjusted to 3 µg/µl and 69 nl were injected into the dorsal side of the insect thorax as described [39] . DsRNA for the whole PGRPLC gene KD was produced by subcloning the EST clone 4A3B-AAA-E-04 [40] into the plasmid vector pLL10 [41] . E . coli and S . aureus were cultured to 0 . 7 OD600 , pelleted , washed and resuspended in PBS . Final OD600 for mosquito injections were 0 . 01 for E . coli and 0 . 4 for S . aureus , and for fruit flies 0 . 1 for E . coli and 0 . 01 for S . aureus . 69 nl of these or PBS for controls were injected into the insect thorax with a nano-injector ( Nanoject , Drummond ) . Dead insects were counted daily and removed . At least three independent experiments were performed , each carried out with more than 50 female individuals . For oral bacterial infections , E . cloacae , S . aureus and E . coli were cultured overnight in LB medium , washed twice in PBS , and re-suspended in 1% sucrose solution . The suspension was placed on cotton pads on which newly emerged mosquitoes were allowed to feed for 48 h . Freshly emerged mosquitoes were fed for 3 days with gentamycin ( 25 µg/ml ) diluted in water and placed on a cotton pad . Sugar cubes were used as a sugar source . Fresh antibiotics solution was provided every 12 h . Mosquitoes remained on antibiotics for 2 more days before being infected with the P . berghei parasite as described above . For gene silencing , dsRNA was injected into mosquitoes 24 h after the beginning of the antibiotics treatment . A modified protocol from [42] was used . Red amine conjugated polystyrene beads ( 0 . 2 µm diameter ( Molecular Probes ) were washed twice in PBS and resuspended in PBS in the original volume . Adult females were injected with 100 nl into the thorax using a nano-injector ( Nanoject , Drummond ) . One day later E . coli or S . aureus were prepared at OD600 = 1 and 69 nl were injected in each mosquito . The experiment was repeated three times and the percent survival rates were averaged and statistically analyzed using the Log rank ( Mandel-Cox ) and the Gehan-Breslow-Wilcoxon tests . Five adult female mosquitoes were surface-sterilized in 70% ethanol for 5 min and rinsed three times in sterile saline ( 0 . 9% NaCl solution ) as described [22] . Genomic DNA was extracted using the QIAamp DNA Mini Kit ( QIAGEN ) and quantified by real-time PCR using the BSF340/19 and BSF806/26 oligonucleotide primers to the bacterial rDNA ( Table S1 ) , the SYBR Green PCR mastermix and the ABI PRISM 7700 sequence detection system following the manufacturer's instructions . The A . gambiae S7 gene was used as a reference . Mosquitoes were fed at 21°C for 15 min on TO mice infected with GFP-expressing P . berghei [21] . Midguts were dissected 10 days later , fixed for 45 min in 4% formaldehyde and mounted on glass slides in Vectashield . Fluorescent and melanized parasites were counted under fluorescence microscope . For P . falciparum infections , schoolchildren of Mfou ( 30 km east of Yaoundé ) were screened for gametocyte presence by thick bloodsmears stained with Giemsa . Blood from children with >20 gametocytes/µl was drawn by venipuncture . The serum was separated by centrifugation and replaced by non-immune AB serum . Mosquitoes were fed for 30 min on a 38°C-warm blood feeder . Midguts dissected 8 days later were stained with 0 . 4% Mercurochrome and developing oocysts were counted . Anopheles and Drosophila survival after infections with bacteria were analyzed using the Log-rank ( Mantel-Cox ) and Gehan-Breslow-Wilcoxon tests of the statistical package Prism version 5 . 0 ( GraphPad Software Inc . ) . Survival rates at the various time points were averaged between biological replicates after being transformed into percentages , and average survival curves were constructed and compared . The median of Plasmodium infection densities were analyzed using the non-parametric Mann Whitney test of Prism version 5 . 0 , and the prevalence of infection and melanized ookinetes were analyzed using the Fisher's exact test . Homology models were constructed in SwissModel after structural alignment of PGRP domains with Tcoffee [43] followed by manual adjustments . PD-loop modeling and optimization was performed in MODELLER 9v1 using the Discrete Optimized Protein Energy method [44] . PD-loop prediction methods included: a template-based protocol in Robetta ( http://robetta . bakerlab . org ) which uses the de novo Rosetta fragment insertion method [45]; and an implementation of ARIA [46] , applying a Cartesian MD and simulated annealing protocol with a high-temperature torsion angle dynamics ( TAD ) stage ( 2000 K , 36 ps ) , two cooling steps ( 2000 to 1000 K over 30 ps and 1000 to 50 K over 24 ps ) and a final energy minimization ( 200 steps ) of the MODELLER-derived PD-loop model while all other atomic positions were restrained . An ensemble of 100 loop conformers was calculated and 10 conformers with the lowest total energy were analyzed . To model TCT binding , TCT was docked in the PGN-binding groove of AgPGRP models according to its binding position in PGRP-LCx-TCT-LCa . TCT-protein interactions were optimized manually using O [47] and the Penultimate rotamer library [48] for putative side chain interactions . The conformation of identical TCT-interacting residues in AgPGRP models and PGRP-LCx was not changed and the side-chain χ1 angles of non-identical residues were maintained when possible . TCT torsions were allowed only to relieved clashes and did not compromise predicted binding . Liganded models were subjected to 200 cycles of conjugate gradient energy minimization in CNSsolve1 . 2 [49] . Similar steps were followed to model Lys-PGN binding , using a complex structure of hPGRP-IαC with a Lys-type MTP [50] . Model quality and intermolecular clashes analysis were performed with PROCHECK [51] and MolProbity [52] . Ligand-protein interactions were analyzed with LIGPLOT [53] . Final models had good stereochemistry without residues in non-permissive areas of Ramachandran plots . To obtain AgPGRPLC/a models , the portions of helix α2 and N-term segment of LCa mediating contacts with LCx were modeled using SwissModel to map corresponding AgPGRPLC sequences , which accounted for induced fit upon dimerization . Side-chain conformations of identical residues at the dimer interface were altered only if necessary for non-identical residues by manual manipulations in O . AgPGRPLC/a models were positioned by superimposing backbone atoms of helix α2 and the N-term of AgPGRPLC/x models onto the corresponding regions of LCa using LSQMAN [54] . Inter-monomer contact analysis was performed with LIGPLOT and MolProbity . Figures were generated with Pymol [55] .
|
Recognition of peptidoglycan on the bacteria cell wall triggers insect immune responses . The fruit fly PGRPLC receptor protein senses the presence of peptidoglycan and activates a pathway that mediates resistance to bacterial infections , mainly Gram-negative . We show that the PGRPLC receptor of the malaria vector mosquito Anopheles gambiae can also sense infections of the hemolymph ( the mosquito blood ) or the gut with bacteria of both Gram types and thereby activate a pathway that confers resistance to these infections . PGRPLC and its downstream responses also control the numbers of symbiotic bacteria that are mostly found in the mosquito gut where they drastically proliferate after a female mosquito takes a bloodmeal . Importantly , when the bloodmeal is infected with malaria parasites , the defense reaction that the mosquito mounts against proliferating bacteria also eliminate a large number of parasites . These mechanisms are largely elucidated using a rodent malaria parasite , but we also show that they significantly affect the intensities of mosquito infections with Plasmodium falciparum parasites found in the blood of children in sub-Saharan Africa .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/immunity",
"to",
"infections",
"genetics",
"and",
"genomics/genetics",
"of",
"the",
"immune",
"system",
"immunology/immune",
"response",
"microbiology/innate",
"immunity",
"immunology/innate",
"immunity",
"genetics",
"and",
"genomics/disease",
"models",
"microbiology/parasitology",
"infectious",
"diseases/protozoal",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2009
|
Anopheles gambiae PGRPLC-Mediated Defense against Bacteria Modulates Infections with Malaria Parasites
|
The mechanistic target of rapamycin ( mTOR ) integrates both intracellular and extracellular signals to regulate cell growth and metabolism . However , the role of mTOR signaling in osteoblast differentiation and bone formation is undefined , and the underlying mechanisms have not been elucidated . Here , we report that activation of mTOR complex 1 ( mTORC1 ) is required for preosteoblast proliferation; however , inactivation of mTORC1 is essential for their differentiation and maturation . Inhibition of mTORC1 prevented preosteoblast proliferation , but enhanced their differentiation in vitro and in mice . Activation of mTORC1 by deletion of tuberous sclerosis 1 ( Tsc1 ) in preosteoblasts produced immature woven bone in mice due to excess proliferation but impaired differentiation and maturation of the cells . The mTORC1-specific inhibitor , rapamycin , restored these in vitro and in vivo phenotypic changes . Mechanistically , mTORC1 prevented osteoblast maturation through activation of the STAT3/p63/Jagged/Notch pathway and downregulation of Runx2 . Preosteoblasts with hyperactive mTORC1 reacquired the capacity to fully differentiate and maturate when subjected to inhibition of the Notch pathway . Together , these findings identified the role of mTORC1 in osteoblast formation and established that mTORC1 prevents preosteoblast differentiation and maturation through activation of the Notch pathway .
The skeleton is a highly specialized and dynamic structure undergoing constant remodeling [1] . The remodeling process is executed by temporary cellular structures that comprise teams of coupled osteoblasts and osteoclasts . The rate of genesis as well as death of these two cell types is vital for the maintenance of bone homeostasis [2] , and common metabolic bone disorders such as osteoporosis are largely caused by a derangement in the proliferation , differentiation or apoptosis of these cells [3] . Osteoblasts , which are the chief bone-making cells , differentiate and produce bone matrix during skeletal development [4] . The differentiation process of osteoblasts is often divided into stages of mesenchymal progenitors , preosteoblasts and osteoblasts ( often called mature osteoblasts ) [5] . Osteoblasts are often characterized by the expression of osteocalcin , while preosteoblasts are usually considered to express the transcription factor Runx2 or both Runx2 and osterix ( Osx ) . Preosteoblasts have been shown to actively divide in vitro and are multipotent in differentiating , thus proliferative expansion and osteoblastic differentiation of preosteoblasts are essential for bone formation . Understanding the intracellular signaling pathways that control preosteoblast proliferation and differentiation is critical for preventing bone loss-related disease caused by impaired bone formation such as senile osteoporosis . The mechanistic target of rapamycin ( mTOR ) is a conserved Ser/Thr kinase nucleating at least two distinct multi-protein complexes , mTOR complex 1 ( mTORC1 ) and mTOR complex 2 ( mTORC2 ) [6] . mTORC1 uniquely contains raptor and is the sensitive target of rapamycin , it integrates both intracellular and extracellular signals , including growth factors , nutrients , energy levels , and cellular stress [7] . The tuberous sclerosis 1 ( TSC1 ) -TSC2-TBC1D7 ( TSC-TBC ) complex is the major upstream inhibitory regulator of mTORC1 [8] , and loss of this complex causes cells and tissues to display constitutive mTORC1 activation . TSC-TBC accelerates the intrinsic rate of GTP hydrolysis of Rheb , converting Rheb from the GTP-bound ( active ) to the GDP-bound ( inactive ) form . The active GTP-bound form of Rheb directly interacts with mTORC1 to stimulate its activity . mTORC1 phosphorylates the translational regulators , eukaryotic initiation factor 4E-binding protein-1 ( 4E-BP1 ) and S6 kinase 1 ( S6K1 ) , to regulate biosynthesis of proteins and modulates autophagy , biosynthesis of lipids and organelles and mitochondrial metabolism as well , through which mTORC1 exerts an essential role in regulating cell metabolism , survival , growth and proliferation [6 , 9] . mTORC1 signaling has emerged as a critical regulator of bone formation . Patients with tuberous sclerosis due to mutation in TSC1/2 present with sclerotic and lytic bone changes [10 , 11 , 12 , 13 , 14] . Moreover , mTOR has recently been identified among genes and pathways that are connected with human skeletal growth [15] . However , results from in vitro and in vivo studies on the function of mTORC1 in osteoblast lineage are inconsistent . The mTORC1 inhibitor , rapamycin , showed controversial capacity to influence the differentiation of various osteoblastic lineage cell lines in vitro [16 , 17 , 18 , 19 , 20 , 21 , 22] and demonstrated inconsistent potential for bone formation in vivo [23 , 24 , 25] . In addition to an undefined function in the osteoblast lineage , the mechanisms through which mTORC1 modulates osteoblast differentiation and bone formation are unknown . Using conditional Tsc1 knockout cell and mouse models , we demonstrated that mTORC1 activation is crucial for preosteoblast proliferation , but prevents their differentiation and maturation . Notch signaling mediates communication between neighboring cells to control cell fate decision [26 , 27] . Notch has been reported to be positively regulated by mTORC1 in various cell lines and be responsible for the impaired cell differentiation by mTORC1 [28 , 29 , 30] . We determined here that it is true in osteoblast lineage cells as well . Our mechanistic studies revealed that Notch signaling is strongly activated by mTORC1 to inhibit osteoblastic transcription factor Runx2 and prevent preosteoblast maturation and bone formation .
mTORC1 is known to promote cell proliferation in many types of cells . To examine the relationship between preosteoblast proliferation and mTORC1 activity , we analyzed the level of mTORC1 during the proliferative expansion of MC3T3-E1 cells , a murine preosteoblast cell line [31] , and subsequent cessation of growth . We counted cells each day to monitor their growth and observed that the cells reached confluence after 6–7 days , when proliferation ceased due to contact inhibition ( Fig 1A ) . Western blot analysis reflected this growth inhibition as a decrease in the levels of the cell cycle markers , cyclin D1 and proliferative cell nuclear antigen ( PCNA ) . As expected , the high level of mTORC1 activity ( indicated by P-S6K ( T389 ) and P-S6 ( S235/236 ) ) decreased when proliferation of MC3T3-E1 cells slowed down and eventually ceased ( Fig 1B ) . Cells did not differentiate during this growth period , as the level of osterix ( Osx , a marker of early osteoblasts ) was unchanged throughout . These data indicate that the level of mTORC1 activity was positively correlated with the rate of preosteoblast proliferation . We then induced osteoblastic differentiation in confluent MC3T3-E1 cells . The induced cells showed a decreased mTORC1 activity during osteoblast differentiation in parallel with increases in markers of osteoblast differentiation ( i . e . osterix ( Osx ) and osteocalcin ( Ocn ) ) ( Fig 1C ) . A similar pattern of mTORC1 activity was observed in osteoblastic induced fetal rat calvarial cells ( Fig 1D ) , which implicated that mTORC1 activity is not required for osteoblastic differentiation of preosteoblasts . We next investigated the role of reduced mTORC1 activity caused by rapamycin in the proliferation and differentiation of preosteoblasts . As shown in Fig 1A , the growth of cells treated with 0 . 1 nM rapamycin significantly lagged behind control cells , and the decreased level of proliferative markers ( cyclin D1 and PCNA ) in rapamycin-treated cells revealed the underlying mechanism ( Fig 2A ) . We next determined the role of reduced mTORC1 activity in the differentiation of preosteoblasts . As seen in Fig 2B , a low concentration of rapamycin ( 0 . 1nM ) increased the expression of osterix and osteocalcin . Separate sets of cells were tested for mineralization capacity , a terminal differentiation parameter for osteoblasts , by staining with alizarin red , and the results confirmed enhanced mineralization of the extracellular matrix ( ECM ) in MC3T3-E1 cells with impaired mTORC1 activity ( Fig 2C ) . Increased mineralization of ECM was also observed in fetal rat calvarial cells treated with 0 . 1nM rapamycin ( S1 Fig ) . To test the results in vivo , we subcutaneously injected newborn wild-type C57BL/6 mice with rapamycin daily ( 0 . 1 mg/kg body weight/day ) for 10 weeks . mTORC1 activity was efficiently down-regulated in the primary spongiosa as observed by the reduction in immunohistochemistry staining for S6 phosphorylation ( Ser235/236 ) in rapamycin-treated mice ( Fig 2D ) . Micro-CT analysis of the distal femur showed a marked decrease in bone mass in the rapamycin-treated mice ( Fig 2E ) , as demonstrated by a significant decrease in BV/TV , trabecular number or trabecular thickness , coupled with an increase in trabecular separation ( Table 1 ) . In line with the reduced trabecular bone mass , mice treated with rapamycin showed decreased cortical thickness as well as smaller outer and inner femoral mid-shaft bone perimeters when compared with controls ( Table 1 ) . The decrease in cortical thickness was due to deficient periosteal apposition , as evidenced by a decrease in the outer perimeter of the mid-shaft , while the inner perimeter was also decreased . We then analyzed the cellular basis of decreased bone mass in rapamycin-treated mice . Tartrate-resistant acid phosphatase ( TRAP ) staining on femoral sections revealed a reduction in osteoclasts within the trabecular bone region in rapamycin-treated mice ( S2 Fig ) . Thus , decreased bone mass in the rapamycin-treated mice was not caused by an overall decrease in bone resorption . We next investigated bone formation parameters by first examining the total number of osteoblasts . ALP staining of the distal femoral section from rapamycin-treated mice showed a marked reduction in osteoblastic lineage cells when normalized to the bone perimeter ( Fig 2F and 2G ) . Immunohistochemistry staining for BrdU revealed that proliferation of osteoblastic lineage cells was attenuated in rapamycin-treated mice ( Fig 2H and 2I ) . To determine if differentiation of osteoblasts was affected , we next measured their numbers at different stages of differentiation by immunostaining femur sections from rapamycin-treated mice and controls . The number of osterix-positive preosteoblasts ( Fig 2J and 2K ) and osteocalcin-positive mature osteoblasts ( Fig 2L and 2M ) on the bone surfaces of rapamycin-treated mice were less than those in the controls . However , the mean density of osterix and osteocalcin-positive cells was increased in mice treated with rapamycin ( Fig 2K and 2M ) , indicating that expression of osterix and osteocalcin in single osteoblasts was increased in rapamycin-treated mice . Thus , in agreement with the in vitro results , rapamycin attenuated proliferation of preosteoblasts but promoted their differentiation in vivo . To characterize the role of mTORC1 activation in the regulation of proliferation and differentiation of osteogenic progenitors , we generated mice in which mTORC1 were selectively activated in osteoprogenitor cells committed to the osteoblast lineage . To achieve such cell type-specific knockout , we crossed floxed Tsc1 mice with Osx-GFP::Cre mice ( which express a GFP-Cre fusion protein under the direction of the Osx1 promoter ) to generate conditional Tsc1 knockout mice . We mated Osx-GFP::CreTG/+;Tsc1flox/+ mice and selected female mice with the genotype Osx-GFP::CreTG/+;Tsc1flox/flox ( hereafter , referred to as ΔTsc1 ) for detailed analysis . Female Osx-GFP::CreTG/+;Tsc1+/+ littermates served as controls . ΔTsc1 mice were born at the expected Mendelian frequency , and recombination of Tsc1 alleles only occurred in skeletal tissues ( i . e . , legs and skull ) as demonstrated by allele specific PCR ( Fig 3A ) . Immunohistochemical staining of distal femur sections showed a dramatic increase in S6 phosphorylation ( Ser235/236 ) in ΔTsc1 mice ( Fig 3B ) , indicating that mTORC1 was activated by Tsc1 disruption . At the gross level , 4-week-old ΔTsc1 mice demonstrated square skulls and prominent dwarfism ( Fig 3C and 3D ) . The body weight of the Tsc1-deficient mice was significantly lower than that of their control littermates , indicating retardation of growth in the mutant mice ( Fig 3E ) . Whole body X-ray analysis showed increased bone mass in the skull , vertebrae and long bones ( Fig 3D ) . Because of thickened cortical bone , pale bones were observed in ΔTsc1 mice ( S3 Fig ) . Micro-CT analysis of the distal regions of the femur confirmed a marked increase in cancellous bone mass in ΔTsc1 mice ( Fig 3F ) , as reflected in a 240% , 140% and 170% increase in BV/TV , trabecular number and trabecular thickness , respectively , coupled with a 50% decrease in trabecular separation . Analysis of femoral mid-shaft revealed a similar increase in cortical bone mass in ΔTsc1 mice , as reflected in a 230% and 130% increase in cortical thickness and outer perimeter , respectively , and a 90% decrease in inner perimeter ( Table 2 ) . Although bone mass was increased in ΔTsc1 mice , micro-CT radiograms showed more porous areas in the bones of mutant mice ( Fig 3F ) , which resulted in a uniform 90% decrease in bone mineral density ( BMD ) in cancellous and cortical bone ( Table 2 ) . The appearance of more porous areas in mutant mice was the result of increased areas of hypomineralization . As shown by Goldner’s Masson trichrome staining , ΔTsc1 mice had 35% more osteoid/hypomineralized areas ( stained red ) in bone ( Fig 3G and 3H ) . Together , these data suggest that mTORC1 activation in osteogenic progenitors stimulates these progenitors to produce increased amounts of immature woven bone . We then analyzed the cellular basis for the increased amounts of immature woven bone in ΔTsc1 mice . ALP staining of 10-week-old ΔTsc1 mouse femurs indicated an increased number of osteoblast lineage cells ( Fig 4A and 4B ) . To determine the underlying molecular mechanism of the increase in osteoblasts in transgenic bone , we cultured postnatal day 3 calvarial osteoblasts and found a significant increase in BrdU-positive cells , indicating increased cellular proliferation in ΔTsc1 calvarial osteoblasts ( S4 Fig ) . In addition , the percentage of proliferative osteoblasts on bone surface was increased in ΔTsc1 mice ( Fig 4C and 4D ) . We next determined whether the differentiation of preosteoblasts was influenced in ΔTsc1 mice . Osteoblasts in ΔTsc1 mice lost normal morphology and appeared immature as shown by scanning electron microscopy ( SEM ) analyses ( S5 Fig ) , indicating a faulty maturation process from precursors to osteoblasts . Immunohistochemical staining of distal femur sections showed decreased expression of osterix ( Fig 4E and 4F ) and osteocalcin ( Fig 4G and 4H ) , indicating impaired differentiation of preosteoblasts in mutant mice . To detect osteoblast activity , we performed double fluorochrome labeling analyses . Incorporation of the two fluorochromes was evident in the control mice bone , while mutants’ bone displayed diffuse fluorochrome labeling , a characteristic feature of immature woven bone . Although mineralizing surface was dramatically increased abnormally in cortical bone , distance between calcin-labeled mineralization fronts at endosteum of the midshaft of femur was smaller in ΔTsc1 mice than that in the controls ( Fig 4I ) . Histomorphometric measurements showed that the endosteum mineral apposition rate ( MAR ) of ΔTsc1 mice was lower than that of controls ( Fig 4J ) , suggesting impaired performance of individual osteoblasts in ΔTsc1 mice . To further examine the impact of mTORC1 activation on osteoblastic precursor differentiation , we used primary calvarial preosteoblast cultures in which Tsc1 was eliminated prior to the induction of osteoblast differentiation due to suppression of Osx-Cre by doxycycline to specifically delete Tsc1 . On the day the cells reached confluence ( day -3 ) , mTORC1 was not activated ( as indicated by unchanged phosphorylation of S6K and S6 ) and markers of differentiated osteoblasts ( Osx and Ocn ) remained normal in ΔTsc1 cells compared with the controls . Doxycycline was then discontinued to activate Osx-Cre and delete Tsc1 and osteoblastic differentiation was induced for another 14 days . Total proteins were extracted for western blot on different days after induction ( day 0 , 7 , 14 ) . Osteoblasts lacking Tsc1 exhibited the expected activation of mTORC1 and reduced expression of osterix and osteocalcin ( Fig 4K ) . Alizarin red staining confirmed that ΔTsc1 mice exhibited a marked decreased capacity to form mineralized nodules ( Fig 4L ) . Notably , osteoclast numbers on the bone surface ( OC . N/B . Pm ) were significantly decreased in mutant mice ( S6 Fig ) . These data suggest that mTORC1 activation in preosteoblasts promotes their proliferation but prevents their differentiation and decreases osteoclast number by an undefined mechanism . To identify whether the increased amount of immature woven bone was mTORC1-dependent , ΔTsc1 mice were administered with rapamycin prenatally from E13 . 5 ( the approximate day of Osx-Cre expression ) and then after birth until 10 weeks old . After treatment with 0 . 1 mg/kg/day rapamycin , S6 phosphorylation ( Ser235/236 ) was significantly decreased in the primary spongiosa ( Fig 5A ) , accelerated proliferation was reduced to normal level ( Fig 4C and 4D ) , increased osteoblastic lineage cells with ALP ( Fig 4A and 4B ) were reduced to normal , expression of osterix ( Fig 4E and 4F ) and osteocalcin ( Fig 4G and 4H ) was increased , diffuse and narrowed distance between fluorochrome labeling was distinct and broadened ( Fig 4I and 4J ) , high bone mass was notably reduced ( Fig 5B ) , narrowed bone marrow cavity was expanded ( Fig 5C ) , areas of hypomineralization were decreased to normal ( Fig 5D and 5E ) and bone architecture was partially normalized ( Table 3 ) . These results indicate that preosteoblasts in ΔTsc1 mice re-acquired the ability to differentiate , and form normal bone . The results of rapamycin treatment in differentiating calvarial cells confirmed these findings . Following treatment with a low concentration ( 0 . 1 nM ) of rapamycin for 14 days , decreased expression of osterix and osteocalcin was elevated ( Fig 5F ) and mineralized nodules were formed increasingly ( Fig 5G ) in ΔTsc1 cells . We next investigated the mechanism by which mTORC1 regulates osteoblast differentiation . Runx2 , a Runt domain-containing transcription factor , is required for commitment of mesenchymal osteochondroprogenitors to the osteoblastic lineage , differentiation into mature osteoblasts and terminal differentiation into osteocytes [32] . ΔTsc1 calvarial cells exhibited a reduced Runx2 expression level , while rapamycin treatment led to an increase in Runx2 above baseline ( Fig 6A ) . The regulation of Runx2 by mTORC1 was further confirmed in ΔTsc1 mice ( Fig 6B and 6C ) . Thus , mTORC1 attenuated the expression of Runx2 in vitro and in vivo , and the delay in osteoblast differentiation was probably due to repression of Runx2 in ΔTsc1 mice . In consideration of the same negative role of Notch signaling in osteoblast differentiation [32 , 33 , 34 , 35] as mTORC1 signaling and the positive correlation between Notch and mTORC1[28 , 29 , 30] , we next investigated the potential role of the Notch pathway in the mechanism underlying the impaired differentiation potential of mTORC1-activated cells . Jagged1 ( a Notch ligand ) protein expression was significantly enhanced in primary ΔTsc1 calvarial cells , and rapamycin reduced this expression to normal ( Fig 6D ) and dose-dependently decreased its expression in MC3T3-E1 cells ( Fig 6E ) . In addition , a similar pattern of expression of the Notch transactivator NICD domain and Hes1 ( a direct target of Notch ) was also observed in these cells ( Fig 6D and 6E ) . These results demonstrated that mTORC1 activated Notch signaling in preosteoblasts . As the Notch pathway has been reported to reduce Runx2 activity [32] , we next determined whether Notch signaling impaired differentiation of preosteoblasts upstream of Runx2 . We found increased Runx2 expression in differentiating MC3T3-E1 and primary cavarial cells following suppression of the Notch pathway using the Notch inhibitor N-[N- ( 3 , 5-difluorophenacetyl-L-alanyl ) ]-S-phenylglycinet-butylester ( DAPT ) ( Fig 6F and 6H ) and Notch siRNA ( Fig 6G and 6I ) . We conclude that mTORC1 inhibited Runx2 expression by activating Notch signaling in preosteoblasts . To define how activated mTORC1 influenced the Jagged1/Notch/Hes1 pathway , we determined the expression of STAT3 and p63 , as STAT3 is a transcriptional activator of p63 [36] as well as the downstream target of mTOR [37] , and p63 is a positive regulator of Jagged expression and Notch activity [38 , 39 , 40] which is induced by PI3K [41] . Cavarial cells with disruption of Tsc1 showed increased phosphorylation of STAT3 on Ser727 and expression of TP63 ( total p63 ) ( Fig 7A ) , and rapamycin reduced these parameters to normal levels . Knockdown of STAT3 by siRNA led to reduction in p63 , Jagged1 and Hes1 without affecting the activity of mTORC1 ( Fig 7B ) , and downregulation of p63 resulted in reduced Jagged1 and Hes1 but no changes of STAT3 phosphorylation and mTORC1 activity ( Fig 7C ) . These findings suggested that the STAT3/p63 cascade is positively regulated by mTORC1 and serves as a conjunction between mTORC1 and the Notch pathway . We next sought to define how mTORC1 regulates STAT3/p63 cascade in osteoblasts . As has been reported that mTORC1 directly phosphorylates STAT3 at Ser727 [37] , we firstly examined whether mTORC1 could phosphorylate STAT3 at Ser727 in osteoblasts . We immunoprecipitated mTOR complex from control and ΔTsc1 calvarial cells and subjected the immunoprecipitates to in vitro kinase assays using a recombinant GST-tagged full-length STAT3 peptide . As shown in Fig 7D , mTOR immunoprecipitates could phosphorylate STAT3 at Ser727 in vitro and constitutive activated mTORC1 from ΔTsc1 calvarial cells enhanced the phosphorylation . Thus the elevated phosphorylation of STAT3 ( S727 ) in ΔTsc1 osteoblasts is due to increased mTOR kinase activity . Next , we examined the role of phosphorylated STAT3 ( S727 ) in the regulation of the p63 gene transcription by electrophoretic mobility shift assay ( EMSA ) . Nuclear protein of osteoblasts bound specifically to a double-strand probe containing a consensus STAT3-specific binding sequence in the promoter region of ΔNp63 [36] , and anti-pSTAT3 ( S727 ) antibody significantly reduced the binding of STAT3 to p63 promoter ( Fig 7E ) . Accordingly , nuclear protein from ΔTsc1 osteoblasts showed an increased binding of pSTAT3 ( S727 ) to the probe ( Fig 7E ) . Taken together , these results suggest that phosphorylated STAT3 ( S727 ) may bind to p63 promoter to regulate its transcription in osteoblast , and mTORC1 activates the Notch pathway through STAT3/p63/Jagged cascade . As mTOR is a positive regulator of the Jagged1/Notch/Hes1 pathway , we next determined whether hyperactive Notch signaling was responsible for the impaired differentiation of preosteoblasts by mTORC1 activation . Firstly , we examined the correlation between the level of Notch activity and differentiation of preosteoblasts . Low activity of Notch signaling is required during differentiation of preosteoblasts , as the expression of Jagged1 , NICD and Hes1 was decreased in parallel with an increase in the markers of osteoblast differentiation ( Fig 8A ) , and inhibition of the Notch pathway by DAPT or si-Notch1 promoted osteocalcin expression ( S7A Fig and Fig 8B ) and mineralized nodules formation in MC3T3-E1 cells ( S7B Fig and Fig 8C ) . Importantly , a reduction in Notch by DAPT ( S7C and S7D Fig ) or siRNA ( Fig 8D and 8E ) potentiated the differentiation of ΔTsc1 preosteoblasts . To test these results in vivo , we administered 6-week old mice with DAPT or equivalent volume of DMSO for 4 weeks . Micro-CT analysis of the distal femur showed a marked normalizing of bone architecture in DAPT-treated ΔTsc1 mice ( Fig 8F and 8G and Table 4 ) when compared to their DMSO-treated control . Inhibition of Notch pathway by DAPT elevated expression of Runx2 and subsequent osterix and osteocalcin in osteoblasts ( Fig 8H–8M ) , and significantly reversed the bone phenotypes of ΔTsc1 mice ( Table 4 ) . Therefore , Notch acts as an inhibitor of osteoblast differentiation , while deregulated mTORC1 activation impairs preosteoblast differentiation through activation of the Notch signaling pathway . Together , these in vitro and in vivo loss- and gain-of-function studies lend support for a central role of mTORC1 signaling in regulating both proliferation and differentiation of preosteoblast during bone homeostasis ( S8 Fig ) .
In this study , we defined the essential role of mTORC1 signaling in osteoblastgenesis . We observed a decline in mTORC1 activity during differentiation of preosteoblasts and enhancement of osteoblastic differentiation following inhibition of mTORC1 in vitro and in vivo . Using conditional knockout cell and mouse models , we further revealed that activation of mTORC1 prevented preosteoblast differentiation through activation of the Notch signaling pathway . mTORC1 is ubiquitously expressed in all types of cells to regulate growth and metabolism . Proliferation in many types of cells is promoted by mTORC1 , thus it is not surprising that the activity of mTORC1 positively correlated with the proliferative rate of preosteoblasts . However , it is interesting that mTORC1 activity resulted in a decline in the differentiation of preosteoblasts . We speculate that the decline in mTORC1 activity is due to differentiated osteoblasts . As osteoblasts differentiate , calcification of the extracellular matrix gradually increases , which generates hyperosmolarity and cellular stress . Coincidentally , this type of stimulus has been shown to inhibit mTORC1 [42] . The role of mTORC1 in bone formation was originally identified in various cell lines and animals treated with rapamycin , however , the results were controversial . Depending on the cell type , rapamycin either stimulates or inhibits osteoblastic differentiation . In rat osteosarcoma ( ROS 17/2 . 8 ) cells , rapamycin inhibited proliferation , but promoted osteogenic differentiation [16] . In C2C12 cells , rapamycin potentiated the effect of BMP-2 inducing late markers of osteoblast differentiation [17] . In Human Embryonic Stem Cells , rapamycin induced the up-regulation of early osteogenic markers and further promoted the expression of late osteoblastic marker mRNA and/or proteins and mineralized bone nodule formation following induction for 2–3 weeks [43] . On the other hand , rapamycin has been shown to block osteogenic protein-1 induction of alkaline phosphatase activity in fetal rat calvarial cells [19] and reduce alkaline phosphatase activity , osteocalcin expression and the calcium content in mesenchymal stem cells [20] . In MC3T3-E1 subclone 4 ( MC-4 ) cells and primary mouse bone marrow stromal cells ( BMSCs ) , rapamycin inhibited osteoblast differentiation by targeting osteoblast proliferation and the early stage of osteoblast differentiation [21] . Although the reasons for the discrepant results are not readily apparent , one possible explanation is that the cells used in these reports were in various stages of osteoblastic differentiation and mTORC1 activity is stage-specifically required during the differentiation process . The results of the present study demonstrated that inhibition of mTORC1 is essential for preosteoblast differentiation . Another possible explanation for these contradictory results could be related to the treatment conditions including concentration and duration . Rapamycin at a concentration as low as 0 . 1 nM is effective in significantly suppressing mTORC1 activity [21] , while higher concentrations and long-term treatment may produce a decrease in cell viability and growth , and inhibition of mTORC2 . Thus , we used a low dose of rapamycin ( 0 . 1 nM ) as a precaution against non-specific effects , and obtained reliable results that low mTORC1 activity is crucial for osteogenesis . Previous results in animals administered rapamycin have also been inconsistent . Administration of rapamycin for 14 days resulted in no change in cancellous bone volume in rats [23] , and after a longer duration of treatment ( 28 days ) , rats still lacked an osteopenic phenotype despite increased bone turnover [24] . However , mice treated with a similar concentration and duration of treatment showed a phenotype with less new bone formation and lower trabecular bone mass [25] , due to a reduced number of osterix and osteocalcin positive cells coupled with unaffected osteoclasts . In the present study , rapamycin-treated mice had a similar phenotype of bone loss as well as decreased numbers of osterix and osteocalcin positive cells , except that the mean density of these cells was enhanced and the number of osteoclasts was reduced by rapamycin . Although we could not rule out that the systemic effect of rapamycin acted on other cell types to affect bone mass indirectly , the results of rapamycin administration in vivo are in agreement with those from rapamycin treated cells in vitro , which consolidates our conclusions that hyperactive mTORC1 is not required for differentiation of preosteoblasts and rapamycin promotes preosteoblast differentiation by blocking mTORC1 . Clinically , rapamycin was initially identified as a macrocyclic antifungal agent [44] and is used for immunosuppression in transplantation . The experimental and clinical trials have showed that rapamycin reduced brain , kidney , and skin lesions of tuberous sclerosis complex ( TSC ) [45 , 46] . Some TSC children are being started on rapamycin at a young age ( < 5 years ) , and it is sometimes continued for many ( >5 ) years [47 , 48] . In this situation , bone densitometry and morphological measurements must be advised to monitor the possible side effects of rapamycin on bone . Rapamycin is likely to impair bone microarchitecture and quality of those children as indicated by the current study , although it presented no effect on growth in weight and height of them [48] . Recent mouse genetic studies also highlighted the critical role of mTORC1 in skeletal development , whereas the function of mTORC1 in osteoblast formation is uncertain and the underlying mechanisms are not defined . Mice with Pten [49] and Tsc2 [50] disrupted by OC ( osteocalcin ) -cre recombinase ( ΔTsc2 ) in mature osteoblasts shared an elevated mTORC1 activity , and exhibited different phenotypes . Although both mouse models had uniform high bone mass , osteoblasts with deletion of Pten showed increased osteoblastic differentiation , while Tsc2 disruption led to impaired differentiation of osteoblasts and mineralized nodule formation . Because inhibition of mTORC1 by rapamycin restored the differentiation defects in osteoblasts due to disruption of Tsc2 , we reason that impairing osteoblastic differentiation is the function of hyperactive mTORC1 in mature osteoblasts , while an mTORC1-independent mechanism may account for the distinct phenotype in mice with disruption of Pten . Akt , which has been reported to promote osteoblast differentiation and bone development [51] , is highly activated following Pten deletion and significantly inhibited following Tsc2 disruption . Acceleration of osteoblast differentiation by Akt may exceed the inhibition due to mTORC1 activation and cause increased differentiation in osteoblasts with Pten ablation . ΔTsc2 mice presented a similar phenotype and same differentiation defect in osteoblasts as ΔTsc1 mice in the current study . However , in contrast to ΔTsc1 mice , ΔTsc2 mice exhibited a decreased proliferation rate of osteoblast , which indicates that mTORC1 may play different role in regulating proliferation of immature and mature osteoblasts , since OC ( osteocalcin ) is expressed exclusively in mature osteoblasts . Moreover , though mTORC1 activation resulted in a same defect of differentiation in mature osteoblasts and preosteoblasts , the underlying mechanisms may be different . In this sense , our work revealed the role of mTORC1 in preosteoblast differentiation under physiological and pathological conditions and explored a different mechanism ( STAT3/p63/Jagged/Notch pathway ) responsible . More recently , another similar phenotype of immature woven bone as that of ΔTsc1 mice has been reported in mice with disruption of Lkb1 in preosteoblasts ( ΔLkb1 ) [52] , but the increased trabecular bone mass was more severe in ΔLkb1 mice than in ΔTsc1 mice , as BV/TV increased 7-fold versus 2 . 4-fold . In contrast , cortical thickness , which showed a 2 . 3-fold increase in ΔTsc1 mice , was decreased 3-fold in ΔLkb1 mice and osteoclasts were increased inversely in ΔLkb1 mice when compared with ΔTsc1 mice . These discrepancies may also be attributed to the activation of mTORC1-independent pathways in ΔLkb1 mice , as AMP kinase ( AMPK ) , the target of Lkb1 , regulates many signaling pathways besides mTORC1 . In addition , there is no evidence to show that rapamycin restores bone phenotypes in ΔLkb1 mice . Unlike Lkb1 , mTORC1 is a well-established target of TSC-TBC , and the phenotype of immature woven bone in ΔTsc1 mice was reversed by rapamycin , which further confirmed that bone changes in ΔTsc1 mice were the result of mTORC1 activation . Together , these findings demonstrate that differentiation of both preosteoblasts and osteoblasts is attenuated by mTORC1 activation . Our data suggest that the impaired differentiation of preosteoblasts following mTORC1 activation is due to over-activated Notch signaling downstream . Notch signaling mediates communication between neighboring cells and decides their fate [32 , 33 , 34 , 35 , 53] . Specifically , gain of Notch function in preosteoblasts under the control of the type I collagen ( Col1a1 ) promoter results in a phenotype similar to ΔTsc1 mice in the present study [32] . By inhibiting mTORC1 and the Notch pathway via rapamycin and DAPT/Si-Notch1 , respectively , we showed that Notch acts downstream of mTORC1 and mediates the attenuation of osteoblastic differentiation by mTORC1 . These findings are supported by a report which revealed that mTOR positively regulates Notch signaling in mouse and human cells through induction of the STAT3/p63/Jagged signaling cascade [28] . More recently , Wang et al . reported that mTORC1 activates Notch3 to accelerate the development of hypoxia-induced pulmonary hypertension [30] and Karbowniczek M et al . found that mTORC1 activates Notch in tuberous sclerosis complex and Drosophila external sensory organ development [54] . In contrast , Notch has also been shown to promote mTORC1 signaling by increasing Raptor protein expression in rat hepatoma cells and primary mouse hepatocytes [55] . We speculate that the interaction between mTORC1 and Notch may depend on cell type . Our data also confirmed that Runx2 is negatively regulated by mTORC1 and identified the Notch pathway as the responsible mechanism . Notch was identified as a major mediator of mTORC1 signaling in the impairment of preosteoblast differentiation in the present study . In summary , this study clarified the potential role of mTORC1 signaling in the regulation of preosteoblast proliferation and differentiation and identified Notch signaling and Runx2 as critical downstream mediators . Pharmaceutical coordination of the pathways and agents in preosteoblasts may be beneficial in bone formation .
The preosteoblast cell line , MC3T3-E1 , was maintained in alpha-MEM ( Gibco ) supplemented with 10% FBS ( Gibco ) , 100 U/ml penicillin , and 100 mg/ml streptomycin sulfate , at 37°C with 5% CO2 . For growth curve analysis , MC3T3-E1 cells were plated in six-well plates at a density of 8×104 cells/well and cultured until confluent ( 8th day ) . Growth rate was assessed by cell counting . Primary osteoblastic cells were prepared from the calvaria of 21-day-old fetal rats or newborn mice as described previously [56 , 57 , 58] and cultured using the same method as for MC3T3-E1 cells . For osteogenic induction , 100 μg/ ml ascorbic acid ( Sigma-Aldrich ) and 10 mM β-glycerol phosphate ( Sigma-Aldrich ) were added to confluent cells . Rapamycin ( Sigma-Aldrich ) was added as stated in the Results section . Alizarin red staining was carried out according to standard techniques . The Southern Medical University Animal Care and Use Committee approved all procedures involving mice . Mice importing , transporting , housing and breeding were conducted according to the recommendations of "The use of non-human primates in research . " Newborn C57BL/6 mice were purchased from the Laboratory Animal Centre of Southern Medical University . Tsc1flox/flox [59] and Osx-GFP::Cre [60] mice were both purchased from The Jackson Laboratory . The background of Tsc1flox/flox mice is 129S4/SvJae , and Osx-GFP::Cre mice were backcrossed on to a 129S4/SvJae background for 8 generations prior to use . We performed genotyping using genomic DNA isolated from tail biopsies , and the primers used are shown in S1 Table . The specificity of recombination was examined by PCR using primers flanking the floxed allele ( S1 Table ) . Mice were sacrificed by cervical dislocation to ameliorate suffering . Femur tissues dissected from the mice were fixed using 4% paraformaldehyde in PBS at 4°C for 24 hours and then decalcified in 15% EDTA ( pH 7 . 4 ) at 4°C for 14 days . The tissues were embedded in paraffin or optimal cutting temperature ( OCT ) compound ( Sakura Finetek ) , and 2–5 μm sagittal-oriented sections were prepared for histological analyses . H&E and Toluidine blue staining was performed as previously described [61] . Tartrate-resistant acid phosphatase ( TRAP ) or alkaline phosphatase ( ALP ) staining was performed using a standard protocol ( Sigma-Aldrich ) . For IHC , we incubated primary antibodies which recognized mouse phospho-S6 ribosomal protein ( Ser235/236 ) ( Cell Signaling , 1:100 , #2211 ) , proliferating cell nuclear antigen ( PCNA ) ( Cell Signaling 1:200 , #13110 ) , osterix ( Abcam , 1:500 , ab22552 ) , osteocalcin ( Abcam , 1:500 , ab93876 ) , and Runx2 ( Cell Signaling , 1:100 ) overnight at 4°C . All sections were observed and photographed on Olympus BX51 microscope . Immunohistochemical staining was evaluated by cell number counting and computerized optical density ( OD ) measurements . In proliferation analysis , osteoblast proliferation fraction was calculated as BrdU+ osteoblasts per total osteoblasts on bone surface . Osteoblasts on bone surface were discerned by morphology and calculated by two independent observers blinded to the groups . In immunohistochemistry assays , cells per bone perimeter ( B . Pm ) was used to calculate the number of positive cells , and integrated optical density per area of positive cells ( IOD/area , mean density ) was used to quantify the staining intensity by detecting in 6 different images taken at 100x magnification with Image Pro Plus 6 . 0 software ( Media Cybernetics , MD , USA ) [62] . Briefly , positively stained regions of the image were selected by HSI ( hue , saturation and lightness ) with S from 0–255 , I from 0–210 and H from 0–25 , and then the brown color was converted into grayscale signal . The grayscale signal was measured as mean optical intensity of staining ( mean density ) within the tissue masks . At least three mice per group were examined . Three equidistant sections spaced at 200 μm apart throughout the midsagittal section of femur were evaluated . The narcotized mice were analyzed using X-ray radiography . Quantitative analysis was performed in mice femora at 12 μm resolution on a micro-CT Scanner ( Viva CT40; Scanco Medical AG , Bassersdorf , Switzerland ) [63] . Briefly , scanning was performed at the lower growth plate in the femora and extended proximally for 300 slices . We started morphometric analysis with the first slice in which the femoral condyles were fully merged and extended for 100 slices proximally . Using a contouring tool , we segmented the trabecular bone from the cortical shell manually on key slices , and morphed the contours automatically to segment the trabecular bone on all slices . The three-dimensional structure and morphometry were constructed and analyzed for BV/TV ( % ) , BMD ( mg HA/mm3 ) , Tb . N . ( mm–1 ) , Tb . Th . ( mm ) and Tb . Sp ( mm ) . We also performed micro CT imaging in the mid-diaphysis of the femur and performed mid-shaft evaluation of 100 slices to quantify the cortical thickness , bone mineral density and outer/inner perimeter of the mid-shaft . Cells and tissues were lysated by 2% sodium dodecyl sulfate with 2 M urea , 10% glycerol , 10 mM Tris-HCl ( pH 6 . 8 ) , 10 mM dithiothreitol and 1 mM phenylmethylsulfonyl fluoride . The lysates were centrifuged and the supernatants were separated by SDS-PAGE and blotted onto a nitrocellulose ( NC ) membrane ( Bio-Rad Laboratories ) . The membrane was then analyzed using specific antibodies and visualized by enhanced chemiluminescence ( ECL Kit , Amersham Biosciences ) . To label the mineralization fronts , 10-week-old mice were subcutaneously injected with calcin ( Sigma , 15 mg/kg body weight ) in 2% sodium bicarbonate solution 10 days and 3 days before death [25] . After dissection , the femurs were fixed in 4% paraformaldehyde for 24 hours . They were then dehydrated through a graded series of ethanol ( 70–100% ) and xylene before being embedded in methylmethacrylate ( MMA ) without prior decalcification [64] . 5 μm-thick sections were prepared for Goldner’s-Masson trichrome [65] , and 10 μm-thick sections were prepared for double-labeling fluorescent analysis . After initial culture for 48 hours , primary calvarial cells were replated and expanded for an additional 24 hours . The cells were then treated with BrdU labeling reagent ( Invitrogen ) for 6 hours according to the manufacturer’s instructions and washed with PBS . The cells were fixed with 70% ethanol for 25 min at 4°C , and then stained for immunocytochemical analysis . Three to five areas for each group ( n = 3 slides ) were counted by two independent observers blinded to the groups . BrdU-positive cells were scored visually . For in vivo proliferation analysis , mice were injected with BrdU ( 1ml/100g body weight ) 2 hours before sacrifice . The surface of the MMA embedded femurs were polished and acid-etched with 37% phosphoric acid for 2–10s . After washing for 5 min with 5% sodium hypochlorite they were coated with gold and palladium before examining with SEM ( S-3700N , Hitachi , Japan ) . To prevent the Osx promoter from driving Cre expression , pregnant mice were exposed to 200 μg/ml doxycycline ( Sigma-Aldrich ) in drinking water until their progeny had been processed for calvarial cell isolation . Doxycycline 100 μg/ml was added sequentially to the isolated cells until they reached confluence . We transiently transfected cells with siRNA using Lipofectamine RNAi MAX ( Invitrogen , Carlsbad , CA , USA ) in Opti-MEM medium ( Invitrogen ) , according to the manufacturer’s instructions . The efficiency of transfection was measured by western blot . The sequences of siRNA used in this study were as follows: Notch1: sense: 5’-CCAAGAAGUUCCGGUUUGATT-3’ , and anti-sense: 5’-UCAAACCGGAACUUCUUGGTT-3’; STAT3: 5’-CTGGATAACTTCATTAGCA-3’; p63 ( all isoforms ) : 5’-CACAGACCACGCACAGAAUdTdT-3’ , 5’-UCCAGAUGACUUCCAUCAAdTdT-3’ ( 1:1 ratio mixture ) . Non-specific siRNA sequences were used as negative controls: sense: 5’-UUCUCCGAACGUGUCACGUTT-3’ , and anti-sense: 5’-ACGUGACACGUUCGGAGAATT-3’ . ( GenePharma , Shanghai , China ) . Primary calvarial cells were lysed in ice-cold buffer ( 40mM HEPES ( pH 7 . 4 ) , 2mM EDTA , 10mM pyrophosphate , 10mM glycerophosphate , 0 . 3% CHAPS , and one tablet of EDTA-free protease inhibitors ( Roche , Basel , Switzerland ) per 25 ml ) . Supernatants were incubated with anti-mTOR antibody for 2h at 4°C , followed by addition of 30 μl of 50% slurry of protein G Sepharose beads for another 1h . Beads were then washed four times with lysis buffer and once kinase buffer ( 25mM HEPES ( pH7 . 4 ) , 50mM KCl , 10mM MgCl2 , 250 μM ATP ) . 0 . 4μg of recombinant GST-tagged full-length STAT3 peptide ( Creative BioMart , #1496H ) was added to 30μl kinase buffer . Kinase assays were performed for 30 min at 30°C , and terminated by the addition of the 2×SDS sample buffer followed by boiling for 5 min . A total of 2μg of nuclear protein extracted from calvarial cells was incubated with biotin-labeled STAT3 binding-site DNA probe in binding buffer ( EMSA kit; Thermo scientific ) for 30 minutes at room temperature . The probe used for the reaction contains the STAT3 binding site of the ΔNp63 promoter with a sequence of 5'-GGATTCCTATTTCCCGTACATAATATGGAT-3' . After incubation , the samples were separated on a 6% polyacrylamide gel in Tris-borate ethylenediaminetetraacetic acid , transferred onto a nylon membrane , and fixed on the membrane by ultraviolet cross-linking . The biotin-labeled probe was detected with streptavidin-horseradish peroxidase ( EMSA kit; Thermo scientific ) . A probe lacking nuclear extracts was used as a negative control . The specificity of the identified STAT3-DNA binding activity was confirmed by using a 200-fold excess of unlabeled probe containing a same sequence . For supershift analysis , 1 μg monoclonal anti-phosphorylated STAT3 ( S727 ) ( Cell Signaling Technology ) was incubated with nuclear extracts for 30 minutes before the addition of the biotin-labeled DNA probe . All results are presented as the mean ± S . D . Curve analysis was determined using Prism ( GraphPad ) . The data in each group were analyzed using unpaired , two-tailed Student’s t-test . The level of significance was set at P < 0 . 05 .
|
The coordinated activities of osteoblasts and osteoclasts in bone deposition and resorption form the internal structure of bone . Disruption of the balance between bone formation and resorption results in loss of bone mass and causes bone diseases such as osteoporosis . Current therapies for osteoporosis are limited to anti-resorptive agents , while bone diseases due to reduced osteoblast activity , such as senile osteoporosis , urgently require targeted treatment and novel strategies to promote bone formation . mTORC1 has emerged as a critical regulator of bone formation and is therefore a potential target in the development of novel bone-promoting therapeutics . Identifying the detailed function of mTORC1 in bone formation and clarifying the underlying mechanisms may uncover useful therapeutic targets . In this study , we reveal the role of mTORC1 in osteoblast formation . mTORC1 stimulated preosteoblast proliferation but prevented their differentiation and attenuated bone formation via activation of the Notch pathway . Pharmaceutical coordination of the pathways and agents in preosteoblasts may be beneficial in bone formation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
mTORC1 Prevents Preosteoblast Differentiation through the Notch Signaling Pathway
|
Lymphatic filariasis ( LF ) , a leading cause of permanent and long-term disability , affects 120 million people globally . Hydrocele , one of the chronic manifestations of LF among 27 million people worldwide , causes economic and psychological burdens on patients and their families . The present study explores and describes the impact of hydrocele on sexual and marital life as well as on marriageability of hydrocele patients from rural areas of Orissa , an eastern state of India . This paper is based on ethnographic data collected through focus group discussions and in-depth interviews with hydrocele patients , wives of hydrocele patients , and other participants from the community . The most worrisome effect of hydrocele for patients and their wives was the inability to have a satisfactory sexual life . The majority of patients ( 94% ) expressed their incapacity during sexual intercourse , and some ( 87% ) reported pain in the scrotum during intercourse . A majority of hydrocele patients' wives ( 94% ) reported dissatisfaction in their sexual life . As a result of sexual dissatisfaction and physical/economic burden , communication has deteriorated between the couples and they are not living happily . This study also highlights the impact on marriageability . The wives of hydrocele patients said that a hydrocele patient is the “last choice” and that girls show reluctance to marry hydrocele patients . In some cases , the patients were persuaded by their wives to remove hydrocele by surgery ( hydrocelectomy ) . The objective of the morbidity management arm of the Global Programme to Eliminate LF should be to increase access to hydrocelectomy , as hydrocelectomy is the recommended intervention . Though the study area is covered by the programme , like in other endemic areas , hydrocelectomy has not been emphasised by the national LF elimination programme . The policy makers and programme managers should be sensitised by utilising this type of research finding .
Lymphatic filariasis ( LF ) , the second leading cause of permanent and long-term disability [1] , affects 120 million people globally [2] . It is a mosquito-borne parasitic disease caused by Wuchereria bancrofti , which accounts for approximately 90% of all LF cases , followed by Brugia malayi and Brugia timoti . India contributes about 40% of the total global burden of LF . In India , a total of 554 million people are at risk of infection , and there are approximately 21 million people with symptomatic LF and 27 million asymptomatic microfilaria carriers [3] . The manifestations of disease are mostly irreversible and a cause of socioeconomic and psychological problems for patients and often their families [4]–[7] . Hydrocele , an accumulation of fluid in the tunica vaginalis in the scrotum that causes it to swell , is one of the chronic manifestations of LF among men ( Figure S1 and Figure S2 ) . There are 26 . 79 million cases of hydrocele worldwide and 48% of these cases are in India [2] . However , little information is available from India , specifically on disability due to hydrocele , except for a few studies on productivity [6]–[9] . The authors undertook one-year round case-control studies to investigate the economic burden , in terms of treatment costs and loss of work , on people affected with chronic and acute forms of LF in rural communities of Orissa [7] , [10] . As part of these studies , epidemiological investigations were also carried out in these communities to identify the cases [11] , [12] . The authors' interaction with people during these studies revealed several problems related to marriage and sex due to hydrocele . In addition , it is a neglected area in disease burden research [13] . Thus , it is hypothesised that hydrocele has an impact on marital life and marriageability of affected individuals . The authors have a strong rapport with the communities in this study , given that these were sites for the socioeconomic studies mentioned above . The present study was undertaken using ethnographic methods to explore and describe the impact of hydrocele on marital and sexual life , and on marriageability .
The present study was conducted in the Khurda district of Orissa , an eastern state of India . This district is known for endemicity of LF caused by W . bancrofti , transmitted by Culex quinquefasciatus [11] . The study area is rural in nature and its inhabitants are mostly small farmers and daily wage labourers . The economic opportunities for daily wage labourers are greater in the monsoon period and sparse during the rest of the year . This paper is based on ethnographic data , for which the protocols were developed during the LF socioeconomic studies mentioned above [7] , [10] . The data for this paper were collected during 2003 . Prior to this , the project protocol was approved by the Scientific Advisory Committee ( SAC ) of the Regional Medical Research Centre , Bhubaneswar . The SAC reviews and approves the research projects for their scientific and ethical merits . A summary of the methodology is presented in Table 1 . Eight focus group discussions were conducted among hydrocele patients and community members separately . Focus groups are widely used to examine people's experience of diseases [14] , [15] . They are useful for studying dominant cultural values , such as narratives about sexuality [16] , [17] , and actively facilitate the discussion of taboo topics . Participants can also provide mutual support in expressing feelings that are common to their group but which they consider to deviate from mainstream culture [16] . Standard guidelines were followed for conducting focus group discussions [16] , [18] , [19] . Due to the sensitivity of the topic , focus group discussions with community members were conducted separately among men and women . The focus group discussions were conducted in Oriya , the language of the state of Orissa . Question guides were used to ensure that the moderator addressed all the issues to be discussed by the group . Separate guides were prepared for patient groups and the general community . Initially , these guides were prepared in English and translated into Oriya , and all translated guides were reviewed for linguistic reliability and correctness . Later , these guides were piloted with groups similar to the participants' groups , but from villages that were not included in the study , to check appropriateness , clarity , and flow of questions . The duration of focus group discussions varied between 60 and 90 minutes . To elicit the views and experiences on the impact of hydrocele , in-depth interviews were conducted with hydrocele patients , wives of hydrocele patients , and key-informants in the community . In-depth interviewing offers the respondents the opportunity to express their own ideas and address themes that the researchers may not have anticipated [20] . Key-informants are individuals in the community who possess special knowledge and who are willing to share their knowledge with the researchers . They have access to the culture under study in a way that the researcher does not [18] . The procedures for selecting key-informants and conducting in-depth interviews were based on standard guidelines [18] , [21] . All of the interviews were held in Oriya and the duration of these interviews ranged from 30 to 75 minutes . Separate interview guides were made for the three categories of participants . These guides were prepared and finalised in the same manner as the focus group guides . Participants for focus group discussions and one-to-one in-depth interviews were selected by purposive sampling from 12 villages of Khurda district , Orissa . In purposive sampling , researchers choose study sites or informants to represent the range of variation on those characteristics that seem to be meaningful for the topic under study . In this situation , a small number of specially chosen informants can yield valid and generalisable information [18] . Participants were selected from all of the villages to the extent possible . During epidemiological investigations [11] , a door-to-door survey was conducted and all individuals in these villages were physically examined for different signs and symptoms of LF . A cohort of 115 patients exclusively with hydrocele was available . These patients were identified for in-depth interviews and focus group discussions , and only those who were married and below 50 years of age were selected . Hydrocele patients who possessed other LF symptoms like lymphoedema were not included . To conduct a focus group discussion with hydrocele patients , six to eight patients from two to three villages were consulted and requested to arrive to a particular place at a particular time . These villages are closely situated within a distance of 2–3 kilometres . Further , they share several common places like agricultural fields , markets , a health centre , etc . Hence , people of these villages were known to each other . Two focus groups consisted of eight patients each and a third group consisted of six patients . The hydrocele patients who were chosen for in-depth interviews were not included in focus groups . The selection of women whose husbands suffer from hydrocele was also made by identifying hydrocele patients from the cohort . These patients were not covered for in-depth interviews and focus group discussions . Key-informants were also selected from all of the villages , based on the guidelines mentioned above . The key-informants were village/community heads , members of local administrative bodies , and teachers residing in these villages . All of these informants were educated , having at least a school education , and were up to the age of 70 years . For focus groups with community members , participants were selected from within a village . Two groups of women ( consisting of six and eight members ) and three groups of men ( consisting of seven , eight , and eight members ) in the age range of 20 to 50 years were selected . Women participants were housewives and the men were farmers and agricultural labourers . Some of the participants did not have any formal education , while others had received formal educations of up to 5 years . During the recruitment of focus group participants , it was ensured that all participants knew each other , and care was taken to maintain homogeneity within each group . Participants having similar socioeconomic characteristics ( like education , income , occupation , and caste affiliation ) were grouped together . The purpose of the study was explained and consent to participate in the study was obtained orally from all of the participants , as suggested by the SAC . Oral consent was obtained instead of written consent , as the majority of the study participants were illiterate . The consent of the participants was recorded along with the recording of the interview/discussion . In addition to obtaining consent from each participant , the consent of the community leaders and village heads was obtained to conduct the surveys in their villages and communities . None of the participants declined to particpate in the study . The researchers and staff who assisted during the conducting of focus groups ( SM and ANN , and those listed in Acknowledgments ) are aware of local language and culture . The discussion/interview guides were prepared initially in English to ensure that all the issues and objectives of the study were covered comprehensively . The focus group moderators were trained medical anthropologists who were experienced with conducting focus group discussions on LF-related issues . The moderator conducted the focus group discussions , while observers watched and took notes on the discussion , as well as on participants' actions , gestures , and emotions that could not be captured on audiotape . The observers transcribed the focus group discussions . The in-depth interviews were conducted by the same researchers who acted as moderators of the focus group discussions . Focus groups as well as interviews with female and male participants were conducted by female and male staff , respectively . After each discussion/interview , the team along with the primary researcher discussed what had transpired and reviewed the observers' field notes on impressions and observations . The entire discussion/interview was recorded on audiocassettes . Later , the audiocassettes were played back and transcribed into Oriya with the help of field notes . The Oriya scripts were translated to English and were entered into a personal computer in MS Word as text files [22] . The analysis was done by using ATLAS . ti for Windows 4 . 1 ( Scientific Software Development , Berlin ) . This computer-based analysis facilitated selecting relevant quotations from the text , coding , annotating , and comparing the quotations . The coding plan was developed based on the issues related to the effect of hydrocele on conjugal life and marriageability . The quotations of each code were retrieved for each category of participants by using a text-based selection option . Thus , by summarising the quotations of each code by category , a matrix was developed to examine the qualitative data .
Data revealed that hydrocele is always a burden to the patient and to his family . In addition , the most worrisome effect of hydrocele for patients and their spouses was the inability to have a satisfactory sexual life . Of the 32 patients interviewed , 30 patients ( 93 . 7% ) expressed their frustration due to their incapacity during sexual intercourse . All respondents interviewed except four ( 87 . 5% ) reported that they get severe pain during intercourse and hence they avoid sex . They also perceived the dissatisfaction of their spouse due to their incapability during sexual intercourse . A young hydrocele patient during an interview said , “The hydrocele has affected my sexual life . I could not do much … . My wife is now not interested to have sex with me . Seems that she hates me… and advises me to go for hydrocele surgery . ” Another patient narrated , “My spouse has no complaint , rather she is very cooperative in this matter . However , my sexual desire has declined drastically over the years . I develop an acute pain in the scrotum during and immediately after the intercourse . ” However , in focus group discussions with hydrocele patients , a few participants argued that it has no gross impact on sexual life , though they do experience some pain . However , the groups reached consensus that their condition has influenced their ability to have sexual intercourse . Of the 35 women whose husbands are hydrocele patients , 33 women ( 94% ) reported dissatisfaction with their sexual life . Some women ( 25 . 7% ) complained that a failure of erection and penetration occurred during intercourse due to the larger size of the hydrocele scrotum . About half of them said that hydrocele patients have lesser sexual potency than normal men and they thought that hydrocele is responsible for male impotency . Many of these women ( 68 . 6% ) also reported that their husbands became hesitant to have sex and subsequently lost interest in it . In the voice of the wife of a hydrocele patient , “When the disease becomes prolonged , the front portion of male genital becomes very small and the scrotum gets unusually large . Eventually that leads to male impotency . ” However , about 6% of these women reported that they did not perceive any problem . This reporting might be due to the following: ( i ) some men are in an early stage of hydrocele and might not be affected much , and/or ( ii ) these women may not choose to reveal this private information . A few women could not speak openly , as the issue is sensitive and related to their family . A 28-year-old woman , whose husband is a hydrocele patient , said , “Yes , there is problem . How there shall be no problem ? There is a major problem at the time of intercourse . But I do not like to reveal it to everybody . For instance , when we are served food by our guests , we normally accept it irrespective of whether or not we like that food . For certain things , I cannot tell to my husband , he may feel bad about me . Though we are not happy on bed , I manage it without showing sense of dissatisfaction . ” This study found that the wives of many hydrocele patients persuaded their husbands to get a hydrocelectomy . In one case , it was found that the wife of a hydrocele patient was no longer interested in sleeping with her husband . In another case , as narrated by a woman respondent , a woman had left her husband and gone back to her parents' house out of frustration . During one interview , a woman reported , “A newly married girl of our village recently left her husband and went back to her parents' house due to this problem . We stay with our husbands because we are old and have children . The younger generation girls are not that much adjustable . ” The general community also reported similar views about the effect of hydrocele on the sexual life of patients . Of the nine key-informants in the community , eight informants ( 89% ) revealed that the hydrocele patients feel pain during sexual intercourse . They also said that , as the penis of hydrocele patients get shorter , they fail to satisfy their wives during sexual intercourse . These respondents became aware of these issues through casual discussions and gossiping with the affected people and other community members . Hence , these respondents discussed these issues in a generalised manner without referring to a particular person . Concerning marital life , the hydrocele patients revealed several issues . During in-depth interviews , half of the 32 patients agreed that they and their wives are not as happy as they were before the development of hydrocele . When they were asked whether or not both of them live just like others , about 40% of patients said that they are not like others , as their condition hampered their economic situation , because of loss of work capacity , and led to dissatisfaction in their sexual life . About one-third of patients said that their spouses were hesitant and did not like to go out with them . Approximately one-fifth of patients revealed that their wives expressed dislike towards them . The focus groups of patients also revealed that communication had deteriorated between the couples and the quality of the marriage had been affected . About half of the women ( 17/35 ) whose husbands have hydrocele told the interviewers that their husbands' activities were affected adversely due to hydrocele . These respondents reported that patients get severe pain in their genitals ( scrotum ) and feel too weak to perform hard work . Approximately 60% of women felt sorry for the condition of their husbands . About one-third of the respondents reported that the disease has affected the economic condition of their family . Some of these respondents ( 11 . 4% ) reported that their husbands drink alcohol to get rid of the pain and other problems associated with hydrocele . A few women said that they frequently quarrel because of the disease and their relations are deteriorating . The following statement of a 40-year-old woman reveals the agony of such women: “My husband has a big hydrocele . He is not able to move and work freely . He feels ashamed to make his appearance in public places . He is now not even fit for conjugal life . ” The community members also perceived the problem of hydrocele and revealed that the disease is affecting people physically and psychologically . They mentioned several instances in which the deterioration of relations between wife and husband and disturbances in conjugal life had occurred . The hydrocele patients also revealed the impact of hydrocele on marriageability . They said that one should remove it by surgery before going for marriage proposals . All patients agreed that it is difficult to get a bride for a hydrocele patient . Some patients in focus groups said that some men remained unmarried due to their condition , but this was denied by a few patients . However , all focus groups of patients agreed that it is problem for young men to get married , as the patient cannot work or even walk properly . In the present study , the hydrocele patients anticipated problems in getting their children married due to their disease . This disease is considered hereditary and people think that the diseases get transmitted to the next generation . When their sons suffer from hydrocele , it then becomes increasingly difficult for the parents to obtain spouses for their sons . The women whose husbands have hydrocele said that the hydrocele patient is the “last choice” and that some girls are reluctant to marry hydrocele patients . A woman who is the wife of a hydrocele patient reported , “Had I know about the hydrocele of my husband , I could have refused to marry him . ” About half of the women whose husbands were suffering from hydrocele said that their husbands had this disease before marriage . Approximately two-thirds of them alleged that the husbands' families had not disclosed their husbands' disease prior to the marriage , and these women expressed the feeling of having been deceived . Some women also expressed their inability to choose a bridegroom due to their economic situation and family customs . The women were asked a hypothetical question querying whether they think getting a spouse for a hydrocele patient is difficult . More than half of these participants said that it is difficult to get spouse for a hydrocele patient . Specifically , if a girl knows beforehand about the disease of the bridegroom , she may not accept that proposal . However , some women respondents ( 34% ) opined that it is not a problem , as hydrocele can be removed by surgery . The community members also felt similarly and said that people do not prefer to give their daughters to a hydrocele patient . Therefore , the hydrocele patients marry with little or no dowry under a compulsive situation . Often they marry girls from lower socioeconomic strata . One person explained , “It is difficult for a filariasis patient to get a girl by choice for marriage . Normally he gets married to a girl of lower economic status . … Parents offer their daughter to such a patient only under compulsive situations . ”
In LF-endemic areas , hydrocele develops from asymptomatic infection through acute clinical manifestations . There are about 73 million people with LF infection worldwide and men in this group are at the risk of developing hydrocele , in addition to 27 million existing hydrocele patients [2] . The peak incidence of noticeable hydrocele seems to occur in early adulthood , between the age of 19–34 years [2] ( Figure S1 ) . It is demonstrated through several studies that hydrocele has an immense impact on economic activities and productivity [6] , [7] , [9] , [23] , [24] and quality of life [25] , [26] . Addiss and Brady [27] reported that hydrocele patients reported both “enacted stigma” and “felt stigma” , based on studies in some endemic areas [28] , [29] . Some patients often described themselves as frustrated , losing hope , and even suicidal [5] , [30] , [31] . The present study highlights the impact of hydrocele on conjugal life and relations between hydrocele patients and their wives . In addition , the paper describes how hydrocele impacts the marriageability of patients and their sons . Hydrocele affects men during the prime age when they pursue social and family goals . Women married to hydrocele patients were “silent sufferers” of their husbands' disease . The dissatisfaction of the patient and his wife leads to the deterioration of the marital relationship . However , in a society like rural India , the institution of marriage is strong and women usually do not separate for reasons like sexual dissatisfaction . The family and society do not appreciate and support such women , in addition to the existence of a strong community sanction against divorce and even temporary separation . However , an incidence of temporary separation due to this problem was reported by a woman participant of this study . Similar findings were reported from other endemic areas . Dreyer and her colleagues found problems among clinic-based patients from Brazil such as marriages devoid of physical and sexual intimacy , a “conspiracy of silence” that includes both the patient and his wife , and profound shame and suicidal thoughts among men with hydrocele [5] . In Ghana , unmarried men found it difficult to find a spouse of their choice , and various degrees of sexual dysfunction were reported amongst married men [30] . This study associated sexual dysfunction with the size of the hydrocele . However , in the present study , no such attempt to find an association with the size of hydrocele was made due to lack of sufficient data . A similar study from another endemic area of Ghana reported the inability of hydrocele patients to have satisfactory sexual intercourse [28] . In addition , hydrocele prevented patients from getting a marriage partner , and there were a few cases of divorce due to hydrocele [28] . Another study , based on the extended Euro quality of life scale among South Indian hydrocele patients , reported that hydrocele adversely affected the patients' sexual functioning and caused moderate problems with anxiety/depression [32] . Though these studies reported sexual dysfunction and its effect on married life , the strength of the present study is that it could capture the feelings of the wives of hydrocele patients . Many wives persuaded their husbands to remove hydrocele by surgery . Women in endemic areas may have an important role in advocating for better access to hydrocelectomy , at least on a household level . However , it is felt that these issues , specifically the feelings of the women , should be understood with gender perspectives . There are methodological limitations in this study , as is usual with this type of research design and methods . The topics of discussion and interviews are sensitive , and participants may not express their views openly , as they think that their responses may damage their reputation or their family . Sometimes , in this type of research , participants may also report the behaviour that is believed to be consistent with their culture , rather than the actual behaviour [33] . In focus groups , some participants were inactive and did not reveal much in the discussion , while some others were active . Though that is a limitation , it has been managed by the trained moderators . It is clear that men with hydrocele need psychological and social support , as opined by Dreyer and her colleagues [5] . As there is a strong feeling of shame and embarrassment among hydrocele patients , the problem of sexual disability is usually not acknowledged unless the patients are specifically probed by health care staff . The desire of some of the hydrocele patients as well as their wives was to have surgery to remove the hydrocele ( hydrocelectomy ) . In addition , a majority of these people were aware of the remedy of hydrocele through the surgery [34] . However , most hydrocele patients have not had a hydrocelectomy due to the costs involved , loss of working days/wages during hospitalisation and recuperation after surgery , and lack of a surgical facility in rural public health institutions . The surgical facilities for hydrocelectomy are available in private hospitals in urban areas , and these hospitals charge more than US$100 for surgery and a bed . In addition , the patient has to bear the other expenditures like medicines , food , travel , etc . This expenditure , along with the loss of work and wage of the patient as well as the escort , prevents patients from accessing surgery for the cure of hydrocele . This study area is covered by the Global Programme to Eliminate LF . The alleviation of disability and control of morbidity among LF patients is the second arm of the programme [35] . This arm has not received much attention and therefore lags behind the first arm of the programme , i . e . , interruption of LF infection through mass drug administration ( MDA ) . It is evident by the fact that 48 of the 83 endemic countries had implemented MDA by the end of 2007 , whereas only 27 of the 48 countries that implemented MDA have initiated morbidity management activities [36] . Even in those areas where morbidity management activities are conducted , more emphasis is given to the management of lymphedema , rather than to the repair of hydrocele . This could be due to several reasons , including lack of resources at health institutions of an implementation level and lack of adequate information on the burden and impact of hydrocele . The policy makers and programme managers should be sensitised by using research findings on the burden and impact of hydrocele . The objective of any LF morbidity management programme should be to increase access to hydrocelectomy . One of the initial activities of the programme should be to detect hydrocele cases using existing community-based surveys , such as enumeration during MDA . In addition , a house-to-house morbidity census may be conducted to acquire a better understanding of hydrocele burden . Individuals with hydrocele should be referred to a facility for surgery , if necessary . Mass hydrocelectomy camps may be feasible initially to reduce the burden of hydrocele in high LF-endemic areas . In a study from Ghana , patients reported that within 3 to 6 months of the post-surgery period , they had experienced a significant improvement in self-esteem , sexual function , and work capacity , and they participated more in community activities [28] . Because MDA may be used as a primary prevention measure for disabilities caused by LF , opportunities for synergy between MDA and disability management and prevention activities need to be explored [37] . Endemic countries have become convinced of the benefits of the programme and real progress in arresting transmission has been reported from countries that commenced MDA early [38] . Also , an unpredicted outcome of MDA , i . e . , reduction of incidence of hydrocele following MDA , has been reported from Papua New Guinea [39] and India [40] . However , a common finding was that even after an aggressive control programme to arrest the transmission of infection , chronic manifestations such as hydrocele have persisted for decades [41] . Hence , the programme's two arms should go hand in hand . There is a need to incorporate LF disability management and prevention into primary health care services , which are well established in many areas . Also , these activities may be integrated with health interventions of other neglected tropical diseases . The integration among these programmes helps improve both efficiency and effectiveness [42] . Recently , there has been significant discussion on potential challenges , opportunities , and estimated potential benefits including cost savings [42] . The momentum of the Global Programme must be sustained to remove the impediments that prevent hydrocele patients from leading a decent life and to stop generation of new hydrocele patients .
|
Lymphatic filariasis , the second leading cause of permanent and long-term disability , affects 120 million people globally . Hydrocele , an accumulation of fluid in the scrotum that causes it to swell , is one of the chronic manifestations of LF among men and there are about 27 million men with hydrocele worldwide . We conducted ethnographic interviews and discussions with patients , women whose husbands have hydrocele , and the general public in a rural community of eastern India . The study describes how hydrocele impacts patients' sexual and marital life . It reveals the most worrisome effect of hydrocele for patients and their wives due to the inability to have a satisfactory sexual life . Patients expressed their incapacity during sexual intercourse . A majority of hydrocele patients' wives reported that their married life became burdened and couples were not living happily . This study also highlights the impact on marriageability , and some women expressed that a hydrocele patient is the “last choice” . In some cases , the patients were persuaded by their wives to remove hydrocele by surgery ( hydrocelectomy ) . Hence , access to hydrocelectomy has to be strengthened under the Global Programme to Eliminate Lymphatic Filariasis , which is operational in several endemic areas in the world . Also , this activity may be integrated with primary healthcare services and interventions of other neglected tropical diseases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] |
2009
|
Marriage, Sex, and Hydrocele: An Ethnographic Study on the Effect of Filarial Hydrocele on Conjugal Life and Marriageability from Orissa, India
|
The average individual is expected to harbor thousands of variants within non-coding genomic regions involved in gene regulation . However , it is currently not possible to interpret reliably the functional consequences of genetic variation within any given transcription factor recognition sequence . To address this , we comprehensively analyzed heritable genome-wide binding patterns of a major sequence-specific regulator ( CTCF ) in relation to genetic variability in binding site sequences across a multi-generational pedigree . We localized and quantified CTCF occupancy by ChIP-seq in 12 related and unrelated individuals spanning three generations , followed by comprehensive targeted resequencing of the entire CTCF–binding landscape across all individuals . We identified hundreds of variants with reproducible quantitative effects on CTCF occupancy ( both positive and negative ) . While these effects paralleled protein–DNA recognition energetics when averaged , they were extensively buffered by striking local context dependencies . In the significant majority of cases buffering was complete , resulting in silent variants spanning every position within the DNA recognition interface irrespective of level of binding energy or evolutionary constraint . The prevalence of complex partial or complete buffering effects severely constrained the ability to predict reliably the impact of variation within any given binding site instance . Surprisingly , 40% of variants that increased CTCF occupancy occurred at positions of human–chimp divergence , challenging the expectation that the vast majority of functional regulatory variants should be deleterious . Our results suggest that , even in the presence of “perfect” genetic information afforded by resequencing and parallel studies in multiple related individuals , genomic site-specific prediction of the consequences of individual variation in regulatory DNA will require systematic coupling with empirical functional genomic measurements .
A growing number of studies associate variation within regulatory DNA and risk of human disease [1]–[3] . Variation in regulatory DNA may result in modulation of recognition by sequence-specific transcription factors ( TFs ) , resulting in altered gene expression [4]–[6] . That the vast majority of variants emerging from human resequencing studies lie in non-coding regions creates an urgent need for determining the consequences of variation within regulatory DNA . Functionally significant variation within the genomic recognition sequences for certain TFs appears to be correlated in aggregate with nucleotide-level evolutionary conservation and/or position-specific information content [7]–[10] . Although surveys have identified sites of allele-specific occupancy of TFs and RNA Polymerase II or allele-specific chromatin states [11]–[15] , these studies have not established the distinguishing characteristics of regulatory sequence variation with an experimentally-observed effect on occupancy . As such , it is currently not possible to interpret reliably the functional consequences of variation within any given TF recognition sequence . To address this , we apply a novel experimental design to identify comprehensively patterns of genetic variation with heritable effects on the occupancy of the major genomic regulator CTCF [16] . Unlike most sequence-specific regulators which rely on cooperative interactions with other factors to bind DNA , CTCF is able to access target DNA within chromatin in a relatively autonomous fashion through its rich binding interface . By combining quantitative genome-wide occupancy analysis by ChIP-seq in a multi-generational pedigree with comprehensive resequencing of the binding site landscape across all individuals , we achieve complete knowledge of variation in both sequence and occupancy , thus creating a benchmark for assessing the characteristics of functional and heritable regulatory sequence variation .
We mapped binding sites for CTCF by ChIP-seq in B-lymphoblastoid cells derived from 12 members of a three-generation pedigree ( Figure 1A , 1B ) . We identified a total of 51 , 686 binding sites across all individuals at a false discovery rate ( FDR ) of 1% . To comprehensively identify genetic variation with potential functional consequences for CTCF binding , we performed targeted resequencing by array capture focused on the 134 bp interval surrounding 46 , 568 CTCF sites ( total ∼6 Mbp ) in all family members assayed by ChIP-seq . 7 , 394 of the 35 , 709 surveyed binding sites ( or 21% ) overlapped one or more SNPs , some of which had clear associations with occupancy in the direction predicted by the CTCF motif ( Figure 1C , 1D ) . We did not consider other variation such as copy number variants or small indels . In order to minimize reference mapping bias for the ChIP-seq data , we remapped tags to personalized genomes including discovered SNPs [17] . Additionally , to avoid artifacts resulting from uncertain mapping of 36 bp reads to the genome , we simulated all reads including discovered SNPs from ±147 bp centered on the ChIP-seq peak and excluded sites with too many ambiguously mapped tags . We integrated the genetic and functional data sets to survey genome-wide heritable variation in transcription factor occupancy . We reasoned that the strongest signal of heritable variation would be from segregating variants overlapping the binding site . Thus we performed a linear regression of the ChIP-seq density on the SNP genotype in cis ( Figure 1D ) . Of 5 , 828 polymorphic sites , this analysis identified 325 ( 5 . 6% ) sites with a significant association of SNP genotype with occupancy at a false discovery rate ( FDR ) of 1% ( Figure 2 , Table S4 ) . We tested whether several confounding factors might be responsible for our results , however sites at which SNPs were significantly associated with changes in occupancy were similar to polymorphic sites without changes in occupancy in terms of GC content ( median 53 . 7% vs . 53 . 0% , Mann-Whitney p<0 . 044 ) and ChIP-seq input signal ( 3 . 61 vs . 3 . 61 , Mann-Whitney p<0 . 84 ) , and distance to the nearest RefSeq TSS ( 33 kb vs . 36 kb , p<0 . 96 ) . Significant sites were only slightly weaker in terms of ChIP-seq density ( 2 . 73 vs . 2 . 93 , p<2 . 7*10−3 ) , and DNase I signal ( 5 . 47 vs . 6 . 92 , p<7 . 8*10−8 ) . Thus we conclude that the SNP genotype is associated with differences in occupancy at 325 of 5 , 828 sites tested . We used a hypothesis-driven linkage analysis to assess the heritability of the remaining unexplained differential occupancy . First , we identified 1 , 376 sites of differential occupancy . Of these , 200 ( 15% ) were already associated to an underlying SNP ( FDR 1% ) , 65 ( 4 . 7% ) had allele-specific occupancy ( FDR 0 . 1% ) , and 197 ( 14% ) were on chromosome X . To test for heritable inheritance of occupancy not explained by these factors , we performed Haseman-Elston sib-pair linkage analysis in aggregate at sites differentially occupied among the 6 grandchildren ( Figure S1A and S1B; see Materials and Methods ) . The 47 binding sites already significantly associated with SNPs had a regression slope of −2 . 86 ( Figure S1C ) , confirming substantial heritability ( p<8 . 9*10−7 , permutation ) . The remaining 50 sites without significant associations had a regression slope of −1 . 01 ( Figure S1D ) , indicating a lower but still significant level of heritable variation ( p<3 . 2*10−5 ) . These results suggest that SNPs directly overlapping the cognate recognition sequence explain most but not all of the heritable variation in this pedigree . Remaining variation in occupancy might be heritable due to sequence variants not considered in our SNP-based analysis or heritable epigenetic variation such as methylation . However , it is quantitatively less significant than the heritability attributable to the direct effect of SNPs on occupancy ( Figure S1E ) . Understanding the effect of DNA sequence variation on transcription factor occupancy is critical to a mechanistic interpretation of non-coding variation . To interpret the association results in the context of the CTCF motif , we scanned the center of the ChIP-seq peak with the known position weight matrix ( PWM ) [18] , which measures the contribution of each nucleotide in the binding site to the energy of the protein-DNA interaction [19] , [20] . 888 binding sites did not contain a motif match ( fimo p-value<10−2 ) and 1 , 040 binding sites overlapped multiple SNPs within ±180 bp of the ChIP-seq peak . Excluding these sites , we analyzed the 4 , 428 binding sites with a single SNP and a single motif match . These SNPs were distributed throughout the resequenced region surrounding each CTCF motif ( Figure 3A ) . In contrast , we expected that SNPs associated with occupancy differences would be concentrated in the 44 bp region of protein-DNA contact [21] . Indeed , despite a slight reduction ( 1 . 08-fold ) in local sequence diversity , 85% of the SNPs that affected occupancy were within this region ( Figure 3B ) . The allele observed to have higher occupancy matched the energetically more favorable one for 83% of these SNPs . Associated SNPs outside the region of contact had less significant q-values ( median q-value of 1 . 3*10−3 outside the versus 1 . 3*10−5 inside ) , consistent with these SNPs being false positives or sites with ambiguity in the true location ( s ) of protein-DNA interaction . Alternatively , some of these SNPs might affect CTCF occupancy indirectly by perturbing an adjacent co-factor binding site . We compared our results to an allele-specific occupancy test performed at heterozygous sites ( Figure S2 ) . Despite a weaker enrichment of significant sites within the core motif and a less substantial concordance of the higher occupancy allele with the energetically more favorable nucleotide , this allele-specific analysis broadly corresponded to the results of the association analysis . Thus , we interpret the results of our analysis as indicating that we have correctly identified the motifs at most binding sites , and that the significant SNPs directly affect occupancy through modulating the protein-DNA interaction at these sites . Although differences in occupancy were largely associated with SNPs at positions strongly affecting overall binding energy ( Figure 3B ) , only 13% of SNPs at the interface of protein-DNA interaction affected occupancy ( Table S4 ) . Thus although functional SNPs are highly concentrated in the region of protein-DNA contact , the majority of SNPs , even in this region , do not measurably affect occupancy . Since our data set includes multiple sites with the same two alleles at the same position measured relative to the binding motif , we investigated the proportion of sites at which a given change was found to affect occupancy ( Figure 4A ) . Like in Figure 3 , the most disruptive changes were observed at positions of high information content in the motif . However , even over the 14 bp core motif , changes affected occupancy at a median of only 36% of the sites where they were observed . We found no changes that uniformly affected occupancy without regard to context . Instead , we observed a strong , progressive depletion in the proportion of changes affecting occupancy at the strongest sites ( Figure 4B ) , and a smaller depletion at the weakest sites . Indeed , simply clustering the ChIP-seq intensities identified three major groups , of low , medium and high occupancy , which were also distinguished by varying proportions of significant SNPs ( Figure S6 ) . This result places an upper limit on the accuracy of methods that predict the effects of non-coding SNPs without consideration of their context . Strength-dependent buffering could be explained by a model where changes in occupancy are observed only when a SNP causes the affinity of a site to cross a threshold for binding . In this case , the strongest and weakest sites will only be affected by the greatest genetic perturbation , while smaller perturbations would affect binding only at sites of intermediate strength . This would create the impression of epistasis between all positions in the cognate recognition site as any affinity-affecting change could potentially buffer another [22] , [23] . We thus compared the inherent affinity of the site with the magnitude of the perturbation caused by each SNP . We divided SNPs affecting occupancy into bins based on the strength of their match to the canonical motif . We found that SNPs at sites matching the CTCF motif more strongly in turn exhibited higher log-odds differences ( Figure 4C ) . We observed no such trend at SNPs not associated with occupancy differences ( Figure S3 ) . These results are consistent with stronger motifs being buffered against all but the largest perturbations . Although a linear regression identifies a significant effect ( p<0 . 006 ) , an r2 of 0 . 04 indicates that the strength of the motif match alone can not explain the breadth of buffering observed . Buffering might also be a consequence of the non-additive effect on binding energy of individual positions in the cognate binding site , as has been observed in vitro [24] . The relevance of non-additive interactions for identifying binding sites has been questioned [25] , [26] , but the implications for understanding the function of specific variants in vivo have remained unclear . To explore the power of our data set to discover epistatic interactions , we measured the mutual information between the sequence context per-base in the core motif and whether a SNP at each location affects occupancy ( Figure 4D ) . This analysis identifies two positions in the consensus sequence that significantly buffer the effect of a SNP at another position . First , of the 24 SNPs observed at position 1 in the motif , 13/13 that affected occupancy had an adenine at position 5 , compared to only 5/11 for those that did not affect occupancy ( Figure 4E , above ) . Interestingly , the second significant buffering interaction is between position 7 and SNPs at the adjacent position 8 ( Figure 4E , below ) , suggesting local compensation for the adjacent SNP . These results indicate that higher-order models may be necessary to fully model the effect of polymorphism on protein-DNA interaction , and are consistent with a model where local factors determine whether polymorphism affects occupancy . Resequencing and association studies are producing large amounts of data on polymorphism in non-coding regions , yet as we have illustrated , their functional classification is difficult . To investigate the power of existing metrics to predict functional polymorphism in non-coding regions , we used as a reference set the 1 , 368 sites with SNPs within the 44 bp vicinity of a recognizable CTCF motif . We first assessed the predictive power of evolutionary constraint , which has been used successfully to discover regulatory motifs [27] , to highlight functional positions within motifs [9] , [10] , [28] , and to predict the effect of coding variants [29]–[31] . Conservation is a particularly attractive operational metric in genome scans , as it can be applied in an unbiased fashion without directly measuring protein-DNA interaction or modeling context effects . Indeed , CTCF binding sites are clearly marked by increased conservation [32] . Thus , we tested the sensitivity and specificity of per-nucleotide conservation ( phyloP 44-way vertebrate alignment , from UCSC browser ) to correctly identify the 186 significant SNPs in our reference set . However , despite being applied only across experimentally determined binding sites , conservation had little predictive power on this data set , with an AUC of 0 . 57 ( Figure 5 ) . Then we measured the improvement from evaluating potentially functional SNPs within the context of protein-DNA binding energetics . Applying such predictor showed a marked improvement over conservation , with an AUC of 0 . 75 ( Figure 5 ) , although positive predictive power was greatest for the most severe perturbations ( Figure S4 ) . Nevertheless , these results illustrate the power to be gained from considering non-coding polymorphism within the context of functional genomics data on transcription factor occupancy .
In contrast to the vast diversity of protein function , the elements that regulate gene expression recruit from a shared repertoire of transcription factors , offering the potential for a common regulatory sequence code . The torrent of variants emerging from human resequencing studies – the vast majority of which lie in non-coding regions – coupled with the growing number of common , disease-associated non-coding variants [1]–[3] has created an urgent need for determining the consequences of variation within regulatory DNA . However , the proportion of variants within regulatory DNA that have reproducible functional consequences on regulatory factor binding is currently unknown , and our ability to predict such outcomes from known rules of protein-DNA interaction is uncertain . We have described a novel , hypothesis-driven genetic method employing targeted capture and genome-wide in vivo occupancy profiling to investigate directly the consequences of heritable variation in regulatory sequence . Our results show that individual transcription factor binding sites are surprisingly robust to genetic variation , even at evolutionarily constrained positions . While previous studies have observed differences in transcription factor occupancy among individuals using occupancy profiling alone [15] , genome-wide linkage scans [33] , or allele-specific occupancy approaches [11] , this work is the first systematic analysis of patterns of functional alteration in TF recognition sequences . This study further advances the characterization of heritable variation in TF binding by using highly accurate sequence information throughout a three-generation pedigree . Our study has revealed a large degree of context dependence for changes to the CTCF recognition sequence . Indeed , even over the core 14 bp motif , only 36% of SNPs affected occupancy ( Figure 4A ) . Our estimate of the percentage of SNPs that affect occupancy in this 14 bp region ranges between 24% to 42% at FDRs 0 . 1% and 5% , respectively , indicating that the magnitude of this effect cannot be explained by the choice of significance cutoff . We have suggested that buffering is partly mediated by the strength of the binding site , as well as the sequence context at the local CTCF recognition sequence . In addition , buffering might be facilitated by a feedback process that maintains a constant CTCF occupancy despite alterations to the site's inherent affinity . However , while 21% of the SNPs in the region of protein-DNA contact that were significant in our association analysis also exhibited allele-specific occupancy in heterozygous samples , only 3 . 7% of the non-significant SNPs did , indicating that buffering is not likely to be the consequence of a feedback process . Alterations in DNA methylation might also mask the effect of otherwise significant genetic changes . However , only 30% of polymorphic CTCF sites contain a CpG at positions 1 or 11 of their recognition sequences . Furthermore , the prevalence of CpGs at these positions is the same at sites where a SNP does and does not affect occupancy , limiting the potential scope of methylation to fully explain the observed buffering . As this study was performed on transformed B-lymphoblastoid cells , it is worth noting that the specific CTCF sites that are buffered may not be extrapolated to primary cell types . However , assuming that EBV transformation does not invoke novel cellular mechanisms to regulate protein-DNA interaction , our primary conclusion stands that TF occupancy is strongly modulated by site-dependent effects . Our results establish a low level of mutational load directly affecting transcription factor occupancy in the 4 founder genomes . Although variants were found at 21% of surveyed CTCF sites , only 0 . 9% of binding sites exhibited a difference in occupancy due to polymorphism ( Figure 2 ) . Previous studies have identified varying levels of positive and negative selection in transcription factor recognition sequences by estimating changes in binding energy [34]–[36] . However , our results indicate that 87% of polymorphism observed in the region of protein-DNA contact does not affect binding ( Table S4 ) . This is a higher proportion of silent variation than predicted by binding energy models ( Figure S4 ) , providing evidence that the scope of sequence change consistent with neutral evolution may be larger than previously thought . Interestingly , we observed that the allele with higher occupancy was the derived allele in 40% of the cases ( assuming the chimpanzee allele is ancestral ) . This indicates that approximately 40% of the functional substitutions in the human lineage increased occupancy , which is surprisingly high given that most mutations might be expected to reduce binding energy . Previous work studying the power of comparative genomics has predicted a steep increase in the number of sequenced genomes required to obtain nucleotide resolution , particularly in the absence of perfect conservation [37] . While genome-wide phylogenetic footprinting approaches have highlighted substantial conservation of transcription factor sequence specificities [27] , [32] , [38] , [39] , functional studies of diverged species have uncovered low conservation in occupancy at orthologous sites [40]–[42] . Any phylogenetic approach is thus a compromise between statistical power gained by sampling more diverged species and the ability to recognize similar functional elements by sequence similarity . The optimum evolutionary distances to sample may be different for assessing functional non-coding elements than for more conserved coding sequence [43] , [44] . This tradeoff suggests a potential motivation for broad resequencing of natural populations , though even this approach faces the fundamental limitation of ineffective purifying selection in primates and humans [45] . Gene-based studies have successfully identified causal variants using current methods for prediction of functional non-synonymous protein variants [46] . Coding mutations in the CTCF gene affecting its DNA binding specificity have been identified in cancer samples [47] , but lesions in its binding sites are harder to interpret . The link between cognate recognition sequence and cellular consequence is complicated by potential influences from the cell-type specific chromatin landscape [48] , maintenance of regulatory function despite sequence rearrangement [49] , altered association with protein complexes [50] , [51] , and the lack of binding specificity and occupancy data for common transcription factors . In spite of these caveats , our results indicate that a motif-based classifier of variation in experimentally-identified CTCF binding sites predicts functional variation with a 59% true positive rate and a false positive rate of 20% ( Figure 5 ) . In comparison , current methods for prediction of non-synonymous protein variants achieve a 73% to 92% true positive rate at the same false positive rate [30] – not dramatically greater considering the comparatively greater depth of variant databases used to assess coding variation . Given the encouraging performance of a straightforward functional genomics approach , that the majority of variants presently associated with human physiology and pathology lie in non-coding regions [3] should be grounds for optimism . In summary , our results indicate the existence of widespread recognition site-dependent buffering of polymorphism within regulatory DNA regions . A major implication of our work is that the potential for accurately predicting the consequences of variation affecting regulatory factor recognition sequences is severely limited by complex context dependencies , necessitating empirical assessment using functional genomic approaches . The feasibility of approaches such as the one we describe here has recently dramatically increased owing to coupled advances in next-generation sequencing technology and molecular biology , and continuation of this trend should in the near future enable further systematic investigations into the effect of polymorphism on protein-DNA interaction on a routine basis .
The B-lymphoblastoid cell lines from CEPH pedigree 1459 were obtained from Coriell and cultured in RPMI1640 medium ( Cellgro ) , supplemented with 15% fetal bovine serum ( FBS , Hyclone ) , 2 mM L-Glutamine , and 25 IU/mL penicillin and 25 µg/mL streptomycin ( Cellgro ) . B-lymphoblasts ( 5 million cells ) were crosslinked with 1% formaldehyde ( Sigma ) , lysed in lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 10 mM EDTA pH 8 . 0 , 1% SDS ) , and sheared by Bioruptor ( Diagenode ) . The supernatant was further diluted 10-fold with dilution buffer ( 50 mM Tris-HCl pH 8 . 0 , 166 mM NaCl , 1 . 1% Triton X-100 , 0 . 11% sodium deoxycholate ) . For each immunoprecipitation , 100 µL Dynabeads ( M-280 , sheep anti-rabbit IgG , Invitrogen ) were incubated with 20 µL CTCF antibody ( #2899 , Cell Signaling ) for at least 6 hours at 4°C . The antibody-conjugated beads were then incubated overnight with sheared chromatin . The complexes were washed with IP wash buffer I ( 50 mM Tris-HCl pH 8 . 0 , 0 . 15 M NaCl , 1 mM EDTA pH 8 . 0 , 0 . 1% SDS , 1% Triton X-100 , 0 . 1% sodium deoxycholate ) , high salt buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 5 M NaCl , 1 mM EDTA pH 8 . 0 , 0 . 1% SDS , 1% Triton X-100 , 0 . 1% sodium deoxycholate ) , and TE buffer ( 10 mM Tris-HCl pH 8 . 0 , 0 . 1 mM EDTA pH 8 . 0 ) . Crosslinking was then reversed in elution buffer ( 10 mM Tris-HCl pH 8 . 0 , 0 . 3 M NaCl , 5 mM EDTA pH 8 . 0 , 0 . 5% SDS ) at 65°C overnight . The DNA was separated from the beads and treated with Proteinase K ( Fermentas ) and purified by phenol-chloroform extraction and ethanol precipitation . Sequencing libraries were constructed according to Illumina's genomic prep kit protocol as previously described [28] . Briefly , ChIP DNA was end-repaired using the End-it DNA repair kit ( Epicentre ) . Adenines were added to the 3′ ends of the blunt-ended DNA using Taq DNA polymerase ( NEB ) . PE adapter ( 1∶20 dilution , 1 µL for 15–50 ng starting ChIP DNA , Illumina ) was ligated to the ends of the A-tailed ChIP DNA with T4 DNA ligase ( NEB ) . 1/3-1/4 of the purified ligation product was PCR amplified for 16 cycles with High-fidelity PCR master mix ( NEB ) and PE primer 1 . 0/2 . 0 ( Illumina ) . Libraries were run on 2% agarose gels , size-selected , and purified with QIAquick gel extraction kit ( Qiagen ) . The libraries were sequenced to 36 bp on an Illumina Genome Analyzer by the High-throughput Genomics Unit ( University of Washington ) according to standard protocol . ChIP-seq experiments were performed on 2–3 independently cultured biological replicates per sample ( Table S1 , Table S2 ) . High quality reads were aligned to the reference genome using the Eland aligner . SPP [52] was used to call peaks on total tag data from the 12 samples , resulting in 51 , 686 peaks at a Poisson-derived FDR of 1% . Using the aggregate of all tags was more conservative ( in terms of number of peaks ) than taking the union of peak calls on individual samples , but less conservative than taking the intersection . The MTC method ( “tag . lwcc” ) was used to call point binding positions ( Table S5 ) . Motif representations used Weblogo [53] . We designed an Agilent 244k SureSelect microarray for targeted resequencing on the 51 , 686 ChIP-seq peaks identified in the 12 samples . We used fimo ( http://meme . sdsc . edu/meme/ ) to scan for instances of the core 14 bp of the canonical motif [18] with a p-value of 10−2 . We adjusted the target locations to center on matches to the nearest CTCF motif if the motif was within 50 bp , and added flanking targets to capture additional nearby motifs . 5 potential probes were tiled at 15 bp spacing to the 120 bp surrounding each target . We adjusted probe binding energy similarly to Ng et al . [54] , adjusting the spacing of probes by up to 5 bp and adjusting the lengths to between 40–60 bp to reach a predicted Tm between 60–72°C . We used the Duke Uniqueness 20 bp track ( UCSC genome browser ) to filter out 5 , 828 probes with potential for cross-hybridization . We further excluded 145 probes in satellite repeats ( RepeatMasker , UCSC genome browser ) or with high blast scores to multiple genomic locations . The final design had 242 , 380 probes targeting 46 , 652 CTCF sites . Genomic DNA was extracted from cultured cells , and targeted capture was performed based on Supplementary Protocol 3 of Mamanova et al . 2010 [55] with modifications . Briefly , 12 µg of genomic DNA was sheared in a Covaris S2 ( Covaris , Inc . ) using a duty cycle of 10% , intensity 5 , cycle/burst 100 , time 600 sec . DNA was end-repaired , A-tailed , and ligated to SE adapters ( Illumina ) , and purified with Agencourt AMPure XP beads ( Beckman Coulter ) . A trial PCR amplification using Phusion HF polymerase ( NEB ) and SE primers SLXA_FOR_AMP and SLXA_REV_AMP [54] ( IDT ) was performed on a fraction of ligated DNA with an Roche LightCycler 480 to determine the optimal number of cycles . PCR for all ligated DNA was then performed in eight 200 µL tubes using the following program: 98°C for 30 s , then a previously determined number of cycles of 98°C for 10 s , 65°C for 30 s , 72°C for 30 s , followed by 72°C for 5 min . The final DNA library was pooled and concentrated to 5 µL in a SpeedVac . For hybridization , 10 µg of DNA library was combined with formamide ( Ambion ) , blocking oligos ( SLXA_FOR_AMP , SLXA_REV_AMP , SLXA_REV_AMP_rev and SLXA_FOR_AMP_rev [54] , Human C0t-1 DNA ( Invitrogen ) , 2× Hi-RPM Hybridization Buffer ( Agilent ) and 10× Blocking Buffer ( Agilent ) . Hybridization was performed using the Maui Hybridization System ( BioMicro Systems , Inc . ) at 55°C for 48 hours according to “MAUI Mixer LC on Agilent 244K CGH Microarrays” protocol . After hybridization , microarrays were washed with Agilent aCGH Wash Buffers 1 and 2 , sealed with Secure-Seal ( GRACE Bio-Labs ) and placed on heat block ( VWR ) for elution of DNA . DNA was eluted with 3 mL of 95°C PCR-grade water , concentrated , and amplified with SE primers using the same PCR program as above . The libraries were sequenced to 36 bp on an Illumina Genome Analyzer by the High-throughput Genomics Unit ( University of Washington ) according to standard protocol . Reads were aligned to the human genome ( hg18 ) using bwa 0 . 5 . 8 [56] using default settings , allowing up to 2 mismatches ( Table S3 ) . Some lanes exhibited an excess of mismatches to the reference sequence at the 5′ or 3′ end , so tags were trimmed by up to 9 bp . Reads with identical 5′ ends were presumed to be PCR duplicates and were excluded using Picard v1 . 22 MarkDuplicates ( http://picard . sourceforge . net/ ) . SAMtools v0 . 1 . 7 [57] was used to generate a pileup of potential SNPs from uniquely-mapping reads with a mapping quality above 30 . SNPs were called ( Table S6 ) as biallelic variants with at least 8× resequencing coverage , a Phred-scaled SNP quality of at least 30 , at least 20% of reads matching the allele with lower coverage , and Mendelian segregation according to PLINK v1 . 07 [58] . The chimpanzee allele was identified using axtNet alignment files for PanTro2 from the UCSC Genome Browser . We performed two validations of the SNP calls from our targeted resequencing . First , we performed Sanger sequencing on PCR products from genomic DNA ( Table S8 ) . We tested 33 SNPs in all 12 samples . 0 of the 388 genotype calls were discordant . Second , we compared genotypes with genotypes available from the HapMap project for 6 of the 12 samples ( Table S9 ) . Release 27 genotypes were obtained from http://hapmap . ncbi . nlm . nih . gov , and matched to capture resequencing SNPs by location . 244 of the 27 , 808 genotype calls in common ( 0 . 88% ) were discordant . For each individual , a custom human genome was created from hg18 including autosomes , unscaffolded contigs , mitochondrial DNA , a Y chromosome for males , and the Epstein-Barr virus genome ( gi|9625578|ref|NC_001345 . 1 ) . Each genome was personalized to reflect SNPs identified by resequencing , using IUPAC codes to represent heterozygous position . ChIP-seq data was mapped using MosaikAligner v1 . 1 . 0021 ( http://code . google . com/p/mosaik-aligner/ ) with the options “-bw 13 -act 20 -mhp 100 -m unique -mm 4 -minp 1 . 0” , requiring a unique mapping considering up to 4 mismatches . Reads with more than 2 mismatches were then discarded . Reads with identical 5′ ends were presumed to be PCR duplicates and were excluded using Picard v1 . 22 MarkDuplicates . Smoothed density tracks were generated using bedmap ( http://code . google . com/p/bedops/ ) to count the number of tags overlapping a sliding 150 bp window , with a step width of 20 bp . Density tracks were normalized for sequencing depth by a global linear scaling to fix an arbitrary value of 25 as the 50th percentile of bins with more than 15 reads . We identified instances of the canonical motif ( fimo p-value<10−2 ) within 15 bp of the center of the resequencing target , keeping the motif with the best p-value . We measured occupancy by the maximum normalized ChIP-seq tag density over the 14 bp motif . We applied a regression method to measure whether a particular biallelic SNP is associated with occupancy , and if so which allele is associated with higher occupancy . We tested only sites with ≥8× resequencing coverage in ≥6 samples , sufficient mappability , and data for ≥4 data points each for ≥2 genotypes and ≥12 data points overall ( Table S7 ) . We further excluded 242 sites overlapping indels in the CEU population identified by the 1000 Genomes Project release 2010_07 [59] . We used a negative binomial generalized linear model ( glm . nb in the R package MASS ) to measure the significance of the effect of polymorphism on occupancy using an additive effect model , and including a replicate term to account for batch effects . We used two ChIP-seq replicates per sample , except for GM12870 , which had one replicate ( Table S1 ) . For sites with more than one SNP , we tested only the SNPs with more data points and those inside the region of protein-DNA contact . If there were still multiple SNPs per window , we chose the SNP with the lowest p-value , though these sites were then excluded from analyses depending on the known position of the SNP relative to the motif . We also tried fitting a dominant effect model where permitted by sample size: we chose between additive and dominant effect models using the Akaike information criterion . We separately fitted a linear model on the same data to estimate the r2 . We used the R package qvalue to estimate q-values ( Figure S5 ) [60] , which established a cutoff of p<9 . 6*10−4 as an FDR of 1% ( 325 sites ) . Using a Benjamini-Hochberg FDR strategy confirms a similar cutoff for FDR 1% ( 4 . 9*10−4 , 293 sites ) . To independently confirm our FDR methodology , we considered the proportion of significant SNPs within the region of protein-DNA contact ( Figure 3 ) , which ranged from 71% , to 85% , to 91% at FDRs 5% , 1% , 0 . 1% , respectively ( Table S4 ) . We used 31 , 128 SNPs identified in our resequencing as markers to generate a map of identity-by-descent ( 95th percentile marker spacing of 0 . 5 cM ) . We used recombination rate data [59] to place our SNP coordinates onto a genetic map , choosing SNPs with fewer missing genotypes at duplicate map positions . We then used MERLIN [61] to filter out improbable genotypes and compute IBD at our marker locations ( option “–ibd” ) . We used the nearest marker at each binding site to estimate IBD for the 15 possible sib pairs . IBD values were placed into 3 bins ( 0 , 0 . 5 , and 1 . 0 ) ; values with >0 . 05 uncertainty were excluded . We then used the package DESeq [62] to identify differentially occupied regions , both throughout all 12 samples as well as among just the grandchildren , using two replicates per sample ( Table S1 ) . We then applied a variance stabilizing transformation , and normalized the occupancy at each site to a mean of 0 and standard deviation of 1 . We then averaged the occupancy of the two replicates . We then performed Haseman-Elston regression at the 97 autosomal sites differentially occupied among the grandchildren , treating separately sites whose differential occupancy was already associated with a SNP or allele-specific binding . We considered all sib pairs and all sites simultaneously . Although regression methods exist with higher power [63] or that use data from all members of the pedigree [64] , we applied the original method of regressing squares of trait differences for sib pairs , reasoning that the robustness of a simple method would have more forgiving assumptions . To account for the non-independence of measuring multiple sib pairs from the same family , we assessed the significance of the regression slope by permuting IBD vectors to random sib pair difference vectors for all differentially occupied sites , thus maintaining any correlation structure in the data . We tested allele-specific occupancy in pooled replicate data for each sample . Given the reliance of allele-specific occupancy tests on high coverage at heterozygous sites , we included data from an additional replicate for 4 samples ( Table S1 ) . We had sufficient power to test 2 , 535 heterozygous binding sites with adequate mappability and 13× ChIP-seq read coverage . We used a chi-squared test against a 50∶50 null expectation to derive a p-value from the counts of the ChIP-seq tags mapping to the two alleles . We used the R package qvalue to estimate an FDR ( Figure S5 ) [60] . In interpreting our FDR threshold , we considered the observed concentration of significant SNPs within the 44 bp region of protein-DNA interaction ( Figure S2 ) , which increased at more conservative FDRs: 63% , 66% , 68% at FDRs 1% , 0 . 5% , 0 . 1% , respectively ( Table S4 ) . At sites for which multiple samples had testable heterozygous sites with the same alleles , the sample with the most total reads was picked as representative for plotting . We used PWM models [19] to measure the effect of a polymorphism on information content . CTCF binds in a multivalent fashion [21] , [65] , [66] , wherein three modes of binding are distinguished by the presence and position of an upstream motif . At each site we chose the best-matching of the three motif models . To measure information content , we converted frequencies to log-odds , using a pseudo-frequency of 0 . 01 ( the minimum observed frequency ) . We calculated the mutual information between whether a given SNP affected occupancy ( FDR 5% ) and sequence context at 14 positions in the core CTCF motif using the mutualInfo function of the R package bioDist . To estimate the significance of the mutual information values , we applied a bootstrap method for each pair of positions tested . We calculated p-values from 2 , 000 iterations of resampling per pair of positions ( Figure S7 ) . To account for multiple testing across all positions , we used the R package qvalue [60] . We downloaded phyloP based on a 44-way vertebrate alignment from the UCSC Genome Browser . SNPs were ranked in decreasing order of the phyloP score at the location of the SNP , thus ranking sites with the most indication of purifying selection the likely to be disrupted by a SNP . To measure the predictive power of models of protein-DNA binding energy , we used a position weight matrix to compute the difference in log-odds score between the two alleles of each SNP . Sites with the largest difference between alleles at the location of the SNP were ranked as most likely to be disruptive . Plots were generated using the R package ROCR [67] . ChIP-seq data are viewable in the UCSC Genome Browser ( http://genome . ucsc . edu , version hg18 ) , and have been deposited in GEO ( GSE30263 ) . Resequencing data are available in the SRA ( SRP009457 ) , and the capture resequencing array design in GEO ( GPL14147 ) .
|
A comprehensive understanding of the contribution of individual genome sequences to disease and quantitative traits will require the general ability to predict consequences of genetic variation in non-protein-coding regions , particularly those involved in gene regulation . Here we tested the power to predict such consequences when presented with “complete” information encompassing the genomic DNA binding site patterns of a well-studied regulatory protein across multiple related individuals , coupled with all individual genome sequences at the binding positions . We find that , while there is reasonable ability to predict the average effects of variation within the consensus recognition sequence of a transcriptional regulator , it is not possible to determine reliably the consequences of variation at any given genomic instance . This suggests that the interpretation of individual genome sequences will require comprehensive complementation with functional genomic studies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"genomics",
"genetics",
"and",
"genomics"
] |
2012
|
Widespread Site-Dependent Buffering of Human Regulatory Polymorphism
|
The Type VI secretion system ( T6SS ) is a widespread weapon dedicated to the delivery of toxin proteins into eukaryotic and prokaryotic cells . The 13 T6SS subunits assemble a cytoplasmic contractile structure anchored to the cell envelope by a membrane-spanning complex . This structure is evolutionarily , structurally and functionally related to the tail of contractile bacteriophages . In bacteriophages , the tail assembles onto a protein complex , referred to as the baseplate , that not only serves as a platform during assembly of the tube and sheath , but also triggers the contraction of the sheath . Although progress has been made in understanding T6SS assembly and function , the composition of the T6SS baseplate remains mostly unknown . Here , we report that six T6SS proteins–TssA , TssE , TssF , TssG , TssK and VgrG–are required for proper assembly of the T6SS tail tube , and a complex between VgrG , TssE , -F and-G could be isolated . In addition , we demonstrate that TssF and TssG share limited sequence homologies with known phage components , and we report the interaction network between these subunits and other baseplate and tail components . In agreement with the baseplate being the assembly platform for the tail , fluorescence microscopy analyses of functional GFP-TssF and TssK-GFP fusion proteins show that these proteins assemble stable and static clusters on which the sheath polymerizes . Finally , we show that recruitment of the baseplate to the apparatus requires initial positioning of the membrane complex and contacts between TssG and the inner membrane TssM protein .
In the environment , bacteria endure an intense warfare . Bacteria collaborate or compete to acquire nutrients or to efficiently colonize a niche . The outcome of inter-bacteria interactions depends on several mechanisms including cooperative behaviors or antagonistic activities [1] . The newly identified Type VI secretion system ( T6SS ) is widely distributed among proteobacteria and has been reported to be a key player in antagonism among bacterial communities [2–4] . Although several T6SSs have been shown to be required for full virulence towards different eukaryotic cells , most T6SSs shape bacterial communities through inter-bacteria interactions [1] . In both cases , T6SSs inject toxic effectors into target/recipient cells . A number of anti-bacterial toxins have been recently identified and carry a versatile repertoire of cytotoxic activities such as peptidoglycan hydrolases , phospholipases or DNases [1 , 5 , 6] . Delivery of these toxins into the periplasm or cytoplasm of the target cell leads to a rapid lysis that usually occurs within minutes [7–9] . At a molecular level , the T6SS core apparatus is composed of 13 conserved subunits that assemble a long cytoplasmic tubular structure tethered to the cell envelope by a trans-envelope complex [3 , 10–12] . The composition , structure and biogenesis of the membrane-associated complex has been well characterized over the last years . It is composed of three proteins: TssL , TssM and TssJ . The TssL and TssM proteins interact in the inner membrane whereas the periplasmic domain of TssM contacts the TssJ outer membrane lipoprotein [13–15] . The current model considers the cytosolic complex of the T6SS to be similar to tails of contractile bacteriophages . These two related structures feature a cell-puncturing syringe and a contractile sheath wrapping an inner tube . The T6SS inner tube is composed of Hcp hexamers stacked on each other [16–18] . The cell-puncturing syringe assembles from a trimer of the VgrG protein tipped by the PAAR protein and is thought to cap the Hcp tube [16 , 19] . This structure is structurally comparable to the tail tube composed of polymerized gp19 proteins capped by the gp27-gp5 complex–or hub–in the bacteriophage T4 [20] . The TssB and TssC proteins share structural and functional similarities with the bacteriophage T4 gp18 sheath [8 , 21–25] . Indeed , time-lapse fluorescence microscopy experiments using a TssB-GFP fusion revealed that these structures are highly dynamic: they assemble micrometer-long tubes that sequentially extend in tens of seconds and contract in a few milliseconds [8 , 9 , 26 , 27] . The mechanism of contraction is thought to be similar to that of contractile bacteriophages [22–25] . Recent fluorescence microscopy assays in mixed culture evidenced that contraction of this sheath-like structure correlates with prey killing [7–9] . Based on these data and on the mechanism of bacteriophage infection , the current model proposes that the contraction of sheath-like structure propels the Hcp inner tube towards the target cell , resulting in the cell envelope puncturing and delivery of anti-bacterial toxins [3 , 10 , 28] . In tailed phages , tube and sheath polymerize on a structure called the baseplate . The bacteriophage T4 baseplate is composed of 140 polypeptide chains of at least 16 different proteins . This highly complex structure assembles from 6 wedges surrounding the central hub . Seven proteins form the baseplate wedges ( gp11 , gp10 , gp7 , gp8 , gp6 , gp53 and gp25 ) [22 , 29–31] . However , in other tailed bacteriophages such as P2 , the baseplate is significantly less complex as it is only composed of four different subunits: gpV ( the homologue of the hub ) and the wedge components W ( the homologue of gp25 ) , gpJ ( gp6-like ) and gpI [32 , 33] . Based on this observation , Leiman & Shneider formulated the concept of a minimal contractile tail-like structure [22] . In the minimal contractile tail , the baseplate could be significantly “simplified” as long as it performs its main functions: controlling tube assembly , initiating sheath polymerization and triggering sheath contraction . The minimal baseplate should then conserve the central hub and three other wedge proteins: gp6 , gp25 and gp53 [22] . The central hub bears the spike and acts as a threefold to sixfold adaptor connecting the tail tube . Gp25 initiates the polymerization of the sheath . Gp6 connects the wedges together maintaining the baseplate integrity during the infection process . The role of gp53 remains unclear . However , gp53 is required for gp25 to assemble onto the gp6 ring [22] . In the T6SS , the assembly of the tail-like tube and sheath must require components that will perform similar functions . With the exceptions of TssE and VgrG , which feature striking homologies to the gp25 protein and the gp27-gp5 hub complex respectively , the components that assemble the T6SS baseplate are unknown . By analogy with the morphogenesis pathway of contractile bacteriophages , we hypothesized that the assembly of the tail tube should be impaired in absence of a functional baseplate . We therefore recently developed a biochemical approach based on inter-molecular disulfide bonds to probe the assembly of the Hcp tube in vivo , in the cytoplasm of enteroaggregative Escherichia coli ( EAEC ) [18] . We demonstrated that Hcp hexamers stacked in a head-to-tail manner to form bona fide tubular structures in vivo [18] . More importantly , the precise stacking organization of Hcp hexamers became uncontrolled in absence of the other T6SS components . Here , using the collection of nonpolar T6SS gene deletions we provide evidence that six T6SS proteins are required for proper assembly of Hcp tubes: TssA , TssE , TssF , TssG , TssK and VgrG . The identification of TssE and VgrG , two known homologues of bacteriophage baseplate components , validates the experimental approach . We further characterize the TssF and TssG proteins . We report that these two proteins interact and stabilize each other , and make contacts with TssE , TssK and VgrG as well as with tube and sheath components . A bioinformatic analysis suggests that TssF and TssG share similarities with the J and I proteins of the bacteriophage P2 baseplate respectively . Fluorescence microscopy experiments further show that functional GFP-TssF ( sfGFPTssF ) and TssK-GFP ( TssKsfGFP ) proteins assemble into static foci near the cell envelope . The integrity of the sfGFPTssF foci is dependent on TssK and its proper localization requires interactions between TssF , TssG and the cytoplasmic loop of TssM . Futhermore , co-localization experiments with mCherry-labeled TssB demonstrate that sfGFPTssF clusters are positioned prior to sheath subunits recruitment and remain at the base of the sheath during elongation and contraction . Taken together the biochemical and cytological approaches presented in this study provide support to the role of TssE , TssF , TssG , TssK and VgrG as T6SS baseplate components and to a sequential recruitment hierarchy ( membrane complex , baseplate , tail tube/sheath ) during T6SS biogenesis .
Using an in vivo inter-molecular cross-linking approach , based on disulfide bond formation between adjacent cysteine residues , we recently reported that the EAEC Hcp hexamers organize head-to-tail to form tubular structures in the cytoplasm of EAEC . Importantly , we also demonstrated that these hexamers stack randomly in a strain lacking the Sci-1 T6SS subunits , resulting in head-to-tail , tail-to-tail and head-to-head configurations ( [18] , Fig 1A ) . During the morphogenesis of tailed bacteriophages , the tail-tube and-sheath structures polymerize on an assembly platform referred to as baseplate [22] . Additionally , the gp19 tail tube of bacteriophage T4 does not polymerize in absence of a fully functional baseplate [34] . By analogy , we reasoned that the aberrant assembly of Hcp hexamers in vivo could report a defective baseplate-like structure in the T6SS . We therefore probed the assembly of Hcp in each nonpolar Δtss strain ( each lacking an essential Tss subunit ) using the disulfide bond assay . As proof of concept , we previously showed that the Sci-1 T6SS-associated spike protein , VgrG , is required for proper assembly of Hcp tubes [18] . Aside the cysteine-less Hcp C38S protein , three combinations were tested to probe head-to-tail ( G96C-S158C ) , tail-to-tail ( Q24C-A95C ) or head-to-head ( G48C ) stacking . As previously reported , SDS-PAGE analyses of cytoplasmic extracts from Δhcp oxidized cells producing these variants showed that the head-to-tail G96C-S158C combination leads to formation of dimers and higher molecular weight complexes while the tail-to-tail Q24C-A95C and head-to-head G48C combinations remain strictly monomeric ( Fig 1A ) . Similar results were obtained for the tssB , tssC and clpV backgrounds , suggesting that tail sheath components do not regulate tail tube assembly ( Fig 1B ) . This is in agreement with the morphogenesis pathway of contractile bacteriophages in which tail tube polymerization immediately precedes that of the sheath . Importantly , we also observed that Hcp hexamers properly assemble in the cytoplasm of tssJ , tssL and tssM mutants ( Fig 1B ) . TssJ , TssL and TssM interact to form the trans-envelope complex that anchors the T6SS tail-like structure to the membranes [12–15] . Proper assembly of the tail tube structure is therefore independent of the membrane complex , a result in agreement with the different evolutionarily history of the T6SS membrane and phage complexes [2 , 35–37] . However , the controlled assembly of Hcp tubes was impaired in the vgrG , tssA , tssE , tssF , tssG and tssK backgrounds: Hcp hexamers interact in head-to-tail , tail-to-tail or head-to-head packing ( Fig 1B ) . Proper Hcp assembly was restored in these different mutant strains when a wild-type allele of the missing gene was expressed from complementation vectors ( Fig 1C ) . From these results , we concluded that six T6SS components , i . e . VgrG , TssA , TssE , TssF , TssG and TssK , increase the efficiency of tube formation in vivo and therefore control the assembly of Hcp tubes . TssE and VgrG share structural homologies with the gp25 protein and the gp27-gp5 complex ( hub ) respectively [16 , 19 , 35 , 38] . During the morphogenesis of the bacteriophage T4 , the tail tube assembly initiates onto the baseplate only after the ( gp27-gp5 ) hub complex has been recruited , while the gp25 subunit is required for functional baseplate assembly [39] . The observation that Hcp assembly was impaired in vgrG and tssE cells is therefore in agreement with the bacteriophage assembly pathway and further validates the initial hypothesis that Hcp tube proper polymerization depends on a baseplate-like structure . Based on these observations , we hypothesized that TssA , TssF , TssG and TssK may form , along with TssE and VgrG , a platform similar to the bacteriophage baseplate . However , while the TssE , TssK and VgrG subunits have been previously characterized [16 , 38 , 40] , little information on TssA , TssF and TssG is available . The bioinformatics study published by Boyer et al . demonstrated a high level of co-occurrence between the tssE ( COG3518 ) , tssF ( COG3519 ) and tssG ( COG3520 ) genes . tssE and tssF are genetically linked in 87% of the T6SS gene clusters whereas the co-organization of tssF and tssG occurs in 97% of these clusters ( [2] , Fig 2A ) . As noted by Boyer and collaborators [2] , co-occurrence usually reflects protein-protein interactions . Indeed , we show below that TssF and TssG are two components of the T6SS baseplate . By contrast , no co-occurrence of the tssA gene ( COG3515 ) with tssE , vgrG or hcp was noticed in this study . Although TssA is required for Hcp tube formation , we will report elsewhere that it is not a component of the T6SS per se ( Zoued , Durand et al . , in preparation ) . We therefore focused our further work on the two uncharacterized tssF and tssG genes . Bioinformatic analyses: TssF and TssG are homologues to phage tail proteins . To gain further insights onto TssF and TssG we performed a bioinformatic analysis using HHPred ( homology detection and structure prediction , [41] ) . The Sci-1 EAEC TssF ( accession number: EC042_4542; gene ID: 387609963 ) and TssG ( accession number: EC042_4543; gene ID: 387609964 ) protein sequences were used as baits to identify homologues in bacteriophages . HHpred analyses with TssF reported that the fragment comprising residues 7–144 ( over 587 ) resembles region 9–132 of the phage tail-like protein TIGR02243 ( PFAM04865 ) that has for prototype the protein J of phage P2 . The segment encompassing residues 86–179 shares also secondary structures with residues 89–178 of gp6 , a baseplate wedge component of bacteriophage T4 ( Fig 2B and 2C ) . Similarly , the TssG fragment comprising residues 37–152 ( over 303 ) resembles region 4–110 of the phage tail-like protein TIGR02242 ( PFAM09684 ) that has for prototype the protein I of phage P2 ( Fig 2D ) . The phage P2 baseplate is composed of four subunits: V , W , I and J [32] . Protein V is an homologue of the bacteriophage T4 gp27-gp5 complex ( VgrG ) [42] whereas protein W is the homologue of gp25 ( TssE ) . Leiman & Shneider recently hypothesized that the baseplate of a minimal contractile structure assembles from a central hub ( gp27-gp5 and the protein V in the bacteriophages T4 and P2 respectively ) and three key wedge proteins ( gp25 , gp6 and gp53 ) in the bacteriophage T4; W , I and J proteins in the bacteriophage P2 ) [22] . The predicted structural homologies suggest that the N-terminal region of TssF corresponds to the N-terminal region of gp6 whereas TssG ( and phage P2 protein I ) corresponds to gp53 . We propose therefore that the baseplate of the T6SS is composed of at least four subunits ( VgrG ( gp27-gp5; V ) , TssE ( gp25; W ) , TssF ( gp6; J ) and TssG ( gp53; I ) . T6SS function: TssF and TssG are required for sheath polymerization and Hcp release . A set of elegant studies coupling genetic and biochemical approaches to electron microscopy imaging demonstrated that during the morphogenesis of bacteriophage particles the absence of baseplate components prevents polymerization of the inner tube and of the outer sheath [31 , 39 , 43–45] . Here , we followed the dynamic of a chromosomally-encoded TssB-mCherry fusion protein ( TssBmCh ) using time-lapse fluorescence microscopy . We observed that sheath assembly is abolished in tssF and tssG cells ( S1A Fig ) . In agreement with the absence of sheath polymerization and contraction , western blot analyses of culture supernatants showed that tssF and tssG cells do not release Hcp in the medium ( S1B Fig ) . Interaction network: TssF and TssG interact with TssE , VgrG , TssK , Hcp and TssC . To gain further information on TssF and TssG partners , we used a bacterial two-hybrid ( BTH ) -based systematic approach . T18-TssF/G and TssF/G-T18 translational fusions were tested against the phage-related T6SS core-components ( TssB , TssC , Hcp , TssE , VgrG and TssA ) fused to the T25 domain . As shown in Fig 3A , TssF interacts with Hcp , while TssG interacts with TssC , Hcp and TssE . These pair-wise interactions were then tested by co-immunoprecipitation in the heterologous host E . coli K-12 . Fig 3B shows that HA-tagged Hcp was co-immunoprecipitated with FLAG-tagged TssF or TssG . Fig 3C shows that HA-tagged TssG was co-immunoprecipitated with FLAG-tagged TssE . Interestingly , the HA-tagged TssF was also specifically co-immunoprecipitated with TssE ( Fig 3D ) even though the BTH assay failed to detect this interaction . Taken together , the BTH and co-immunoprecipitation assays provide evidence that TssF and TssG interact with T6SS tail components , including the TssE baseplate protein , as well as with the tube and sheath proteins Hcp and TssC . These data provide further evidence to support the Hcp assembly assay and the bioinformatics analyses to propose that TssF and TssG form with TssE and VgrG the T6SS baseplate onto which the tube ( Hcp ) and sheath ( TssBC ) polymerize . In bacteriophages such as T4 , six wedges formed by the gp6-gp25-gp53 complex assemble around the central hub . We therefore tested whether the TssE-F-G complex interacts with VgrG using a reconstitution approach . Lysates of cells producing VSV-G epitope-tagged VgrG and FLAG-tagged TssE , -F and-G were mixed prior to immunoprecipitation on anti-VSV-G resin . Fig 3E shows that VgrG efficiently precipitates TssE , -F and-G . Based on this result and on the bacterial two-hybrid approach ( Fig 3A ) , we hypothesized that TssE links VgrG and TssF/TssG . We therefore repeated the immunoprecipitation experiments in absence of TssE . However , in these conditions we also observed that both TssF and TssG co-immunoprecipitate with VgrG ( Fig 3E ) . Because we did not detect direct VgrG-TssF and VgrG-TssG interactions in BTH and co-immunoprecipitation assays , these results suggested that formation of a TssF-TssG complex is pre-required to interact with VgrG . Indeed , BTH experiments showed that VgrG-TssF and VgrG-TssG interactions are detected when the third partner , TssG and TssF respectively , is present ( Fig 3F ) . A stable TssKFG complex was recently reported [46] . However , we did not observe interactions between TssK and TssF or TssG neither in this study , nor in the systematic interaction study of TssK [40] . Interestingly , further BTH experiments showed that both TssF and TssG are required to stably interact with TssK ( Fig 3F ) , similarly to what we observed for VgrG . Therefore , we conclude that formation of the TssFG sub-complex is a pre-requisite for further interactions with VgrG and TssK . The experiments described above and the genetic linkage of the tssF and tssG genes suggest that TssF and TssG interact . To test this hypothesis , we first performed steady-state stability experiments in E . coli K-12 cells producing TssF , TssG or both TssF and TssG . Cells were harvested at different time points after inhibition of protein synthesis , and the stabilities of TssF and TssG were estimated by Western blot . Fig 4A shows that TssF and TssG , when produced alone , are relatively unstable proteins as TssF and TssG were undetectable 120 and 60 minutes after protein synthesis inhibition respectively . However , both proteins were stabilized when co-produced and remained detectable up to 8 hours after protein synthesis arrest . This result shows that TssF and TssG stabilize each other . The co-stabilization of these two proteins is in agreement with a recent work showing that the Serratia TssF and TssG proteins are unstable in absence of the other [46] . To test for direct interaction , we performed BTH and co-immunoprecipitation experiments . First , the BTH assay revealed that ( i ) both TssF and TssG are involved in homotypic interactions suggesting that these proteins dimerize or multimerize , and ( ii ) that these two proteins interact ( Fig 4B ) . However , the location of the fusion at the C-terminus of TssF or at the N-terminus of TssG causes a steric hindrance that prevents TssF-TssG complex formation . In addition , the HA epitope-tagged TssF protein was specifically co-immunoprecipitated with FLAG-tagged TssG in the heterologous T6SS- host E . coli K-12 ( Fig 4C ) demonstrating that this interaction is not mediated by an another T6SS components . Taken together , these experiments demonstrate that TssF and TssG form a complex that stabilizes both subunits . Interestingly , within the uropathogenic E . coli ( UPEC ) CFT073 T6SS gene cluster , the tssF and tssG genes are fused , leading to a tssF'-'tssG chimeric gene ( see Fig 4D ) . A Clustal W protein sequence alignment of the EAEC TssF and TssG proteins with the UPEC TssF-G fusion showed that the C-terminal PG residues of TssF are fused to the N-terminal MGFP residues of TssG to yield a PGMGFP motif in the fusion protein ( see S2 Fig ) . To test whether a fusion protein might be functional in EAEC , we constructed a ΔtssFG mutant strain and fused the tssF and tssG genes in frame either in the native ( F-G fusion ) or in the opposite orientation ( G-F fusion ) . Both fusion proteins accumulated at comparable levels . The tssF and tssG genes were also cloned contiguously , mimicking their natural genetic organization ( F+G ) in EAEC . As expected , the T6SS was nonfunctional in ΔtssFG cells as Hcp was not released in the culture supernatant , nor in the supernatant of ΔtssFG cells producing TssF or TssG alone . The WT phenotype was restored upon production of both TssF and TssG , or of the TssF-TssG fusion protein; however , production the TssG-TssF “inverted” fusion protein failed to complement the ΔtssFG mutation ( Fig 4E ) . Taken together , these data provide evidence that TssF and TssG interact . The observation that the TssF-TssG fusion protein is functional suggests that TssF and TssG form a sub-complex with a 1:1 stoichiometry . However , by using quantitative gel staining , English et al . recently reported a 2:1 molar ratio for the Serratia TssF:G complex [46] . Although our data suggest a 1:1 stoichiometry , we cannot rule out that the TssF-G fusion protein we engineered is subjected to partial degradation . In bacteriophage T4 , a 2:1 ratio has been noted for the gp6:gp53 complex [31] , in agreement with the Serratia data [46] . To gain insight onto TssF and TssG , we further tested their sub-cellular localizations using cell fractionation experiments . In WT cells , both TssF and TssG mainly co-fractionated with EFTu , a cytoplasmic elongation factor ( Fig 5A ) . Surprisingly , small but reproducible amounts of TssF and TssG were found associated with the membrane fraction ( Fig 5A ) . TssF and TssG co-fractionate with the IM protein TolA , and the NADH oxidase activity in sedimentation density gradient experiments indicating that both proteins associate with the inner membrane ( IM ) ( Fig 5B ) . These results suggest that TssF and TssG are peripherally associated with the IM , probably through protein-protein contacts . Interestingly , both proteins exclusively localized in the cytoplasmic fractions in E . coli K-12 ( i . e . , devoid of T6SS genes ) ( Fig 5C ) , further supporting the notion that TssF and TssG are tethered to the inner membrane by a T6SS component . Three proteins of T6SS interact to form a membrane-associated complex that spans the cell envelope: the inner membrane TssL and TssM proteins and the outer membrane TssJ lipoprotein [12–15] . To identify TssF and TssG partners , we tested their interactions with the soluble domains of TssL , TssM and TssJ by BTH . As shown in Fig 5D , TssG interacts with the cytoplasmic loop of the inner membrane protein TssM ( TssMc ) . This result was validated by co-immunoprecipitation: the HA-tagged TssG was co-immunoprecipitated with the FLAG-tagged TssMc domain ( Fig 5E ) . The hypothesis that TssF and TssG were recruited to the membrane through interactions with TssM was tested by cell fractionation in ΔtssM cells . Fig 5F shows that in absence of TssM , TssF and TssG co-fractionate exclusively with the cytoplasmic marker EFTu and do not associate with the membrane fraction anymore . Taken together , these data show that TssF and TssG are recruited at the cytoplasmic face of the inner membrane via interactions with the cytoplasmic loop of TssM . However , association to the membrane complex might be stabilized by additional contacts involving TssFG-TssK and TssK-TssL or TssK-TssM interactions [40] . The previous data suggest that TssE , -F , -G , -K , VgrG assemble a baseplate structure anchored to the T6SS membrane complex . Based on the knowledge on bacteriophages , we hypothesized that this baseplate serves as assembly platform for tail tube/sheath extension . To further gain information on baseplate components cellular locations , recruitment and dynamic behavior , we engineered strains producing super-folder GFP ( sfGFP ) fused to the N- or C-terminus of TssE , TssF , TssG and TssK . All these chromosomal constructs were introduced at the native , original loci . Only the GFP-TssF ( sfGFPTssF ) and TssK-GFP ( TssKsfGFP ) fusions were functional . In WT cells , sfGFPTssF forms 1–3 foci per cell , located close to the cytoplasmic side of the envelope and with a limited dynamic ( Figs 6A [upper panel] and S3A and S3B ) . The number of sfGFPTssF foci and their dynamic remained unchanged in tssBC cells suggesting that assembly of the T6SS sheath do not impact formation of these structures ( Figs 6A and S3A and S3B ) . By contrast , the sfGFPTssF fluorescence was diffuse in tssK cells , demonstrating that TssK is required for proper assembly of the TssF-containing baseplates ( Fig 6A ) . The sfGFPTssF behavior was different in tssM cells . Although ~ 95% of the cells present diffuse fluorescence , sfGFPTssF clusters are present in ~ 5% of the cells . These clusters do not remain tightly associated to the membrane but rather display random dynamics ( Fig 6A ) , suggesting that the membrane complexes stabilize and anchor baseplate complexes to the IM . Fluorescence microscopy recordings of cells producing both sfGFPTssF and mCherry-labeled TssB ( TssBmCh ) from their native chromosomal loci further informs the assembly mechanism of the T6SS: i ) sfGFPTssF clusters are positioned first ( Figs 6B and S3C ) , and ii ) The TssBmCh sheath extends from the preassembled sfGFPTssF cluster ( Figs 6B and S3D and S3E ) . In addition , the recordings show that sfGFPTssF foci remain associated with the sheath during all the cycle ( elongation , contraction and disassembly ) ( S3C and S3D Fig ) . These observations strongly support a model in which TssF-containing complexes serve as platforms for the polymerization of the sheaths . The TssKsfGFP fusion present similar behavior to sfGFPTssF: it assembles 1–3 stable and static foci per cell and shows diffuse localization in absence of TssM ( Figs 6C and S3B and S3F ) . Conversely , sfGFPTssM forms discrete foci in WT cells [15] that assemble independently of TssK ( Figs 6D and S3G ) . Based on these results , and in agreement with the hypothesis raised by English and co-authors [46] , we suggest the TssL-M-J membrane complex serves as the docking area for the T6SS tail-like structure , the assembly of which starts with the subsequent recruitments of TssK , TssFG , Hcp and TssBC ( Fig 6E ) .
Recent studies have evidenced that the T6SS assembles a cytosolic structure similar to the tail tube and sheath of contractile bacteriophages [16 , 23–26] . During bacteriophage morphogenesis , the tube and sheath polymerize onto the baseplate that initiates and guides the assembly process [29–31] . In addition , the baseplate undergoes large conformational rearrangements upon landing on a target cell that ultimately trigger tail contraction [30] . In the bacteriophage T4 , the baseplate is a complex structure; however , simplest baseplates exist in other contractile bacteriophages . Based on baseplates comparison , Leiman & Shneider proposed that only four components will be required to have a functional baseplate: the gp27-gp5 hub complex ( or spike ) and the gp25 , gp6 and gp53 wedge subunits [22] . However , in the T6SS , the identity and composition of the baseplate that controls the assembly of the tube/sheath structure are not known [10–12 , 37] . To identify these components , we developed an assay to probe formation of Hcp tubes in vivo [18] . Employing systematically this assay in all T6SS gene deletion mutant strains , we identified six T6SS core components required for the controlled polymerization of the Hcp tube . This experimental approach was validated by the fact we identified VgrG and TssE , the T6SS homologues of the bacteriophage spike/hub complex and gp25 subunits respectively . Among the four remaining subunits , TssA , TssK , TssF and TssG , we showed that the two laters share limited but significant homologies with protein J and I , two baseplate components of phage P2 , respectively [32 , 33] . In addition to bacteriophages and T6SS , homologues of P2 baseplate I and J proteins , as well as homologues of the spike/hub and gp25 , are found in anti-feeding prophages , Photorhabdus Virulence Cassettes and R-pyocins [47] suggesting that they likely represent the core of phage-like protein translocation machineries . A schematic comparison of the homology and contacts between bacteriophage T4 and T6SS components is shown in S4 Fig . In this study , we focused our work on the TssF and TssG proteins . We demonstrated that TssF and TssG interact with each other and with TssE . Although we have not addressed the biological relevance of these interactions , these results are in agreement with the co-occurrence of these three genes in T6SS gene clusters [2] , as well as with the observation that the bacteriophage T4 homologues of TssE and TssF–gp25 and gp6 –interact [48] . Interestingly , Aksyuk and coauthors showed that the gp6-gp25 interaction involves the N-terminal fragment of gp6 , the fragment that shares homology with TssF [48] . In addition , contacts were detected with TssK , VgrG and components of the tube ( Hcp ) and of the sheath ( TssC ) . Interestingly , contacts with TssK and VgrG require the pre-formation of the TssF-G complex . Taken together these data support the idea that the T6SS baseplate is composed of the VgrG , TssE , TssF , TssG and TssK subunits , on which the Hcp tube and the TssB-TssC sheath will sequentially polymerize ( Fig 7 ) . Indeed , co-localization studies showed that TssF is recruited to the apparatus prior to sheath extension . Based on the observations that this baseplate is docked to the membrane complex and remains at the base of the extended tail , it likely corresponds to the structure observed at the same location on T6SS electron cryo-tomographs [26] . The phage wedge and baseplate assembly pathways are regulated by the stepwise addition of the different subunits in a strict order [39 , 44] . Regarding T6SS biogenesis , the results presented here as well as three recent studies [15 , 40 , 46] support the proposal that the TssLMJ membrane complex is first assembled and that the T6SS is built by the hierarchical addition of TssK and TssFG . Alternatively , complete baseplates may assemble prior to docking to the membrane complex ( Fig 7 ) . Indeed , the baseplate-like structure is anchored to the inner membrane by a network of interactions including contacts between TssK and TssL , TssK and TssM , and TssG and TssM ( [40] and this study ) . One may hypothesize that TssK is recruited to the TssJLM complex , and the interaction between the baseplate-like structure and the membrane complex is then stabilized by additional contacts between TssM and the TssFG complex . This assembly pathway is supported by fluorescence microscopy recordings demonstrating that TssF is not recruited to the apparatus in absence of TssK or TssM , and that TssK is not recruited to the apparatus in absence of TssM ( Fig 6 ) . Interestingly , in absence of TssM , TssF-containing complexes ( baseplates ? ) are assembled–albeit at significant lower levels , suggesting that these complexes are stabilized by the membrane complex . The TssM-independent assembly of these TssF-containing complexes is in agreement with the observations that ( i ) T6SS membrane and baseplate/tail complexes have distinct evolutionarily histories [2] and ( ii ) that the membrane complex is not required for proper assembly of Hcp tubes ( Fig 1C ) . Strikingly , phage baseplates display 6-fold symmetry [22 , 29] and one could assume that an identical symmetry will apply for T6SS baseplates . However , the T6SS membrane complex has been recently show to have 5-fold symmetry [15] . Understanding how a 6-fold symmetry structure successfully and functionally associates to a 5-fold symmetry docking station remains to be elucidated . Once the baseplate is docked to the TssJLM complex , the Hcp inner tube/TssBC outer sheath polymerization can proceed ( Fig 7 ) . Indeed , interaction studies showed that TssF and TssG are connected to Hcp and TssC , and might initiate tail extension . Data reporting the specific role of TssA during T6SS biogenesis will be reported elsewhere ( Zoued , Durand et al . , in preparation ) . The spatio-temporal recruitment of TssE and VgrG during the T6SS assembly pathway and their contributions to the structure of the T6SS baseplate are not yet known and will require further investigations . In contractile tailed bacteriophages , the baseplate serves as an assembly platform for the tube/sheath polymerization and triggers contraction of the sheath by transducing conformational changes from the fibers upon landing on host cells . It will be important to define the contribution of the T6SS baseplate to the control of T6SS sheath dynamics upon contact with prey cells .
Escherichia coli K-12 DH5α was used for cloning procedures , W3110 for co-immunoprecipitation , and BTH101 for the bacterial two-hybrid assay . The enteroaggregative E . coli strain 17–2 was used for this study . Strains were routinely grown in LB broth at 37°C , with aeration . For induction of the sci-1 T6SS gene cluster , cells were grown in Sci-1-inducing medium ( SIM: M9 minimal medium supplemented with glycerol ( 0 . 2% ) , vitamin B1 ( 1 μg/mL ) , casaminoacids ( 40 μg/mL ) , LB ( 10% v/v ) ) [49] . Plasmids and mutations were maintained by the addition of ampicillin ( 100 μg/ml for K-12 , 200 μg/ml for EAEC ) , kanamycin ( 50 μg/ml for K-12 , 50 μg/ml for chromosomal insertion on EAEC , 100 μg/ml for plasmid-bearing EAEC ) or chloramphenicol ( 40 μg/ml ) . L-arabinose was purchased from Sigma-Aldrich , anhydrotetracyclin ( AHT–used at 0 . 2 μg/ml throughout the study ) from IBA . The strains , plasmids and oligonucleotides used in this study are listed in S1 Table . Δtss deletion mutant strains were constructed using the modified one-step inactivation procedure [50] using red recombinase expressed from pKOBEG [51] as previously described [52] . The kanamycine cassette from pKD4 [50] was amplified with oligonucleotides carrying 50-nucleotide extensions homologous to regions adjacent to the target gene . The Polymerase Chain Reaction ( PCR ) product was column purified ( Promega PCR and Gel Clean up ) and electroporated . Kanamycin resistant clones were recovered and the insertion of the kanamycin cassette at the targeted site was verified by PCR . The kanamycin cassette was then excised using plasmid pCP20 [50] , and the final strain was verified by PCR . The Δsci-1 deletion strain , which comprises a deletion of the tssB-tssE fragment ( i . e . , all the T6SS genes ) , was constructed similarly . Chromosomal fluorescent reporter insertions were obtained by the same procedure using pKD4-gfp , pgfp-KD4 and pmCh-pKD4 as templates for PCR amplification . PCR were performed with a Biometra thermocycler , using the Pfu Turbo DNA polymerase ( Stratagene; La Jolla , CA ) . Custom oligonucleotides were synthesized by Eurogentec . Constructions of pOK-HcpHA and pUC-HcpFLAG and its derivatives have been previously described [18 , 52] . pTssF-HA has been constructed by insertion of EcoRI-XhoI PCR fragment into pMS600 digested by the same enzymes . All other plasmids have been constructed by restriction-free cloning [53]: the gene of interest was amplified with oligonucleotides carrying 5’ extensions annealing to the target vector . The product of the first PCR has then been used as oligonucleotides for a second PCR using the target vector as template . All constructs have been verified by DNA sequencing ( MWG ) . In vivo disulfide cross-linking assay was performed as previously described [18] . We used the adenylate cyclase-based two-hybrid technique using previously published protocols [40 , 54 , 55] . Briefly , pairs of proteins to be tested were fused to the two catalytic domains T18 and T25 of the Bordetella adenylate cyclase . After co-transformation of the BTH101 strain with the two plasmids producing the fusion proteins , plates were incubated at 30°C for 2 days . 600 μl of LB medium supplemented with ampicillin , kanamycin and 0 . 5 mM isopropyl—thio-galactoside ( IPTG ) were inoculated with independent colonies . Cells were grown at 30°C overnight and spotted on LB agar plates supplemented with ampicillin , kanamycin , IPTG ( 0 . 2 mM ) and 5-bromo-4-chloro-3-indolyl—D-galactopyrannoside ( X-Gal ) , the chromogenic substrate of the -galactosidase . 1011 exponentially growing cells producing the proteins of interest were harvested , and resuspended in Tris-HCl 20 mM ( pH8 . 0 ) , NaCl 100 mM supplemented with protease inhibitors ( Complete , Roche ) and broken by three passages at the French press ( 1000 psi ) . The total cell extract was ultracentrifuged for 45 min at 20 , 000 × g to discard unsolubilized material . Supernatants were then incubated overnight at 4°C with anti-FLAG M2 affinity beads ( Sigma Aldrich ) or with Protein G-Agarose beads ( Roche ) coupled to the anti-VSVG antibody . Beads were then washed three times with Tris-HCl 20 mM ( pH8 . 0 ) , NaCl 100 mM . The total extract and immunoprecipitated material were resuspended in Laemmli loading buffer prior to analyses by SDS-PAGE and immunoblotting . For reconstitution experiments , cell lysates were mixed for 30 min . at 28°C prior to immune precipitation . Hcp release assay , Fractionation , SLS differential solubilisation , discontinuous sedimentation sucrose gradients and NADH oxidase activity measurements were performed as previously described [13] . Overnight cultures of entero-aggregative E . coli 17–2 derivative strains were diluted 1:100 in SIM medium and grown for 6 hours to an OD600nm ~ 1 . 0 to maximize expression of the sci-1 T6SS gene cluster that is up-regulated in iron-depleted conditions [49] . Cells were washed in phosphate buffered saline ( PBS ) , resuspended in PBS to an OD600nm ~ 50 and spotted on a thin pad of 1 . 5% agarose in PBS and covered with a cover slip . Microscopy recordings and digital image processing have been performed as previously described [9 , 15 , 18 , 40] . For statistical analyses , fluorescent foci were automatically detected . First , noise and background were reduced using the ‘Subtract Background’ ( 20 pixels Rolling Ball ) plugin from Fiji [56] . The sfGFP foci were automatically detected by a simple image processing: ( 1 ) create a mask of cell surface and dilate ( 2 ) detect the individual cells using the “Analyse particle” plugin of Fiji ( 3 ) sfGFP foci were identified by the “Find Maxima” process in Fiji [56] . To avoid false positive , each event was manually controlled in the original raw data . Box-and-whisker representations of the number of foci per cell were made with R software . For sub-pixel resolution tracking , fluorescent foci were detected using a local and sub-pixel resolution maxima detection algorithm and tracked over time with a specifically-developed plug-in for ImageJ [56] . The x and y coordinates were obtained for each fluorescent focus on each frame . The mean square displacement was calculated as the distance of the foci from its location at t0 at each time using R software and plotted over time . For each strain tested , the mean square displacement of at least ten individual focus trajectories was calculated . Kymographs were obtained after background fluorescence substraction and sectioning using the Kymoreslicewide plug-in under Fiji [56] . The protein stability was assessed as previously described [57] . Exponential growing cells producing TssF , TssG or both TssF and TssG were treated with chloramphenicol ( 40 μg/ml ) and spectinomycin ( 200 μg/ml ) . Equivalent OD samples were harvested at 0 , 5 , 15 , 30 , 60 , 120 , 240 , 480 and 960 minutes after protein synthesis arrest , resuspended in loading buffer prior to analyzes by SDS-PAGE and Western blot immunodetection . Bacterial density ( OD600nm ) was measured throughout the experiment to verify that no growth occurred . Proteins suspended in loading buffer were subjected to SDS-PAGE . For detection by immunostaining , proteins were transferred onto nitrocellulose membranes , and immunoblots were probed with primary antibodies , and goat secondary antibodies coupled to alkaline phosphatase , and developed in alkaline buffer in presence of 5-bromo-4-chloro-3-indolylphosphate and nitroblue tetrazolium . Anti-TolA , -TolB , -Pal and-OmpA polyclonal antibodies are from our laboratory collection . Anti-FLAG ( Sigma-Aldrich ) , anti-VSV-G ( Sigma-Aldrich ) , anti-HA ( Roche ) , anti-EFTu ( Hycult Biotech ) and anti-rabbit , -mouse or-rat alkaline phosphatase-conjugated goat secondary antibodies ( Beckman Coulter ) have been purchased as indicated and used as recommended by the manufacturer .
|
In the environment , bacteria compete for privileged access to nutrients or to a particular niche . Bacteria have therefore evolved mechanisms to eliminate competitors . Among them , the Type VI secretion system ( T6SS ) is a contractile machine functionally comparable to a crossbow: an inner tube is wrapped by a contractile structure . Upon contraction of this outer sheath , the inner tube is propelled towards the target cell and delivers anti-bacterial effectors . The tubular structure assembles on a protein complex called the baseplate . Here we define the composition of the baseplate , demonstrating that it is composed of five subunits: TssE , TssF , TssG , TssK and VgrG . We further detail the role of the TssF and TssG proteins by defining their localizations and identifying their partners . We show that , in addition to TssE and VgrG that have been shown to share homologies with the bacteriophage gp25 and gp27-gp5 proteins , the TssF and TssG proteins also have homologies with bacteriophage components . Finally , we show that this baseplate is recruited to the TssJLM membrane complex prior to the assembly of the contractile tail structure . This study allows a better understanding of the early events of the assembly pathway of this molecular weapon .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
The Type VI Secretion TssEFGK-VgrG Phage-Like Baseplate Is Recruited to the TssJLM Membrane Complex via Multiple Contacts and Serves As Assembly Platform for Tail Tube/Sheath Polymerization
|
Stochastic simulations of coarse-grained protein models are used to investigate the propensity to form knots in early stages of protein folding . The study is carried out comparatively for two homologous carbamoyltransferases , a natively-knotted N-acetylornithine carbamoyltransferase ( AOTCase ) and an unknotted ornithine carbamoyltransferase ( OTCase ) . In addition , two different sets of pairwise amino acid interactions are considered: one promoting exclusively native interactions , and the other additionally including non-native quasi-chemical and electrostatic interactions . With the former model neither protein shows a propensity to form knots . With the additional non-native interactions , knotting propensity remains negligible for the natively-unknotted OTCase while for AOTCase it is much enhanced . Analysis of the trajectories suggests that the different entanglement of the two transcarbamylases follows from the tendency of the C-terminal to point away from ( for OTCase ) or approach and eventually thread ( for AOTCase ) other regions of partly-folded protein . The analysis of the OTCase/AOTCase pair clarifies that natively-knotted proteins can spontaneously knot during early folding stages and that non-native sequence-dependent interactions are important for promoting and disfavouring early knotting events .
A much debated problem in the thermodynamics and kinetics of protein folding [1] , [2] is the process of knot formation in proteins [3] , [4] , [5] , [6] , [7] , [8] . The topological entanglement found in naturally-occurring proteins presents several differences from that of compact or collapsed flexible polymers [9] . Firstly , the overall percentage of knotted native states in the protein data bank ( PDB ) is lower than for globular flexible chains with the same degree of polymerization [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . In addition , the free energy landscape of proteins , unlike that of homopolymers or random heteropolymers , is sufficiently smooth to ensure that the same knot in the same protein location is observed in folded structures [20] , [21] , [22] , [23] , [24] , [25] . These distinctive features are arguably the consequence of concomitant effects , including the propensity of polypeptides to form secondary structure elements , which enhance their local order compared to a typical collapsed polymer chain [9] , as well as functionally-oriented evolutionary mechanisms [26] , [27] , [28] . To gain insight into such mechanisms as well as to highlight general physico-chemical mechanisms favoring knot formation , an increasing number of experimental and numerical studies of knotted proteins have been carried out [9] , [20] , [21] , [25] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . Notably , the latest experimental results have clarified that native knots can form spontaneously and efficiently from an unknotted initial state . In particular , Yeates and coworkers [21] successfully designed a protein which can refold into the natively-knotted structure , albeit much more slowly than an unknotted counterpart . Finally , the recent study of Mallam and Jackson showed that newly translated , knot free , YibK molecules fold spontaneously to the native trefoil-knotted state , and that chaperones can significantly speed up the folding process [20] . Numerical simulations have been a valuable complement to experiments , particularly regarding the characterization of the pathways leading to the self-tying of knotted proteins . In particular , folding simulations based on simplified protein representations and/or force fields , have indicated two main mechanisms leading to knot formation: the threading of one of the termini through a loop [35] , or by slipping a pseudoknot through a loop [36] . The two mechanisms are not mutually exclusive , as reported by Sulkowska et al . [25] for protein MJ0366 from Methanocaldococcus jannaschii . Importantly , in coarse-grained ( -trace ) simulations where folding was promoted by exclusively favoring native contacts it was seen that the yield of folding trajectories was low and knots would form at late folding stages . Specifically , for the deeply-knotted protein YibK , only 1–2% of the trajectories reached the native state and the native knot was formed when about of the native contacts had already been established . Arguably , the coarse-grained nature of the models contributes to the observed low folding yield . However , even when employing atomistic representations , purely native-centric models tend to favor a rather late onset of knotting [37] . For example , a relatively late stage of knot formation was also estimated from the analysis of the free-energy landscape of protein MJ0366 , despite being half as long as YibK and with a shallower knot [25] . These findings are aptly complemented by the early observation of Wallin et al . [35] that the low yield of purely native-centric models can be dramatically enhanced by promoting the attraction of specific protein regions that are not in contact in the native state . In particular , it was seen that the formation of the native knot in YibK was particularly facilitated by introducing an ad hoc non-native attraction between a loop and the C-terminus that , by threading the former , produced a sizeable population of knots even at early folding stages , % [35] . These results , together with recent experimental ones [20] , [21] , [34] , pose the question of whether natively-knotted proteins can form non-trivial entanglements during early folding stages and whether non-native interactions can provide a general mechanisms for such self-tying events . Clarifying such aspects is important to advance the understanding of some of the general mechanisms aiding knot formation . It must however , be borne in mind that there could co-exist independent pathways where knots are established at different stages of the folding process and that active mechanisms , such as interactions with chaperones , could be involved in vivo [20] . In this study we address these questions by simulating the early folding process of two transcarbamylase proteins that are structurally very similar and yet their native states are differently knotted [26] , [27] . Specifically , one of them is a trefoil-knotted N-acetylornithine carbamoyltransferase ( AOTCase ) , while the other is an unknotted ornithine carbamoyltransferase ( OTCase ) , see Fig . 1 . Their evolutionary relatedness has posed the interesting question of understanding the source of their different native topology , with particular regards to the role of specific loop regions whose “virtual” excision or addition alters the native topology [27] . Here , for both the OTCase and AOTCase , more than a hundred folding simulations are carried out using a coarse-grained model and two different energy functions . We first evolve a fully extended configuration and steer it towards the native state by exclusively promoting native contact interactions . In a second set of simulations we enrich the energy function by adding quasi-chemical and electrostatic non-native interactions . In both cases , the stochastic evolution is followed up to the formation of about 35% of the native contacts and the knotted topology of the partially-folded structures is monitored . We find that knot occurrence is negligible for both the AOTCase and OTCase when the energy function favoring only native contacts is used . A dramatic difference in knotting propensity is instead seen when the quasi-chemical non-native interactions are added . In this case , the level of self-entanglement remains negligible for the OTCase but is greatly enhanced for the AOTCase , which is natively knotted . Notably , all proper and improper knots formed in these early folding stages have the correct ( native ) trefoil topology and chirality . The analysis of the repeated knotting/unknotting events observed in the simulated trajectories indicates that knotting usually results from the threading of the C-terminal through loops present in the loose protein globule . The threading events are favored by the effective ( non-native ) attraction of the C-terminus to other protein regions . Additional folding simulations carried out for “in silico mutants” of the AOTCases , provide further support for the effect of the C-terminus chemical composition on the knotting propensities .
For the purpose of our study , both proteins are described with a simplified structural model , where each amino acid is represented by one interaction center , coinciding with the atom . The folding dynamics is simulated using the stochastic Monte Carlo ( MC ) approach introduced and validated in Ref . [41] . The MC evolution entails a brief relaxation from the initial fully-extended state using both local and non-local moves . After this stage , indicated with a shaded region in Fig . 2 and related ones , the MC dynamics proceeds exclusively through local moves . The moves amplitude is sufficiently small that no chain crossings occurs , as verified a posteriori . The stochastic scheme is analogous to the kink-jump dynamics [42] which , by virtue of the local character of the moves , is known to provide a physically-viable description of biopolymers' kinetics in thermal equilibrium [41] , [43] . The folding simulations are carried out using two alternative energy functions . The first one promotes exclusively the formation of native contacts between pairs of amino acids . Following Refs . [44] , [45] , the strength of the attractive interaction between any given native amino acid pair is derived from the strength of their hydrogen bonding in the native state and is expressed in thermal units , , at the nominal Monte Carlo temperature of . The second energy function includes electrostatic and non-native pairwise interactions in addition to the native-centric potential . The relative strength of the non-native interactions is set according to the quasi-chemical potentials of Miyazawa and Jernigan ( MJ ) [46] that reflect the statistical propensity of amino acid pairs to be in contact in proteins' native states . Following , again , Ref . s [44] , [45] , the average strength of the added non-native interactions is set to one tenth of the native one . No change to the MC temperature was made using this second potential because previous studies have shown that the addition of non-native interactions modifies the effective temperature of the system by less than [47] . Consistently with this fact we have verified that the native states of both AOTCase and OTCase remain stable when evolved with the MC scheme using the second type of potential . In fact , the asymptotic value for average fraction of native contacts was % . The fact that this value is smaller than the asymptotic one , % , of the first type of potential indicates that the strength non-native interactions while weak enough to maintain stable the native state , can nevertheless compete with the native attractive interactions . The addition of the quasi-chemical interactions are therefore expected to be capable of accounting for sequence-specific non-native contact propensities in the partly-folded state . The sequence-dependent character of the non-native interaction differentiates the present approach from the early one of Wallin et al . [35] where the knot-promoting effect of non-native interactions was probed by systematically introducing attractive interactions between various pairs of segments of YibK . For each of the two proteins and for each of the two energy functions , we generated trajectories each consisting of MC moves per amino acid ( corresponding to 3000 units of MC time reported in Fig . 2 and Fig . 3 ) , for a total of 75 , 000 CPU hours . The acceptance rate of the local MC moves for both proteins was nearly constant throughout the simulation and approximately equal to . The entanglement of the structures sampled during the MC evolution was established using a combination of two unrelated knot detection schemes , which are described in the Materials and Methods . This choice was made to maximize the robustness of the criterion used to establish the knotted state of an open chain - which is mathematically properly defined only after the chain termini are joined by a segment or arc thus giving a circular chain ( chain closure operation ) . As described in the Materials and Methods section we used many alternative closures for each chain and adopted a majority rule to single out chains that have non trivial self-entanglements . These chains can correspond to proper , fully developed knots ( fully accommodated in the knotted structures ) as well as improper ones ( partly accommodated in the closing arcs ) . In order to illustrate how the folding process differs for the two potential energy functions , we show in Fig . 2 the evolution of the gyration radius ( upper panel ) and the fraction of native contacts ( lower panel ) of the knotted AOTCase , obtained by employing these two potentials . It is seen that the effect of the non-native and electrostatic interactions results in the higher compactness of the protein globule , since the gyration radius in this case lies systematically below the one calculated with the purely native-centric model . At any given stage of the MC evolution , the fraction of established native contacts is systematically lower for non-native interactions thus indicating that the latter introduce frustration that slows down the folding dynamics . Analogous results hold for the unknotted OTCase , see top panel of Fig . 3 . The data in Fig . 3 represent how the average radius of gyration and fraction of formed native contacts evolve in the course of the folding simulations of the two transcarbamylases . It is seen that , over the duration of the simulation , the average fraction of formed native contacts grows to on average ( the maximum value in all trajectories was . Overall , the probability of formation of native contacts decreases with the sequence separation of the amino acid pair , so that native -helical contacts are substantially more probable than inter-strand ones [48] . From the steady increase of the fraction of native contacts , , it is extrapolated that folding completion would occur on time-scales at least ten times larger than considered here . From the same figure it emerges that the radius of gyration gradually decreases to about 25 Å , which is larger than the native one , consistently with the less compact character of the partially folded states . To characterize the overall propensity of the two proteins to form knots while still largely unfolded , we extracted configurations at regular intervals of the simulations and analyzed their topological state . The knotting propensity of AOTCase , which is natively knotted , is illustrated in the upper panel of Fig . 4 which portrays the fraction of configurations that are properly or improperly knotted during a given time-window of the MC dynamics . It is seen that when the purely native-centric potential is used , the knotting propensity is always negligible . However , when non-native interactions are added , the number of knotted configurations increases to a definite fraction of the total: at the end of the simulations , when only of the native contacts are formed , the fraction of conformations that are properly or improperly knotted is about . Notice that this value is comparable with the yield of purely native-centric coarse-grained models where pathways with a late-stage formation of knots are typically observed [36] . Within the limitations of coarse-grained approaches , the results indicate that sequence-dependent non-native interactions can produce a detectable fraction of configurations with the correct native entanglement already at early folding stages . The analysis was repeated for the natively-unknotted OTCase and the results are shown in the lower panel of Fig . 4 . The contrast with the case of the ATOCase is striking . In fact , the knotting probability is negligible not only for the purely-native case , but remains so even when non-native interactions are introduced . The results indicate that the sequence-dependent non-native interactions – promoted by the quasi-chemical potential – increase dramatically the incidence of knots in the partially folded state of the AOTCase compared to the OTCase . Equivalently , the model calculations indicate that the two related natively-knotted and unknotted proteins have different knotting propensities already in the partially folded state , and that this difference can be ascribed to sequence-specific non-native interactions . To further elucidate the mechanisms responsible for these differences we monitored several parameters in the course of the simulations of the two transcarbamylases . More specifically , we searched for systematic differences in the interactions that the termini of these proteins establish with other parts of the peptide chain and for preferential locations of fully-developed knots . In this respect , it is important to point out that , in the course of the simulations for the AOTCase with non-native interactions , the process of knot formation is not irreversible . As illustrated in Fig . 5 , trefoil knots can be formed and untied during each simulation . In particular , fully-developed knots can persists for up to one tenth of the duration of our simulations . Notably , only knots with the correct ( i . e . native ) chirality are observed . By analysing the configurations preceding and following the knotting/unknotting events it is found that the change in topological state is typically caused by the threading of the helical C-terminus through loops formed by various protein regions . Among the independent knotting events we identified six for which the stochastic closure returned a non-trivial topology in at least of the cases or the portion accommodating the knot had a depth of at least 20 amino acids . The analysis of the loop threading events leading to such persistent fully-developed knots showed that they involved the C terminus interacting with amino acids 55–65 , 90–110 , or 125–155 . Only in one case it was observed the threading of the N-terminal through a loop involving segments 252–277 . Representative configurations are shown in the left panel of Fig . 6 where it is clear that the helical C-terminus of the AOTCase typically points towards the rest of the protein , and the stiffness of the helix facilitates the threading of various loop regions . At variance with the above situation , for both potential energy functions the OTCase C-terminus is typically exposed to the solvent and pointing away from the rest of the protein , as in the example shown in the right panel of Fig . 6 . The terminus also shows a lower propensity to form -helices . An analogous situation is found for the AOTCase in the presence of only native interactions . The effect is quantitatively illustrated in Fig . 7 , which portrays the average strength of the non-native interaction potential energy between the amino acids in the C-terminal -helix of the AOTCase or the OTCase and all the other residues in the chain . It is seen that the average non-native attraction is consistently stronger in the case of the knotted protein by about , which is a sizable amount considering that the average is taken over all sampled structures , irrespective of their compactness and knotted topology . The same figure also illustrates that , if the amino acids in the C-terminal -helix of protein AOTCase are replaced by neutral hydrophilic residues ( GLU , GLN , ASN ) , then the average non-native interaction energy becomes slightly repulsive , explaining the tendency of the -helix to point outwards . This provides an additional argument in support of the picture where the hydrophobicity character of the residues plays a role in the folding of knotted proteins [35] . Finally , in Fig . 8 we plot the average electrostatic potential energy between the -helix and the rest of the chain and show that , overall , the electrostatic interaction is about two orders of magnitude smaller than that of the quasi-chemical potential . To further clarify the role of the C-terminal -helix in the knotting of the partially-folded states of the AOTCase , we have carried out another set of simulations on a mutant protein . The last 25 residues of the OTCase ( which are involved in the C-terminal -helix ) were substituted with the same number of residues that form the C-terminal -helix in the knotted AOTCase . Apart from such a substitution , all other attributes , namely the length of the chain and the native contact map , were identical to the unknotted protein . The outcome of the simulation is summarized in Fig . 9 , from which we can conclude that the mutant protein forms a substantial fraction of knotted configurations in the presence of non-native interactions in its early stage of the folding . A simplified coarse-grained protein model was used for a comparative study of early folding stages of two evolutionarily related transcarbamylase proteins: an AOTCase ( PDB id 2g68 ) and an OTCase ( PDB id 1pvv ) . The two proteins are well-alignable in sequence and structure and yet possess differently knotted native states: AOTCase is trefoil-knotted while OTCase is unknotted . The role of sequence-dependent non-native interactions in promoting the correct native topology in the early folding stages was investigated by using two different energy functions in the simulations: one with a purely native-centric potential energy ( favoring only native contact interaction ) and one with the additional contribution of quasi-chemical and electrostatic non-native interactions . We found that in the absence of quasi-chemical interactions , neither protein shows an appreciable propensity to self-entangle in the early folding stages . However , once non-native interactions are introduced , the natively-knotted AOTCase does show a strongly enhanced propensity to form proper and improper knots with the correct native topology and chirality . By contrast , the knot enhancement effect is completely absent in the natively-unknotted OTCase . Inspection of the ensemble of folding trajectories of the two proteins suggest that knotting in the partly unfolded AOTCase mostly results from the approach of the hydrophobic C-terminal -helix to other regions of the protein which are eventually threaded through . Because of the non-compact character of the partly-unfolded structures , the -helix in the C-terminal can also retract from the threaded regions , so that various events of formation/disruption of proper and improper knots can be observed in a given trajectory . The influence of the C-terminal region on the different knotting propensity of the two carbamylases is further supported by the folding simulations for an “in silico mutant” of the OTCase obtained by replacing the C-terminal sequence with the one of the knotted AOTCase . In fact , the folding simulations based on the quasi-chemical non-native interactions yielded a portion of knotted structures similar to the natively-knotted AOTCase , from which the C terminus was taken . We recall that , the seminal study of Wallin et al . [35] , which was based on a coarse-grained model of YibK with ad hoc non-native interactions , had suggested the relevance of non-native interactions for steering the early formation of the native knotted topology ( by the threading of a loop by the C-terminal ) . Our results , which are consistent with these conclusions , provide additional elements to the picture by taking advantage of a coarse-grained framework where the folding of two carbamylases with similar structures , and yet different knotted topology are compared on equal footing . In particular our results indicate that non-native interaction propensities that are encoded in the primary sequence , can favor or disfavor the formation of knots already at early folding stages . The necessarily limited scope of the coarse-grained approach used here does not clarify whether additional pathways leading to a late-stage formation of knots can be present in transcarbamylases . It would be most interesting to address this standing issue in future studies within the present , or alternative comparative schemes .
We have adopted the coarse-grained model developed in Ref . s [44] , [45] , in which the effective degrees of freedom are the amino acid residues , represented by a spherical bead located at the position of the corresponding atom . The potential energy of the model consists of bonded and non-bonded terms . The bonded part of the of the potential consists of stretching potentials of the pseudo-bond - and pseudo-angle -- , as well as potential for pseudo-torsions ---: ( 1 ) All the values of the spring and energy constants appearing in the expressions of the bonded energy terms in Eqs . ( 2 ) , ( 3 ) and ( 4 ) can be found in Ref . [45] . The non-bonded part of the potential consists of three terms , comprising native , non-native and electrostatic interactions , respectively: ( 5 ) For pairs of residues that effectively repel each other , so that , the non-native potential energy function takes the following form: ( 8 ) The effective Lennard-Jones interaction strength between residues and in the coarse-grained model of Kim and Hummer [45] is defined as ( 9 ) The coefficients are negative and coincide with the entries of the MJ matrix [46] , while is an offset parameter . Hence , Eq . ( 8 ) defines a statistical knowledge-based potential which measures the preference of residue-residue interactions relative to residue-solvent interactions . The parameter scales the strength of the Lennard-Jones interaction compared to the physical electrostatic interactions . The two free parameters and are fitted in order to correctly reproduce the binding affinity of the broad set of experimentally well-characterized protein complexes [45] . As an illustration , the effective interaction strength between the neutral hydrophilic residues GLU and GLN is , so that effective interaction is repulsive , while for the hydrophobic residues ILE and LEU is , so that the effective interaction is attractive . The non-native interactions in the force field of our coarse-grained model introduce frustration and make the potential energy surface quite rugged . As a consequence , even within such a simple model , molecular dynamics ( MD ) simulations of the folding for chains of several hundreds amino acids are very expensive . Within the simulation time intervals which were accessible to our computer resources , such MD trajectories did not allow to monitor significant changes in the chain conformations . To cope with this problem , we have simulated MC dynamics , using an algorithm which combines different types of moves , namely: The moves were accepted or rejected according to the standard Metropolis criterion . MC algorithms based on local crankshaft and end moves are commonly employed in the polymer physics [41] to study dynamic properties , since it is was shown that they can mimic the intrinsic dynamics of a polymer in solution [50] at a much lower computational cost of MD simulations [52] . However , we emphasize that since our computational scheme is based on the MC evolution , we can not establish a quantitative mapping between the simulation time and physical time . We have generated 150 independent MC trajectories for each system , starting from the same stretched coil configuration . In the early stage of the folding , these types of moves were attempted with the constant probabilities of 0 . 8 , 0 . 1 and 0 . 1 , respectively . Such an algorithm generates a rapid collapse of the chain from a fully stretched configuration to one in which the gyration radius is reduced from about 70A˚ to about 30A˚ in about MC steps per particle , that corresponds to 100 units of MC time ( see Fig . 3 ) . During such collapse , no knot was observed . At the end of this stage , the acceptance rate of the global pivot moves typically dropped below . After this happened , we switched off the pivot moves , except from those involving few residues near the the two terminal of the chain . From this point on , the conformational changes of the chain are driven by the local crank-shaft and cartesian moves with an overall acceptance of about . When computing the fraction native contacts along the calculated MC trajectories , we adopt a criterion according to which two residues with index difference are said to be in contact if the distance of their is less than 7 . 5Å . The conformations visited during the Monte Carlo dynamics were topologically classified by computing the Alexander determinants after suitable closure into a ring [17] . For robustness , two alternative closure schemes are used: the minimally-interfering closure [53] and a modified version of the stochastic one [54] . The minimally-interfering closure is used first because it is very computationally effective , in that it entails a single , optimally chosen closure . In case of positive knot detection we further validate the non-trivial entanglement by performing 100 closures where each terminus is prolonged far out of the protein along a stochastically chosen direction , and the end of the prolonged segments are closed by an arc ( that does not intersect the protein ) . The stochastic exit directions are picked uniformly among those that are not back-turning . Specifically , they must form an angle of more than 90 with the oriented segment going from each terminus to the at a sequence distance of 10 . If the majority of the stochastic closures return non-trivial Alexander determinants , than the conformation is non-trivially entangled . Such conformations can correspond to both proper , fully developed knots , and improper ones . The two can be distinguished using knot localization criteria [53] , [55]: proper knots are entirely accommodated within the original protein chain , while improper ones span the exit segments . The knot type was determined using the scheme of Ref . [15] , which is based on the KNOTFIND algorithm .
|
Knotted proteins provide an ideal ground for examining how amino acid interactions ( which are local ) can favor their folding into a native state of non-trivial topology ( which is a global property ) . Some of the mechanisms that can aid knot formation are investigated here by comparing coarse-grained folding simulations of two enzymes that are structurally similar , and yet have natively knotted and unknotted states , respectively . In folding simulations that exclusively promote the formation of native contacts , neither protein forms knots . Strikingly , when sequence-dependent non-native interactions between amino acids are introduced , one observes knotting events but only for the natively-knotted protein . The results support the importance of non-native interactions in favoring or disfavoring knotting events in the early stages of folding .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"physics",
"protein",
"folding",
"biophysics",
"simulations",
"biophysics"
] |
2012
|
The Role of Non-Native Interactions in the Folding of Knotted Proteins
|
Spontaneous copy number variant ( CNV ) mutations are an important factor in genomic structural variation , genomic disorders , and cancer . A major class of CNVs , termed nonrecurrent CNVs , is thought to arise by nonhomologous DNA repair mechanisms due to the presence of short microhomologies , blunt ends , or short insertions at junctions of normal and de novo pathogenic CNVs , features recapitulated in experimental systems in which CNVs are induced by exogenous replication stress . To test whether the canonical nonhomologous end joining ( NHEJ ) pathway of double-strand break ( DSB ) repair is involved in the formation of this class of CNVs , chromosome integrity was monitored in NHEJ–deficient Xrcc4−/− mouse embryonic stem ( ES ) cells following treatment with low doses of aphidicolin , a DNA replicative polymerase inhibitor . Mouse ES cells exhibited replication stress-induced CNV formation in the same manner as human fibroblasts , including the existence of syntenic hotspot regions , such as in the Auts2 and Wwox loci . The frequency and location of spontaneous and aphidicolin-induced CNV formation were not altered by loss of Xrcc4 , as would be expected if canonical NHEJ were the predominant pathway of CNV formation . Moreover , de novo CNV junctions displayed a typical pattern of microhomology and blunt end use that did not change in the absence of Xrcc4 . A number of complex CNVs were detected in both wild-type and Xrcc4−/− cells , including an example of a catastrophic , chromothripsis event . These results establish that nonrecurrent CNVs can be , and frequently are , formed by mechanisms other than Xrcc4-dependent NHEJ .
The importance of genomic copy number variants ( CNVs ) , defined as submicroscopic deletions or duplications ranging in size from 50 bp to over a megabase [1] , has become better understood in recent years . Normal polymorphic CNVs are a major contributor to human genomic variation and phenotypic diversity [2] , [3] , [4] , [5] , [6] , while spontaneous CNVs are a very important and frequent cause of genetic and developmental disorders , including intellectual disability , neuropsychiatric disorders , and structural birth defects [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . Their frequency further suggests a high de novo mutation rate , with estimates between 0 . 01 and 0 . 05 per meiosis [6] , [15] , [16] , [17] . In addition , CNVs are likely to be but one manifestation of the same mutagenic forces that create many classes of chromosomal structural variants , including copy-number neutral inversions and translocations [18] , [19] , [20] . While there is growing appreciation for their importance , less is understood about how many CNVs are formed . Recurrent CNVs arise during meiosis by nonallelic homologous recombination ( NAHR ) in regions flanked by large segmental duplications [21] . In contrast , nonrecurrent CNVs are distributed throughout the genome in regions lacking such homologous sequences . These CNVs have breakpoint junctions that are characterized by blunt ends , microhomologies , and small insertions , suggesting the involvement of a nonhomologous repair mechanism in their formation [22] , [23] , [24] , [25] . A number of different DNA repair mechanisms have been suggested to account for nonhomologous junctions , principally nonhomologous end-joining ( NHEJ ) , alternative end-joining ( alt-EJ ) , and forms of replication template switching [26] . Canonical NHEJ , along with homologous recombination ( HR ) , is one of the two major mechanisms used to repair DNA double-strand breaks ( DSBs ) in eukaryotic cells . NHEJ directly joins two DSB ends without using extensive sequence homology to guide repair through the action of a well-defined set of proteins , including the Xrcc4-ligase IV complex , which is dedicated to and essential for this pathway [27] . The junctions formed are typically characterized by blunt ends or short microhomologies and can include insertions of a few nucleotides [26] , [28] . NHEJ can ligate distant DSBs to form deletions [29] . Consistently , NHEJ has been implicated in the formation of deletion CNVs [22] , [25] , [30] , [31] , [32] . In a two-step mechanism combined with HR , NHEJ has also been suggested to be involved in the formation of duplications [23] , [30] , [33] . Xrcc4-ligase IV-independent forms of DSB end joining also exist , variably called alt-EJ or microhomology-mediated end joining . Alt-EJ is ordinarily less efficient than and/or suppressed by NHEJ such that its activity is often revealed principally in the absence of NHEJ proteins . For example , in the absence of Xrcc4-ligase IV , alt-EJ becomes important in class-switch recombination [34] and executes an increased frequency of translocations in a two-DSB model system [35] . The alt-EJ mechanism ( s ) are much less well defined than NHEJ , but repair events are typically characterized by longer stretches of microhomology at junctions , thought to arise mainly through annealing of single strands exposed by DSB resection [28] , [36] , [37] . Accordingly , alt-EJ is strongly mutagenic . In contrast to end joining mechanisms , which obligatorily proceed through DSB intermediates and could occur throughout the cell cycle , mechanisms based on replication template switching have also been proposed to explain the presence of microhomologies at CNV junctions . Lee et al . [23] proposed the Fork Stalling and Template Switching ( FoSTeS ) model in which replicating DNA strands switch between forks . A revision of this model , termed microhomology-mediated break-induced replication ( MMBIR ) [37] , invokes one single-ended DSB intermediate at a collapsed replication fork at which a liberated DNA strand makes the template switch into a distant genomic site . These models are supported by complex CNVs in humans and mice that can be explained by multiple template switching events [19] , [38] , [39] , [40] , as well as by deletions and duplications occurring independently of breakage fusion bridge cycles near fused telomeres in C . elegans [41] . However , as with alt-EJ , mammalian proteins involved in this process are not well defined . We previously reported an experimental approach for inducing de novo CNVs that closely mimic the nonrecurrent class of human CNVs [42] , [43] , [44] . In this approach , mild replication stress resulting from low doses of the replication inhibitors aphidicolin and hydroxyurea potently induces formation of de novo CNVs that resemble nonrecurrent CNVs in vivo in size and structure . In particular , both human nonrecurrent CNVs and those induced in our experimental system have breakpoint junctions primarily characterized by microhomologies , blunt ends , or small insertions . These observations led to the hypothesis that NHEJ participates in the formation of nonrecurrent CNVs , although their induction by replication stress and the occurrence of complex events also raised the possibility of template switching mechanisms [23] , [37] , [42] , [43] . For both experimentally-induced CNVs and those occurring in humans in vivo , the proposed mechanisms have only been inferred from junction sequences; direct experimental tests have been lacking . Because of the strong functional implications of the potential alternative mechanisms of nonrecurrent CNV formation , we sought to definitively explore the role of the well-defined , canonical NHEJ pathway . We report studies of CNV formation using Xrcc4−/− mouse embryonic stem ( ES ) cells and the DNA polymerase inhibitor aphidicolin ( APH ) . Xrcc4 is an essential component of DNA ligase IV that is absolutely required for NHEJ [27] . We demonstrate that APH induces de novo CNVs in mouse ES cells as it does in human fibroblasts , but that there is no difference in the frequency or structure of spontaneous or induced CNVs between wild-type and Xrcc4−/− cells . CNV breakpoint junctions were characterized by blunt ends and microhomologies regardless of genotype , with no observed shift in microhomology lengths . We conclude that replication-associated CNVs in mouse ES cells are created through mechanism ( s ) other than canonical NHEJ and discuss the potential roles of alt-EJ and template switching in the context of both simple and complex CNVs observed in the presence and absence of Xrcc4 .
To document the validity of our cell model , PCR was used to demonstrate the presence of a homozygous inactivating Xrcc4 deletion mutation in the Xrcc4−/− mouse ES cells used in these studies . Supporting this , these cells also demonstrated a large decrease in survival after exposure to ionizing radiation ( Figure S1 ) , consistent with NHEJ deficiency . Prior to the experiments , parental cells of each genotype were expanded from a single clone to minimize the number of potentially mosaic CNVs in the starting cell population . To induce replication stress , wild-type and Xrcc4−/− mouse ES cells were cultured in the presence of 0–0 . 6 µM APH for 72 hours prior to plating for isolation of clonal cell populations . This mild dose of APH does not block the cell cycle , but instead allows replication to proceed at a reduced rate . Individual clones were expanded and subjected to CNV analysis using Nimblegen 3x720K aCGH arrays ( Figure S2 ) . De novo CNVs were defined as a segmental gain or loss detected in a clone when using the parental cell population as a reference . A total of 85 independent clones from untreated or APH-treated wild-type and Xrcc4−/− cells were analyzed in three independent experiments . In wild-type cells , de novo CNVs were found in untreated and APH-treated clones at a frequency of 0 . 43 and 5 . 19 CNVs per clone , respectively ( p<10−14 ) ( Figure 1A ) , demonstrating that , just as in previous studies with human fibroblasts , de novo CNVs in mouse ES cells can arise spontaneously during culture but that their frequency is significantly increased following replication stress . In Xrcc4−/− cells , de novo CNVs were identified in untreated and APH-treated clones at a frequency of 1 . 31 and 7 . 34 de novo CNVs per clone , respectively ( p<10−14 ) ( Figure 1A ) . When all experiments are considered together as in Figure 1A , there appears to be a slight increase in CNV induction in Xrcc4−/− cells compared to wild-type cells . However , this effect was only seen in one experiment in which APH-treated , wild-type cells had an unusually low CNV frequency . It was not recapitulated in the two subsequent experiments ( Figure S3 ) . Thus , there was no consistent effect of Xrcc4 deficiency on the frequency of spontaneous or APH-induced de novo CNV formation in ES cells . Most importantly , Xrcc4 deficiency did not reduce the frequency of CNVs , as might be expected if NHEJ were the predominant pathway of CNV formation . Because NHEJ deficiency might affect CNV structure independently of frequency , we compared numerous features of de novo CNVs in wild-type and Xrcc4−/− cells . While CNVs consisted of a mix of both deletions and duplications , there was a clear overrepresentation of deletions in both wild-type and Xrcc4−/− cells . 130 of 143 ( 90 . 9% ) CNVs from wild-type cells and 195/234 ( 83 . 3% ) CNVs from Xrcc4−/− cells were of the deletion type . The abundance of deletions compared to duplications is consistent with results seen in normal human fibroblasts after replication stress , in which 65–82% of CNVs were deletions [42] , [43] , and in humans in vivo [6] . There was no difference in overall de novo CNV size between wild-type and Xrcc4−/− cells ( Figure 1B ) . De novo CNVs in wild-type cells were generally large , with a median size of 59 kb ( 11 . 6 kb to 1 . 4 Mb ) . These sizes are similar to de novo CNVs seen in Xrcc4−/− cells , which had a median size of 63 kb ( 7 . 7 kb to 26 . 2 Mb ) . We did note that CNVs arising in mouse ES cells ( median = 62 kb ) were 2 . 2-fold smaller than de novo CNVs seen in similar experiments with human fibroblasts ( median = 138 kb ) [42] ( Figure 1C ) . Consistent with previous observations in human fibroblasts , spontaneous and APH-induced CNVs in both wild-type and Xrcc4−/− mouse ES cells were distributed throughout the genome , with most arising in distinct , nonoverlapping regions ( Figure 2 ) . Superimposed on this distribution pattern were hotspots containing five or more different , overlapping CNVs , a number identified as unexpected by simulation modeling ( see Materials and Methods ) . Each CNV within these hotspots had unique boundaries , indicating that each one arose independently , supporting the hypothesis that these regions are especially sensitive to replication stress . A difference in the size distribution of CNVs at hotspots and non-hotspots was observed , with hotspot CNVs being on average 1 . 9-fold larger than non-hotspot CNVs ( median sizes of 89 . 8 kb and 46 . 3 kb , respectively ) . In addition , the abundance of deletions over duplications was more pronounced at hotspots than at non-hotspots . At non-hotspots , 79 . 5% ( 194/244 ) of de novo CNVs were of the deletion type , whereas at hotspots , almost all de novo CNVs ( 98 . 5%; 131/133 ) were deletions ( p<0 . 0001 ) . Most importantly , there was no apparent difference in the spatial distribution of CNVs between wild-type and Xrcc4−/− cells , including that hotspots accounted for 35 . 0% ( 50/143 ) and 35 . 5% ( 83/234 ) of all de novo CNVs , respectively . Notably , several ES cell hotspots were found in the syntenic regions corresponding to hotspots seen in human fibroblasts ( Table 1 , Figure 3 , Figure S4 ) . The mouse hotspot with the most frequent occurrence of CNVs was in the Auts2 gene at chromosome 5G2 . One or more CNVs in Auts2 were seen in 28/55 APH-treated clones , and accounted for 8 . 5% of all de novo CNVs . In addition , a hotspot was seen in the Wwox gene at 8E1 , with CNVs found in 12/55 APH-treated clones . Both of these hotspots corresponded to hotspots seen in human fibroblasts ( Figure 3 , Table 1 ) . However , the most frequently observed hotspot in human fibroblasts , at 3q13 . 31 , was not a hotspot in mouse ES cells . In fact , most ( 11/13 ) of the hotspots in mouse ES cells were not observed in human fibroblasts ( Table 1 ) . In addition , two hotspots corresponded to the common fragile sites Fra8E1 ( FRA16D ) and Fra14A2 ( FRA3B ) , while others mapped to regions syntenic to human fragile sites . These results suggest that while there is some conservation in replication stress-induced CNV hotspots , differences are also seen due to cell type or species variation . In the absence of canonical NHEJ , the pattern of breakpoint junction sequences provides the most precise structural signature for revealing altered utilization of different end joining repair mechanisms [28] , [36] , [37] . To examine this , we sequenced 24 CNV breakpoint junctions from Xrcc4−/− cells and 17 from wild-type cells ( Table 2 ) ( Figure 4 ) . All of the junctions from both wild-type and Xrcc4−/− cells were characterized by 0–5 bp of homology , while two junctions in each cell type also had small insertions of 1–3 bp . The mean length of microhomology in CNVs from wild-type and Xrcc4−/− cells was 2 . 0 bp and 2 . 1 bp , respectively , and the median length for both was 2 . 0 bp . The lack of a shift toward longer microhomologies in the absence of Xrcc4 strongly argues against a shift from utilization of canonical NHEJ toward alt-EJ in Xrcc4-deficient cells , and therefore that these junctions were not formed by Xrcc4-dependent DSB repair , even in wild-type cells . Similarly , none of the sequenced junctions had long stretches of homology that would suggest a shift toward HR in NHEJ-deficient cells . To explore this further , we examined the breakpoint regions of unsequenced deletions in silico to determine if Xrcc4−/− cells had an increased breakpoint frequency near segmental duplications that might suggest formation by HR . For each CNV , 10 kb windows of sequence from the left and right breakpoint regions were compared to each other , scoring instances of sequence identity >90% along a stretch of sequence at least 1000 bp . Such large sequence homologies were associated with only 3 . 5% and 4 . 0% of CNVs in wild-type and Xrcc4−/− cells , respectively ( p = 1 . 0 ) , reinforcing that there is no apparent increase in sequence homology at breakpoint regions in Xrcc4-deficient cells . Thirteen of the sequenced breakpoint junctions were from five complex CNVs that contained two to four breakpoint junctions each . These CNVs recapitulate the type of complex events seen in human fibroblasts [42] , [43] and in vivo [19] , [38] , [39] , [40] . Two of these complex CNVs were found in wild-type cells and three were from Xrcc4−/− clones , again suggesting no Xrcc4-dependent structural difference . These complex CNVs were initially scored as simple deletions based on aCGH data , but sequencing revealed the presence of small retained sequences , as well as duplications and inversions that were below the resolution limit of the array ( Figure 5A , Figure S5 ) . In addition , Xrcc4−/− clone X6-40 contained a 2 . 5 Mb region of chromosome XE3 containing at least 10 discrete deletions ( Figure 5B ) . This CNV is similar to the recently-described chromothripsis class of structural alterations [45] . Finally , we note that we successfully sequenced CNV breakpoint junctions in only 41 out of 60 attempts ( 68% ) . The CNVs for which breakpoint cloning failed likely include some junctions with complex structures that are difficult to amplify . Accordingly , we expect that our six complex CNVs are an underrepresentation of the actual incidence of such events .
The experiments reported here demonstrate that APH-induced replication stress creates de novo CNVs in mouse ES cells that mimic in vivo nonrecurrent CNVs in the same manner as in human fibroblasts , and that these and spontaneous CNVs arise independently of Xrcc4-dependent NHEJ . Neither the frequency nor any observable feature of location or structure of APH-induced CNVs was affected by Xrcc4 loss . Almost all de novo CNVs in both wild-type and Xrcc4−/− cells had breakpoint regions devoid of the extended sequence homology needed to drive HR . Detailed characterization of individual breakpoint junctions confirmed that the CNVs arose via a non-homologous mechanism characterized by blunt ends , short microhomologies or short insertions , regardless of Xrcc4 status . These results eliminate canonical NHEJ as a primary mechanism for de novo CNV formation in our cell system . Moreover , the identification of complex , chromothripsis-like events in Xrcc4−/− cells suggest this rearrangement can occur in the absence of the NHEJ pathway . Instead , the findings together implicate alt-NHEJ and/or replication template switching as the principal mediator ( s ) of nonhomologous junction formation . In many ways , the results of this CNV study are similar to observations made using a two-DSB translocation model system [35] , [46] , [47] . Jasin and colleagues have shown that alt-EJ rather than canonical NHEJ likely acts in the formation of translocations following DSB induction , even when a functional NHEJ pathway is present . Similar to results here , they found that loss of Xrcc4 does not change the nature of translocation breakpoint junctions , which , like those seen at APH-induced CNVs , are typically characterized by 0–4 bp of microhomology . In addition , translocation junctions were sometimes complex , containing multiple insertions that were duplicated from sequences that could be as much as 4 Mb away from the initiating DSB , suggesting that iterative DNA synthesis occurred prior to joint resolution . These similar results could indicate that alt-EJ is playing a role in the CNVs induced in our system . However , a key difference is that loss of Xrcc4 and NHEJ increased DSB-induced translocations 5-fold [35] , whereas loss of Xrcc4 did not significantly alter the frequency of CNV induction . This lack of CNV suppression might suggest that the precursor lesion for CNVs is distinct from the translocation model in that it can be processed by alt-EJ but not NHEJ . A powerful way to rationalize this would be creation of CNVs by joining of two single-ended DSBs formed at different collapsed replication forks ( Figure 6 ) . Individually , such replication-dependent DSBs are not substrates for local NHEJ and might , by analogy to the translocation model , be processed primarily by alt-EJ when joined at a distance . Alternatively , the lack of CNV suppression by NHEJ might suggest that DSB end joining is not an important contributor to CNV formation . Replication template switching , including FoSTeS and MMBIR [23] , [37] , are strong alternative models that are entirely consistent with all results here , including the lack of dependence of both CNV structure and frequency on Xrcc4 ( Figure 6 ) . Importantly , alt-EJ and template switching models of CNV formation are not mutually exclusive , and indeed might be considered more similar than different ( Figure 6 ) . The demonstration that CNV formation increases after replication stress in mouse as well as human cells strongly supports both replication-dependent alt-EJ and template switching mechanisms [42] , , since the partial inhibition of replication fork progression leads to increased frequencies of fork stalling and collapse to single-ended DSBs . Moreover , both replication-dependent alt-EJ and MMBIR invoke a processed single-ended DSB intermediate that could execute the sometimes multiple iterative template copying events that underlie the species- and cell type-independent occurrence of complex CNVs . What distinguishes replication-dependent alt-EJ and template switching is simply the mode of final joint resolution , which for alt-EJ is ligation to a second DSB end but for template switching is stabilization of the ultimate template copying event into a mature replication fork ( Figure 6 ) . Evidence that the latter event occurs is the induction of tandem duplication CNVs by replication stress , since duplications are easily explained by a template switch upstream of the DSB end . In total , though , both alt-EJ and maturation of template copying events might be used to resolve one-ended replication DSBs in different CNV events . The results from this study also relate to the nature of chromothripsis events and CNV hotspots . Chromothripsis is a recently-described , catastrophic chromosome rearrangement seen in 2–3% of cancers [45] . It is also seen as a constitutional event in humans , suggesting that it is not specific to aberrant DNA repair pathways seen in cancer [48] , [49] . These complex rearrangements are thought to occur as a single catastrophic event , rather than accumulating over time [50] , [51] . The detection of a chromothripsis-like event in a Xrcc4−/− clone suggests that these catastrophic rearrangements can occur via an NHEJ-independent pathway , in contrast to chromosome shattering followed by religation via NHEJ [50] . Such catastrophic events can be explained under a unifying template switching model of CNV formation , in which the same basic replication stalling mechanism can give rise to simple as well as highly complex CNVs . As with human cells , de novo CNVs in mouse cells are distributed across the genome , but include hotspots . Only some of these hotspots correspond to the syntenic regions of hotspots in human fibroblasts , indicating that hotspot conservation can vary between cell types and perhaps species . As seen in human fibroblasts , some CNV hotspots in mouse ES cells correspond with molecularly-characterized common fragile sites or in cytogenetic bands that contain fragile sites [42] , [52] , [53] . This correlation is not perfect , and several hotspots do not correspond to any known fragile site region . However , fragile sites have largely been characterized in primary lymphocytes and lymphoblastoid cell lines , and it is known that different cell types can have altered fragile site expression [53] . It is therefore possible that additional hotspots seen in mouse ES cells and human fibroblasts correspond to fragile sites that are preferentially expressed in those cells . The observation that hotspot CNVs are almost all deletions and tend to be almost twice as large as non-hotspots , coupled with the possible fragile site connection , raises interesting possibilities for their formation . APH and hydroxyurea are known to activate the firing of dormant replication origins in Xenopus extracts and mammalian cells [54] , [55] . It has been shown that common fragile sites can occur in regions with a paucity of activated origins after replication stress , resulting in delayed and incomplete replication [56] , [57] , [58] . The lower active origin density results in a larger mean distance between active replication forks in these regions . If a replication-based mechanism involving either alt-EJ or template-switching between forks is responsible for CNVs , the greater fork spacing in regions with low active origin density would result in a larger CNV size , and large unreplicated regions that persist beyond S-phase could favor the formation of deletions over duplications , as observed in our experiments . While origin paucity is characteristic of fragile sites , incomplete activation of primary or dormant origins could also play a role in CNV formation at non-hotspots . In summary , the experiments described here demonstrate that canonical , Xrcc4-dependent NHEJ is not involved in CNV formation in somatic cells cultured in vitro . Evidence from breakpoint junction structures further demonstrates that the CNVs did not form via HR . Instead , the data implicate a replication-dependent alt-EJ and/or template switching mechanism . Because of the strong similarity of the observed CNVs to the major classes of nonrecurrent normal and pathogenic CNVs seen in humans , we argue that these conclusions are generalizable to most de novo , nonrecurrent CNV formation in both germline and somatic human cell lineages , with the simple difference that event rates are higher in our model system because replication is exogenously stressed . Although not mutually exclusive , important features distinguish the remaining alt-EJ and template switching mechanisms , specifically the manner in which the strands are stably resolved . Also enigmatic is precisely which DNA intermediate is the substrate for template switching and which proteins are involved in executing the transfer when little or no microhomology is present . Major efforts moving forward should thus be to delineate the precise strand intermediates and protein mechanisms involved in mediating nonrecurrent CNV formation .
All experiments were performed with two isogenic male mouse ES cell lines . The first ( TC1 ) was wild-type , while the second ( Xrcc4−/− ) was homozygous for a targeted inactivation of Xrcc4 [59] . Genomic DNA was prepared from cells using the Blood & Cell Culture DNA Mini Kit ( Qiagen ) . ES Cells were grown irradiated fibroblast feeder cells in DMEM media supplemented with 15% FBS , 20 mM HEPES , and 1 mM sodium pyruvate . To create replication stress-induced CNVs , cells were treated with 0 . 6 µM APH . In three independent experiments , cells were treated for 72 hours followed by a 24 hour recovery period before plating at low density for single-cell clones . Cells were plated at a density of 100–500 cells per 100-mm culture dish and individual clones isolated with a pipette tip after 7–10 days . CNVs were detected using Nimblegen whole genome arrays containing 720 , 000 ( 720K ) unique sequence oligonucleotides ( Roche Applied Science ) . Arrays were prepared according to the manufacturer's protocol . Arrays were scanned on an Axon 4000B scanner ( Molecular Devices ) with GenePix software at 532 and 635 wavelengths . Data extraction , normalization , and visualization were achieved by using manufacturer-provided software ( NimbleScan and Signal-Map ) . Arrays were analyzed for copy number differences using SegMNT , part of Nimblegen's NimbleScan software package , as well as our software platform , VAMP , as previously described [19] . All clones were analyzed using the appropriate mixed parental cell population as the normalization reference . This approach routinely detects CNVs larger than 20 kb and can detect CNVs as small as a ∼1 kb , depending on probe placement . CNV breakpoint junctions were amplified using the Expand Long Template PCR System ( Roche Applied Science ) . For deletions , PCR primer pairs were generated that flanked deletion breakpoints , whereas for duplications , primers were designed within the duplicated region , directed outward , as described previously [43] . PCR amplification generated a product that spanned the breakpoint junction . All products were then subjected to standard Sanger sequencing . The resulting sequence was compared to the reference genome ( build mm9 ) to identify the breakpoint junctions . CNVs in our model system are relatively rare events and therefore the numbers of CNVs per clone are expected to fit a Poisson distribution determined by the mean frequency of CNVs in all clones . Therefore , p values of treated vs . untreated samples were determined using the one-sided E-test of Krishnamoorthy and Thomson for comparing two Poisson mean rates [60] . To determine whether the observed clustering of CNVs within genome regions was non-random , we performed the Monte Carlo simulation summarized in Table S1 , as previously described for human cells [42] . A simulation of 10 , 000 iterations was performed on the combined wild-type and Xrcc4−/− CNV sets . Regions with 5 or more overlapping CNVs were very rarely observed by random placement ( p<0 . 01 , Table S1 ) and were therefore scored as CNV hotspots in mouse ES cells . These hotspot regions are highlighted by shading in Table S2 .
|
Copy number variants ( CNVs ) are a major factor in genetic variation and are a common and important class of mutation in genomic disorders , yet there is limited understanding of how many CNVs arise and the risk factors involved . One DNA damage response pathway implicated in CNV formation is nonhomologous end joining ( NHEJ ) , which repairs broken DNA ends by Xrcc4-dependent direct ligation . We examined the effects of loss of Xrcc4 and NHEJ on CNV formation following replication stress in mouse cells . Cells lacking NHEJ displayed unaltered CNV frequencies , locations , and breakpoint structures compared to normal cells . These results establish that CNV mutations in a cell model system , and likely in vivo , arise by a mutagenic mechanism other than canonical NHEJ , a pattern similar to that reported for model translocation events . Potential roles of alternative end joining and template switching are discussed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mutagenesis",
"genetic",
"mutation",
"chromosome",
"biology",
"chromosome",
"structure",
"and",
"function",
"genetics",
"biology",
"genomics",
"genetics",
"and",
"genomics",
"mutational",
"hypotheses"
] |
2012
|
De Novo CNV Formation in Mouse Embryonic Stem Cells Occurs in the Absence of Xrcc4-Dependent Nonhomologous End Joining
|
High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution . However , there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data . The challenges arise , at least in part , from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data . Here , we present an algorithm , termed Bycom , a new Bayesian model that can perform methylcytosine calling with high accuracy . Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy . Bycom performance was compared with the performance of Lister , the method most widely used to identify methylcytosines from bisulfite sequencing data . The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels . Bycom also showed higher sensitivity and specificity for low methylation level samples ( <1% ) than Lister . A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94% . This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels . Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines .
DNA methylation is an important epigenetic modification involved in the regulation of gene expression and plays critical roles in cellular processes [1]–[5] . Abnormalities in DNA methylation contribute to the dysregulation of gene expression and have been reported to be associated with tumorigenesis [6] and imprinting disorders [7] . DNA methylation occurs on the cytosine residues in DNA and the accurate identification of methylated cytosines ( methylcytosines ) is essential for studying variance in methylation [8] . Advances in high-throughput bisulfite sequencing ( BS-seq ) [9]–[11] such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing ( RRBS ) , provide comprehensive and well-fitted ways to identify methylcytosines at single-base resolution . However , the large data sets generated by BS-seq pose data processing challenges for methylcytosine calling . Typically , the first step of methylation analysis with BS-seq data is to map the bisulfite-converted reads to a reference genome using software such as SOAP and BSMAP [12]–[14] . Methylcytosines can then be identified from the reads aligned to the cytosines on the reference genome . However , besides sequencing errors , methylcytosine calling is affected by incomplete bisulfite conversion , which corresponds to the ratio of unmethylated cytosines that were not converted to thymines by the bisulfite treatment . Additionally , cell heterozygosis caused by multicellular sequencing can also influence the precision of methylcytosine detection because the methylation status of the same cytosine site in different cell is probably inconsistent owing to the coexistence of methylation and demethylation [15]–[18] . As a consequence of the factors mentioned above , the false-positive rate for methylcytosines detected by simple filtering using a predefine threshold that the methylation level should be above zero , has been reported to be extremely high [19]–[21] . In recent years , the methylation ascertainment method applied by Lister et al . [9] , which is widely used in the methylation analysis software such as Bismark [22] and Bisulfighter [23] , has been employed to determine the methylcytosines from BS-seq data [5] , [24] and has been shown to have a much lower false positive rate than the simple filtering method [25] , [26] . We termed the method as “Lister” hereafter . Although Lister uses binomial distribution to overcome the impacts of false-positive rate ( sum of non-conversion rate and sequencing errors ) [24] , it does not consider the impact of methylation heterozygosis caused by cell heterozygosis . Besides , as amounts of cytosines , whose methylation level is low , are difficult to be distinguished from unmethylated sites , the methylation status determined by the binomial test is not reliable for samples with extremely low genome-wide methylation levels [5] . Here , we present a novel algorithm , Bycom that can take into account sequencing errors , non-conversion rate , and cell heterozygosis which is initially introduced as the factors to identify precisely the methylcytosines from BS-seq data . Bycom is based on the Bayesian inference model , which is not limited to certain data distribution and has been used successfully for single-nucleotide polymorphism ( SNP ) calling in next-generation sequencing data [27] . Bayesian inference model has also been successfully applied to methylated DNA immunoprecipitation ( MeDIP ) -based studies [28] . We evaluated the performance of Bycom on simulated BS-seq data , publicly available BS-Seq data , and RRBS data and demonstrated that Bycom can identify methylcytosines more precisely than Lister . This methodology of Bycom has been packaged and available at https://sourceforge . net/projects/bycom , which is written in PERL and designed for Linux 64 platform .
The performance of Bycom was evaluated and compared with Lister based on simulated data that were generated from chromosome 10 of the human reference genome sequence ( hg18 , UCSC ) using the bisulfite sequencing simulator for next-generation sequencing BSSim ( http://122 . 228 . 158 . 106/BSSim ) . Six factors that may affect the identification of methylcytosines were considered in the simulation process , namely , total methylation level of the sequenced sample , sequencing depth , bisulfite conversion rate , high quality value ratio , mismatch number , and SNP frequency . With all parameters set to their default values , we found that Bycom had a higher overall accuracy than Lister as shown by the receiver operator characteristic ( ROC ) curves ( Figure 2A ) . Next , we investigated the impact of each single factor on the performance of Bycom . For the total methylation level , we assumed that the number of methylcytosines in the simulated data increased steadily as the total methylation level was increased . When the methylation levels were extremely low ( 0 . 2% , 0 . 4% , 0 . 6% , and 0 . 8% ) , the number of methylcytosines called rose slightly and the false negative rate remained stable at 2% for Bycom and at 15% for Lister . At low methylation levels ( 2% , 4% , 6% , and 8% ) , the number of methylcytosines called by the two methods increased and the false negative rate for Bycom decreased slightly to 0 . 3% compared with 14% for Lister . At the high methylation levels ( 20% , 40% , 60% , and 80% ) , the number of true positive sites increased , while the false negative rates for both Bycom and Lister remained very low ( Figure 2B ) . For the impact of sequencing depth , the total number of methylcytosines called by Bycom increased when the read depth incrementally changed from 5× to 25× . The highest number of methylcytosines called was when the depth was 25×; thereafter , the number of calls remained relatively constant for depths >25× ( Figure 2C ) . In contrast , the total number of methylcytosines called by Lister increased when the read depth went up from 5× to 55× after which the number of calls steadily decreased . The false negative rates for both Bycom and Lister reduced when the higher read depths were introduced and , in all cases , the false positive rates for Lister were higher than the false positive rates for Bycom . The bisulfite conversion rate can affect the methylation level of the cytosines in a genome sequence; therefore , we evaluated the performance of Bycom using different values for this parameter . The number of methylcytosines called by Bycom and Lister remained constant when the non-conversion rate was <1% . When the non-conversion rate was >1% , the total number of methylcytosines called by Bycom increased slightly and the false negative decrease steadily; conversely , the total number of methylcytosines called by Lister increased sharply ( Figure 2D ) . The other three factors , high quality value ratio ( Q20 cut-off ) , mismatch number , and SNP frequency made only small contributions to either the number of methylcytosines called or the false positive rate , both of which remained nearly constant . Here , for the method Lister , the influence of sequencing errors calculated by quality value were reduced as the quality cut-off went up , while for Bycom , the influence was still kept at a relatively low level ( Figure S1B & S1D ) . Overall , compared with the performance of Lister , Bycom generated more true positive sites and fewer false positive sites ( Figure S1 ) . To further assess the ability of Bycom to identify methylcytosines , whole-genome BS-seq data of YH [24] , silkworm [19] and the ascomycetes Aspergillus flavus [5] were used as described in [24] . The YH BS-seq data , generated from the peripheral blood mononuclear cells of an Asian individual , had a high methylation level of 68 . 4% at CpG sites and <0 . 2% at non-CpGs sites with an overall coverage of 12 . 3-fold per strand . 32 methylated CpGs and 18 non-methylated CGs were selected randomly and validated by bisulfite Sanger sequencing . Because the depth of 11 validated cytosines was lower than the threshold ( lowest depth was 4-fold ) [24] , only 24 methylated CpGs and 15 non-methylated CGs gave eligible validated results . The methylcytosine calling results showed that the accuracy for Bycom was 71 . 79% compared with the 66 . 67% accuracy for Lister , and both methods made no false calls ( Figure 3A ) . We also evaluated the performance of Bycom on a species that had a low methylation level . The BS-seq data of silkworm had overall methylation levels of 0 . 67% at CG , 0 . 21% at CHG , and 0 . 24% at CHH sites ( where H is A , C , or T ) with an average depth of 7 . 4-fold per strand . In the BS-seq silkworm data , 598 methylcytosines and 311 cytosines were validated ( ≥4× ) among which 24 methylated and 101 non-methylated sites were verified by bisulfite Sanger sequencing while the others were confirmed using bisulfite-PCR followed by 454 sequencing . The distribution of the validated sites called by Bycom and Lister is shown in Figure 3A . For bisulfite Sanger sequencing , Bycom had 93 . 6% accuracy with a false positive rate of 0 . 99% , while Lister exhibited 83 . 2% accuracy with a false positive rate of 15 . 84% . For 454 sequencing , the Bycom and Lister accuracies were the same at 82 . 53% with false positive rates of 10 . 48% for Bycom and 13 . 81% for Lister ( Figure 3B ) . Finally , we evaluated the performance of Bycom on the BS-seq data of Aspergillus flavus , which had been reported to have a negligible level of methylation with 4-fold overall depth per strand . All the methylcytosines called by Lister on this data were regarded as false positives ( ≥4× ) . An overlapping strategy was applied between the cytosines detected by Bycom and by Lister . The results indicated that the false positive rate for Lister was at least three times the false positive rate for Bycom ( Figure 3C ) . Further , sites that were selected randomly from the methylcytosines called by Lister and verified by Sanger sequencing as non-methylated cytosines were not called by Bycom . Meanwhile , we splited the validated sites of YH , silkworm and Aspergillus flavus into CpG and CpH ( CpA , CpC , and CpT dinucleotides ) context . The strategy was also performed on the validated results of Bycom and Lister in these samples . We found that true positive sites ( mC ) were all in CpG context and the sites in CpH context were all false positive . The results also indicated that Bycom was more precise on the detection of mCpH than Lister ( Table S1 ) . We evaluated the performance of Bycom on RRBS data from T29 cells ( GSE55568 ) . A total of 2 , 110 , 089 methylcytosines were identified in the genome , making up nearly 0 . 18% of the total cytosines; 98 . 58% of the identified methylcytosines were in CG sites with a mean sequencing depth of 15 . 5-fold per strand ( Table S2 ) . To verify the precision of Bycom , seven batches of sequences from the T29 RRBS data were selected randomly for validation by bisulfite-PCR followed by sequencing on the Illumina MiSeq platform . As a result , 68 methylcytosines and 158 cytosines were validated . Bycom identified a high percentage ( 75% ) of the CpG methylcytosines and a low percentage ( 5 . 56% ) of the CpH methylcytosines . Lister identified a relatively low percentage ( 71 . 88% ) of mCpG and the equal proportion of mCpH as Bycom . Besides , a similar low proportion of the validation sites were identified as cytosines by both Bycom and Lister ( Table S3 ) . Bycom and Lister had an accuracy of 93 . 81% and 93 . 36% , respectively , with the same false positive rate of 4 . 12% . Notably , methylcytosines in the CpH context , which were nearly complete absence in the T29 cells [20] , were regarded as background noise ( Table S3 ) . We compared the accuracy of mC calling between Bycom and methylation analysis tools , including Bismark and Bisulfighter , which employed Lister as the algorithm to detect methylcytosines . With all parameters set to default in simulator BSSim , we found that the software based on Bycom run fastest ( Table S4 ) . Besides , Bycom had a highest overall accuracy than others as shown by the ROC curves ( Figure 4A ) . Then , we compared the software based on Bycom with Bismark and Bisulfighter using the previous public data . Firstly , Bycom had a highest accuracy than other two in YH Sanger-sequencing data without false positive results . Secondly , in silkworm data , for 454-sequencing validation , the accuracy of Bycom was lower than that of Bismark and higher than that of Bisulfighter . For Sanger-sequencing validation , Bycom kept highest precision on the detection of methylcytosines than others ( Figure 4B ) . Finally , the validation sites of Aspergillus flavus Sanger sequencing data all proven to be false positive were not called by Bycom and Bismark , and 16 . 67% of them were selected by Bisulfighter as methylcytosines .
Detecting methylcytosines accurately from high-throughput sequencing data is an essential step to calculate the methylation level of genomes at single-base resolution . In this study , we presented a novel computational strategy , Bycom , for identifying precisely methylcytosines from BS-seq data using the Bayes inference model . Bycom not only considers the impacts of sequencing errors and non-conversion rate , both of which are treated as the false-positive rate in the Lister method , but also introduces cell heterozygosis to identify methylcytosines in an unbiased manner . The results on the simulated data showed that the non-conversion rate and total methylation level significantly affected the accuracy of the Bycom and Lister methods , while sequencing errors had limited influence after the relatively stringent quality filtration of the data . Additionally , the accuracy of methylcytosine calling was better with Bycom than with Lister . The results on the public data indicated that the false positive rate of Lister was higher than the false positive rate of Bycom in the genome-wide samples with low methylation levels . Although The validation sites of public data in CpH context were all detected as false positive sites , Bycom had a better performance on the classification of mCpH than Lister . Furthermore , the validation results on the T29 RRBS data showed that Bycom detected the vast majority of methylcytosines and maintained the false calling rate at a low level . The performance of Bycom on the public data with different genome-wide methylation levels revealed that Bycom had no bias on data with high or low methylation levels . Moreover , to further verify the accuracy of our method , we compared the accuracy of Bycom with the methylation analysis tools , Bismark and Bisulfighter , which could also be used to call methylcytosines . The results of simulated data and public data both showed that Bycom behaved better than Bismark and Bisulfighter . However , we noticed that variances of the methylation level on single cytosines between the BS-seq data and the Sanger/454 sequencing data likely led to false/missing calls by both Bycom and Lister . Although the methylcytosines were still detected by Bycom , this finding suggested that the accuracy might improve further if the methylation level of the base was recalibrated depending on mass validated sequencing data . In future work , we aim to refine the model with well-recalibrated sequencing data . Besides , other functions , such as differentially methylated regions ( DMRs ) detection will be added in future , which will be great facilitated by the precise methylated cytosines calling . In conclusion , the results presented here demonstrate that Bycom is an effective statistical framework that is capable of identifying methylcytosines and will be very useful for genome-wide investigations of DNA methylation .
NCBI build 36 . 1 ( hg18 ) was downloaded from the UCSC database ( http://genome . ucsc . edu/ ) and used as the human reference genome . The whole-genome DNA methylation data from an anonymous male Asian Han Chinese ( YH ) were obtained from the NCBI Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo; GSE17972 ) . The silkworm sequence data were downloaded from the GEO database ( GSE18315 ) and the Sequence Read Archive ( SRA; SRP001159 ) . The Aspergillus flavus reference genome sequence was downloaded from the Aspergillus flavus Genome Sequencing Project web site ( http://www . aspergillusflavus . org/genomics/ ) and the DNA methylation data were obtained from GEO ( GSE32177 ) . The raw Illumina T29 RRBS data are available from GEO under accession number GSE55568 . Simulated bisulfite short reads with different levels of six factors ( total methylation level , sequencing depth , bisulfite conversion rate , sequencing quality of base , number of misaligned reads , and mutation rate ) were simulated from chromosome 10 of hg18 using BSSim ( http://122 . 228 . 158 . 106/BSSim/ ) . The default settings of the six factors in BSSim were used for the simulation as: sequencing depth 30×; maximum mismatches 2; bisulfite conversion rate 0 . 998; high quality value ratio 95%; and SNP frequency for homozygote/heterozygote 0 . 0005/0 . 001 . Besides , different methylation levels of 86 . 53% , 2 . 03% , and 2 . 27% were set for the different types of cytosines CG , CHG , and CHH , respectively , where H is A , C , or T . Reads were generated from random locations on chromosome 10 with different settings of the six factors; one factor was changed while the other parameters remained at the default values . Different levels were set as: sequencing depth 5× , 15× , 25× , … , 95×; mismatch number 1 , 2 , 3 , … , 10; bisulfite non-conversion rate 0 . 01 , 0 . 02 , 0 . 03 , … , 0 . 1; and whole-genome methylation level 0 . 002 , 0 . 004 , 0 . 006 , 0 . 008 ( extremely low ) , 0 . 02 , 0 . 04 , 0 . 06 , 0 . 08 ( low ) , and 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 ( high ) . For SNP frequency , homozygotes were set as 0 . 0005 , 0 . 001 , 0 . 0015 , … , 0 . 005 and heterozygotes were set at double the homozygote frequency . High quality values indicated by ASCII quality values above the char “h” of 10% , 20% , 30% , … , 90% , 99% were assigned to the whole reads . The methylation status and SNP genotypes of each cytosine were then retrieved from the BSSim simulated files . Low quality reads ( Q20 ) were eliminated for the raw data and the clean reads were aligned against the reference genome allowing a maximum of two mismatches . Uniquely mapped reads were retained for the following analysis . For a selected genomic location , the cytosines in that region were set as , where n is the total number of cytosines in the region . Then , the observed base of a mapped read j at cytosine site ci in the genomic sequence was assumed as , where m is the total number of reads that cover site . The quality value of the observed base was set to . In the Bayesian inference model , the posterior probability of expecting base at site on the reference genome was expressed as:where m is the total number of reads covering site . Here , was considered as the actual base according to the observed base of the read when bi was estimated . The independent base was expressed as . When the sequencing error ratio is set to , the likelihood of the site was calculated as: Beside the sequencing error , other factors , including bisulfite conversion rate and methylated heterozygosity , also influence the composition of the bases derived from the reads . After bisulfite treatment , heterozygous methylcytosines , heterozygous cytosines , wrong-sequenced cytosines , correct-sequenced cytosines , converted cytosines , and unconverted cytosines may appear simultaneously at identical cytosine sites in the sequences from distinct cells . Therefore , the likelihood was computed as:The prior probability of each base bi at site on the genome was set as: In this way , the posterior probability derived from the Bayesian inference formula was obtained . The key to the model is the accurate estimation of three parameters , , and , which can change the of base contribution component from the reads covering the cytosine site . To estimate the methylated heterozygosity , a linear fitting equation was built using the methylation level of a 1000-bp genome region of single chromosome in which the methylation status has been reported to be highly correlated [24] , [28] . The of the midpoint in a 1000-bp region was estimated as:where is the methylation level ( methylated reads/total reads ) of site . The values of a and b were obtained by a least square procedure as:where is the methylation level of site in a 1000-bp region centered at sites and is the average methylation level of the 1000-bp region centered at sites . The region centered at contained n cytosine sites . The of cytosine sites in the 500-bp head or tail of the chromosome were estimated using the average methylation level of the cytosine sites in the 1000-bp head or tail chromosome genome regions as:Where p is the total number of the cytosine sites located in the 1000-bp head or tail of the chromosome . and are the number of methylated reads and total reads at site , respectively . The sequencing error ratio , which is indicated by the quality value of the observed base , was calculated as [30] . The bisulfite conversion rate was determined initially by un-methylated lambda DNA spike-ins [25] or by calculating the C to T conversion rate for all cytosines in the CpH context [19] , [24] . After bisulfite treatment , the posterior probability of confirming the cytosine site as base C is actually the probability of cytosine methylation . The threshold T was set to ensure methylcytosine calling was precise , and was computed by a numerical iteration algorithm as: ( * ) where and represent the number of cytosine and methylcytosine sites , respectively , on a chromosome . The false discovery rate ( FDR ) was set as 0 . 01 . In the iteration , was calculated as the number of cytosine sites with a posterior probability above the T threshold . Set for the first iteration , and the posterior probability above the value of the cytosine would be the methylated cytosine . Then we can calculate , substitute it into ( * ) and obtain . For the second iteration , replaces , and acquire ; will tend to the invariant threshold as T after several iterations . Total genomic DNA was extracted from the T29 cell line and converted by sodium bisulfite following an established protocol [31] . Seven batches of regions were selected randomly and amplified with nested RT-PCR primers ( Table S5 ) . The amplified products were sequenced on the Illumina MiSeq platform ( Illumina Inc , San Diego , CA ) . The validation results are summarized in Table S3 .
|
High-throughput bisulfite sequencing ( BS-seq ) has advanced tremendously the study of DNA methylation and the determination of methylcytosines at single-base resolution . In BS-seq data analysis , sequencing errors , incomplete bisulfite conversion , and cell heterozygosis affect the accuracy of methylcytosine detection in quite a major way . Simple filtering methods using predefined thresholds have proved to have extremely low efficiency . The commonly used Lister uses binomial distribution to overcome the impacts of non-conversion rate and sequencing errors , but the impact of the cell heterozygosis is not considered . Here , we present Bycom , a novel algorithm based on the Bayesian inference model . To improve the accuracy of methylcytosine calling , Bycom considers sequencing errors , non-conversion rate , and cell heterozygosis integratively to identify methylcytosines from BS-seq data . We evaluated the performance of Bycom using different kinds of BS-seq data . Our results demonstrated that Bycom identified methylcytosines more accurately than Lister , especially in BS-seq data with extremely low genome-wide methylation levels .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"bioinformatics",
"database",
"and",
"informatics",
"methods",
"mathematical",
"and",
"statistical",
"techniques",
"genetics",
"bayesian",
"method",
"biology",
"and",
"life",
"sciences",
"dna",
"computational",
"biology",
"dna",
"modification",
"epigenetics",
"epigenomics",
"dna",
"methylation",
"research",
"and",
"analysis",
"methods"
] |
2014
|
A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data
|
CD8+ T cells are essential for host defense to intracellular bacterial pathogens such as Mycobacterium tuberculosis ( Mtb ) , Salmonella species , and Listeria monocytogenes , yet the repertoire and dominance pattern of human CD8 antigens for these pathogens remains poorly characterized . Tuberculosis ( TB ) , the disease caused by Mtb infection , remains one of the leading causes of infectious morbidity and mortality worldwide and is the most frequent opportunistic infection in individuals with HIV/AIDS . Therefore , we undertook this study to define immunodominant CD8 Mtb antigens . First , using IFN-γ ELISPOT and synthetic peptide arrays as a source of antigen , we measured ex vivo frequencies of CD8+ T cells recognizing known immunodominant CD4+ T cell antigens in persons with latent tuberculosis infection . In addition , limiting dilution was used to generate panels of Mtb-specific T cell clones . Using the peptide arrays , we identified the antigenic specificity of the majority of T cell clones , defining several new epitopes . In all cases , peptide representing the minimal epitope bound to the major histocompatibility complex ( MHC ) -restricting allele with high affinity , and in all but one case the restricting allele was an HLA-B allele . Furthermore , individuals from whom the T cell clone was isolated harbored high ex vivo frequency CD8+ T cell responses specific for the epitope , and in individuals tested , the epitope represented the single immunodominant response within the CD8 antigen . We conclude that Mtb-specific CD8+ T cells are found in high frequency in infected individuals and are restricted predominantly by HLA-B alleles , and that synthetic peptide arrays can be used to define epitope specificities without prior bias as to MHC binding affinity . These findings provide an improved understanding of immunodominance in humans and may contribute to a development of an effective TB vaccine and improved immunodiagnostics .
Infection with Mycobacterium tuberculosis ( Mtb ) remains an important cause of infectious disease , morbidity , and mortality worldwide [1] and has emerged as a major opportunistic infection in individuals with HIV/AIDS [2] . Control of infection with Mtb relies heavily on the cellular immune system , that is , the interaction of lymphocytes and Mtb-infected macrophages and dendritic cells ( DCs ) [3 , 4] . CD8+ T cells are associated with strong CD4+ TH1 cell responses , and are not only essential for effective immunity to viral pathogens , but also for immunity to some intracellular bacteria , such as Listeria monocytogenes and Salmonella species [5] . Increasing experimental evidence in the mouse tuberculosis ( TB ) model has suggested a protective role for CD8+ T cells in the host response . For example , adoptive transfer or in vivo depletion of CD8+ cells showed that this subset could confer protection against subsequent challenge [6–8] . β2-microglobulin–deficient mice , deficient in expression of major histocompatibility complex ( MHC ) class I , are more susceptible to Mtb [9] and to large doses of Bacille Calmette Guérin [10] infection than their wild-type littermates . This finding has been corroborated in CD8-deficient mice [11] and other mice deficient in class I processing and presentation [11–13] . However , mice lacking class Ia–restricted CD8+ T cells demonstrate more moderate susceptibility to Mtb infection [14 , 15] . In humans , Mtb-specific CD8+ T cells have been identified in Mtb-infected individuals and include CD8+ T cells that are classically , MHC-Ia , restricted [16–22] , and non-classically , MHC-Ib , restricted by HLA-E [18 , 23] , and by CD1 [24–26] . Taken together , studies of mice and humans support an important role for CD8+ T cells in TB immunity . For most infections , the repertoire of the CD8 response is shaped by the entry of antigen into the MHC-I processing pathway , binding of peptides and/or non-peptide antigens to MHC-I molecules , and recognition of these structures by T cells . Ultimately , a relatively limited subset of pathogen-specific T cells emerge , a process that has been termed immunodominance [27] . While substantial effort has focused on defining immunodominant CD8 antigens for important human viral pathogens such as HIV and cytomegalovirus ( CMV ) , little is known about the antigens recognized by human CD8+ T cell in response to intracellular bacterial infections . Furthermore , although a number of commonly recognized CD4 Mtb antigens have been described [28 , 29] ( ESAT-6 , CFP10 , Ag85 , etc . ) , surprisingly little is known about common Mtb antigens recognized by human CD8+ T cells . The majority of CD8+ epitopes that have been identified were defined by testing of Mtb peptides selected for high-affinity binding to MHC class Ia molecules ( HLA-A2 in most cases; [19 , 20 , 30–34] ) . In almost all of these examples , however , the ex vivo frequency of these T cells in Mtb-infected individuals is low or undetectable , suggesting that these specificities may not represent immunodominant responses . In contrast , in the limited cases in which T cells have been used to define epitopes contained in selected Mtb antigens , high ex vivo frequencies have been demonstrated [17 , 35] , suggesting that a T cell–centered approach can identify immunodominant epitopes . Moreover , CD8+ T cell responses to some Mtb antigens that represent good CD4 antigens ( CFP10 , ESAT-6 , Ag85 , and Mtb39 ) have been detected at high frequency in persons infected with Mtb [17–19 , 34] . Therefore , we used a limited library of overlapping synthetic peptides representing several known CD4 Mtb antigens to determine the magnitude of the CD8 response to these antigens in persons with active TB and latent TB infection ( LTBI ) , as well as uninfected individuals . Furthermore , we utilized a panel of Mtb-specific CD8+ T cell clones to define minimal epitopes recognized within these antigens and determined the contribution of these novel epitopes to the ex vivo Mtb-specific CD8 response .
To define immunodominant Mtb-specific CD8 antigens , and to determine whether or not these responses result from infection with Mtb , we have used CD8+ T cells from uninfected donors , those with LTBI , or those actively infected with Mtb . Responses were determined either directly ex vivo , or using CD8+ T cell clones obtained by limiting dilution cloning on Mtb-infected autologous DCs [36] . As much is known about dominant CD4 Mtb antigens , a panel of these commonly recognized antigens was selected for further evaluation . These were Mtb39 , CFP10 , Mtb8 . 4 , Mtb9 . 9A , ESAT-6 , Ag85b , 19kDa , and EsxG . To avoid bias introduced by using peptides of predicted HLA binding specificity , we synthesized overlapping peptides ( 15 aa , overlap 11 aa ) to represent the proteins of interest [17] . To accurately determine the ex vivo effector cell frequencies of CD8+ T cells , linear regression analysis was used . As shown in Figure 1 , using D466 as an example , magnetic bead–purified CD8+ T cells were tested against peptide-pulsed DCs over a range of CD8+ T cell numbers in an IFN-γ ELISPOT assay . A positive assay was determined as described below and if positive , the antigen-specific frequency was determined using linear regression . Uninfected individuals ( n = 14 ) , those with LTBI ( n = 20 ) , and those with active TB ( n = 12 ) were evaluated for CD8 responses to a panel of Mtb CD4+ T cell antigens , as well as to Mtb-infected DCs . All individuals tested had robust CD8+ T cell responses to Mtb-infected DCs and were of greater magnitude in individuals with active TB than in those with LTBI ( p = 0 . 01; Figure 2; Table 1 ) . However , CD8+ T cell responses to the panel of Mtb antigens were found almost exclusively in those infected with Mtb in that statistically significant differences between uninfected and Mtb-infected individuals were noted for seven of ten antigens for both the magnitude of the response ( Figure 2 ) and the proportion of positive assays ( Table 1 ) . However , differences in CD8+ T cell responses between individuals with active TB and LTBI were not statistically different . While strong CD8+ T cell responses were observed against many of the antigens tested , it is equally notable that several individuals with strong Mtb-directed CD8+ T cell responses did not have demonstrable responses to many of the antigens tested ( unpublished data ) . These ex vivo frequency data demonstrated the presence of high-frequency responses to a number of known Mtb antigens , but did not shed light on the restricting allele , minimal epitope , or dominance hierarchy within the gene of interest . To address this question , we performed limiting dilution cloning of human CD8+ T cells using Mtb-infected DCs [36] , and generated panels of both classically and non-classically HLA-restricted CD8+ T cell clones . Using peptide pools representing known CD4 antigens , the antigenic specificity of the HLA-Ia-restricted clones was defined in more than half of the clones ( Table 2 ) . This approach is demonstrated in detail for a single representative clone , D466 D6 , derived from an individual with active TB . As shown in Figure 3A , testing the clone against autologous DCs pulsed with a panel of peptide pools unambiguously defined the antigenic specificity as CFP10 . The clone was then tested against each of the 15-mer peptides that comprise the CFP10 pool , revealing that the epitope was contained within CFP101–15 ( Figure 3B ) . Each possible 8-aa , 9-aa , 10-aa , and 11-aa peptide was then synthesized and tested for reactivity , revealing antigenic activity between aa 2–11 ( Figure 3C ) . Similarly , each clone was tested against lymphoblastoid cell lines ( LCLs ) sharing at least one HLA type with the donor ( Figure 3D ) . Autologous LCL and IHW 9058 LCL , which share B4501 and C1601 , presented the epitope to the clone , identifying both B4501 and C1601 as possible restricting alleles . However , C1601+ D433 LCL did not present the epitope , eliminating C1601 as a candidate-restricting allele . Therefore , D466 D6 was restricted by HLA-B4501 . As demonstrated in Figure 4 , by testing each plausible epitope over a broad range of concentrations , the minimal epitope was defined as CFP102–12 for D466 D6 . Experimental data supporting the assignment of the minimal epitope is provided for each clone in Figure 5 , and a summary of the antigenic specificity , minimal epitope , and HLA-restricting allele is presented in Table 3 . Unexpectedly , all but one of the T cell clones were restricted by HLA-B alleles . Furthermore , a minority of those observed were 9 aa in length . Because each of the individual CD8+ T cell clones was derived based on growth in the presence of Mtb- infected DCs , we sought to determine whether or not the antigen and epitopes identified reflected immunodominant epitopes ex vivo . Two independent approaches were pursued , the first to determine if the response was present at high frequency , and the second to determine what proportion of the total response to the antigen was constituted by the epitope . To determine the ex vivo effector cell frequency , as described in Figure 1 , each epitope was tested using autologous DCs and magnetic bead–purified CD8+ T cells derived from the donor from whom the T cell clones was isolated . A summary of the ex vivo epitope-specific effector cell frequencies is presented in Table 3 . For comparison , effector cell frequencies using autologous DCs infected with Mtb as the antigen-presenting cells ( APCs ) are shown as well . For 11 CD8+ T cell clones recognizing distinct epitopes , the epitope-specific frequency exceeded 50% of the total Mtb-specific CD8+ T cell response . For six of these clones , the epitope-specific frequency actually exceeded the total frequency of Mtb-specific CD8+ T cells . Conversely , for two clones , the epitope-specific T cell frequency constituted the minority of the total Mtb-specific CD8+ T cell response . Thus , overall , the epitopes reflected high-frequency responses , and could be considered a response that has been primed by exposure to Mtb . Notably , T cell clones isolated from four donors recognized CFP10 . To determine if the epitopes defined reflected a substantial proportion of the total response to the antigen of interest , magnetic bead–purified CD8+ T cells from three donors with sufficient available peripheral blood mononuclear cells ( PBMCs ) were tested for reactivity to each individual 15-mer peptide , the peptide pool , and peptide representing the minimal epitope . As is demonstrated in Figure 6 , the ex vivo frequencies to the minimal epitope , 15-mer peptide ( s ) containing the minimal epitope , and peptide pool were remarkably concordant . These data , then , suggest that for each donor a dominance hierarchy has been clearly established , and was reflected in the original clones . Finally , as is noted in Table 3 , daughter clones of identical specificity were frequently identified , a result that would be predicted based on an immundominance hierarchy . TCR V beta staining was used to confirm the clonal relationship between daughter clones . Interestingly , in two cases , the identical minimal epitope and HLA restriction was represented by two distinct clones ( Table 3 ) . Because much work on human CD8+ T cell responses to Mtb has relied upon the use of HLA prediction algorithms , as each epitope was defined we asked whether or not the epitopes would have been predicted by these approaches . Given the prevalence of HLA-B alleles and 10-mer and 11-mer epitopes , it is perhaps not surprising that many of these epitopes were not ranked strongly ( unpublished data ) . This left open the possibility that either HLA binding was indeed not predictive of antigenicity , or simply highlighted the limitations of those algorithms at the time they were used . To address this question experimentally , the IC50 for each peptide that had been synthesized in the course of definition of the minimal epitope was determined against a panel of human HLA molecules ( Table S1 ) . Shown in Table 3 is the IC50 for the minimal epitope with the cognate restricting allele . These data demonstrate that the T cell epitopes bound avidly to HLA , and show a high degree of concordance between the T cell epitope data and HLA binding data ( Figure 5; Table S1 ) .
Although the complete repertoire of CD8+ T cell responses in Mtb remains incompletely characterized , the following conclusions can be drawn . First , CD8+ T cell responses are present in persons infected with Mtb at frequencies that are comparable to that seen following many common viral infections such as vaccinia , influenza , and CMV [37 , 38] . This conclusion is based both on the pooled peptide experiments described above , and on the observation that when defined , dominant epitopes are present at high ex vivo frequencies . Conversely , we have not observed high-frequency responses to CD4+ T cell antigens in those without evidence of infection with Mtb . This observation strongly supports the hypothesis that these responses reflect an adaptively acquired response to infection with Mtb , rather than an innate response . By contrast , CD8+ T cell responses to Mtb-infected DCs were equivalent in infected and uninfected individuals . Using limiting dilution analysis , we have studied the proportions of Ia- versus Ib- restricted CD8+ T cells in Mtb-infected compared to uninfected individuals and have noted that Ib-restricted CD8+ T cell responses predominate in uninfected individuals ( D . Lewinsohn , unpublished data ) . Therefore , we speculate that the response detected to Mtb-infected DCs in uninfected individuals may reflect chiefly a Ib-restricted response . While we did not observe an association of responses to specific antigens with disease status , a larger study might reveal more subtle differences . All but one of the epitopes that have been mapped to date are restricted by HLA-B molecules . The reasons for this possible skewing are not yet clear . It is a formal possibility that the cloning methods we used biased isolation of HLA-B- over HLA-A-restricted T cell clones . However , using identical cloning methodology , we have isolated T cell clones specific for vaccinia , CMV , and influenza that did not display a preference for HLA-B restriction ( D . Lewinsohn , unpublished data ) . Furthermore , in all three cases where we tested ex vivo the entire set of peptides representing CFP10 , the entire response mapped to the HLA-B-restricted epitope ( Figure 6 ) . Therefore , we speculate that Mtb antigens may preferentially bind to HLA-B molecules , that Mtb preferentially interferes with HLA-A processing and presentation , that infection with Mtb leads to selective upregulation of HLA-B , or that HLA-B is preferentially delivered to the Mtb phagosome . Nonetheless , these data are consistent with those reported by Kiepiela et al . [39] , in which HIV-specific T cell responses were found to be 2 . 5-fold more likely to be HLA-B- than HLA-A-restricted , and observed that viral load was more closely linked to HLA-B than HLA-A alleles . Human infection with mycobacteria has been demonstrated in pre-urban Egypt [40] and in pre-Columbian Peru [41] , observations consistent with the hypothesis that mycobacterial infection has driven the diversity and peptide-binding repertoire of the HLA-B locus . Furthermore , our data is consistent with the hypothesis that the diversity of HLA-B alleles is related to the containment of both viral and bacterial pathogens . Hence , delineation of immunodominant epitopes and antigens within the context of important human pathogens will likely require careful evaluation of those epitopes presented by HLA-B . While the immune response within an individual to a given antigen is narrowly focused , dominant epitopes that would be useful for population-based analysis have yet to be defined . This conclusion is based on the fact that few of the HLA-A2 epitopes described to date have proved to be widely recognized , and , most importantly , on the observation that a wide variety of HLA alleles appear to be used in the recognition of Mtb antigens . The antigen CFP10 is an excellent case in point . As is demonstrated in Table 3 , T cell clones have been used to define high-frequency epitopes restricted by a variety of HLA alleles . While the N-terminal 15 aa could reasonably be considered immunogenic , in all but one case ( CFP102–11; HLA-B44 ) the minimal epitope defined has been unique to the individual from whom the T cells were derived . By using a T cell–driven approach to epitope identification , it is possible to define dominant epitopes in humans infected with Mtb . While it is striking that for several of the T cell clones the ex vivo frequency of epitope-specific T cells was equal to or exceeded the total ex vivo frequency of Mtb-specific T cells , we acknowledge that the use of peptide-loaded DCs to determine T cell frequency likely overrepresents the proportion of the total Mtb-specific CD8+ T cell responses . Possible reasons are that Mtb infection is likely to produce relatively less cognate peptide compared to peptide loading , and that Mtb infection could possibly interfere with class I processing and presentation . Although the current observations are limited to a small panel of known CD4 antigens , current work is underway to perform a genome-wide survey of dominant CD8 antigens in Mtb . Definition of this panel will likely prove useful in the further study of the natural history of infection with Mtb and in the design of novel vaccines and diagnostics . In TB , evaluation of epitopes based strictly on HLA binding algorithms has focused on HLA-A2 , and has often failed to define dominant epitopes . Our observation that many of the epitopes are HLA-B restricted , and often longer than 9 aa , for which these algorithms are less robust , may help explain these findings . However , when evaluated experimentally , all of the minimal epitopes exhibit high-affinity binding to the cognate-restricting allele . In this regard , substantial progress in HLA prediction algorithms is evident [42 , 43] , and could facilitate more efficient identification of dominant epitopes . Here , more information with regard to longer peptides will be of benefit . Finally , we speculate that these caveats are likely to apply to other intracellular pathogens as well . One limitation to current knowledge is that the responses in humans have been made at a single time point . In this regard , a feature of Mtb is the chronic exposure to antigen that may persist for many years . How this chronic infection influences the shaping of the immune response and dominance repertoire is an important question that remains unresolved . The advent of new reagents for immunologic studies in the mouse model [44] will likely be useful in this regard . For example , chronic antigenic exposure seems likely to alter the affinity of the T cell response over time . Furthermore , it is possible that such long-term infection might lead to clonal exhaustion or dysfunction as has been described for chronic viral infection [38] . Finally , given the very high-frequency responses that we and others have observed to Mtb-infected DCs and to single antigens , it appears that the immune response to Mtb occupies a sizable fraction of the host's immunological activity , similar to that previously observed for infection with CMV [45] . If so , then this may have important implications for the aging immune system , and potentially for the requirements for the long-term containment of intracellular infection . For example , it is possible that threshold Mtb-specific CD8 frequencies are necessary for the maintenance of a state of chronic persistence . Conversely , the substantial immunological effort directed at Mtb may limit the host's ability to effectively combat novel infections . As a result , this static picture leaves open important questions as to the evolution of the Mtb-specific responses , and its relationship with chronic infection with Mtb . It seems likely that Mtb has evolved potent mechanisms to modulate the immune response . At present , specific mechanisms for MHC-I immune modulation have not been described . However , it appears that TLR II stimulation via the Mtb-derived 19-kDa lipoprotein can modulate both MHC-I and MHC-II antigen processing , and can interfere with IFN-γ signaling [46–49] . Further work on the T cell subsets important in Mtb , including the immunodominant epitopes , will extend our understanding of the immunology of TB and potentially contribute to the development of a vaccine against this major killer . How the Mtb-specific CD8+ T cell response fits into the natural history of infection with Mtb remains poorly characterized . For example , the ontogeny of the CD8+ T cell response relative to infection with Mtb remains unknown , as does the relationship of CD8+ T cell frequencies with regard to bacterial burden . However , by defining commonly recognized CD8+ T cell antigens and epitopes , it will become increasingly possible to employ direct ex vivo analysis to more precisely define Mtb-specific T cell responses in various subject groups of particular interest .
Study participants , protocols , and consent forms were approved by the Oregon Health and Science University institutional review board . Informed consent was obtained from all participants . Uninfected individuals and individuals with LTBI were recruited from employees at Oregon Health and Science University as previously described [36] . Uninfected individuals were defined as healthy individuals with a negative tuberculin skin test and no known risk factors for infection with Mtb . Individuals with LTBI were defined as healthy persons with a positive tuberculin skin test and no symptoms and signs of active TB . Individuals with active TB were recruited via institutional review board–approved advertisement and were self-referred from the Multnomah County TB Clinic , Portland , Oregon , United States , or from the Washington County TB Clinic , Hillsboro , Oregon , United States . In all active TB cases , pulmonary TB was diagnosed by the TB Controller of these counties and confirmed by positive sputum culture for Mtb . PBMCs were isolated from whole blood obtained by venipuncture or apheresis . Culture medium consisted of RPMI 1640 supplemented with 10% fetal bovine sera ( BioWhittaker , http://www . cambrex . com/ ) , 5 × 10−5 M 2 ME ( Sigma-Aldrich , http://www . sigmaaldrich . com/ ) , and 2 mM glutamine ( GIBCO BRL , http://www . invitrogen . com/ ) . For the growth and assay of Mtb-reactive T cell clones , RPMI 1640 was supplemented with 10% human serum . Mtb strain H37Rv was obtained from the American Type Culture Collection ( http://www . atcc . org/ ) and prepared as previously described [36] . Peptides were synthesized by Genemed Synthesis ( http://www . genemedsyn . com/ ) . Synthetic peptide pools consisted of 15 mers overlapping by 11 aa , representing Mtb proteins demonstrated to be potent CD4 antigens . Peptide pools representing CFP10 [50 , 51] , ESAT-6 [52] , Mtb39a ( two pools , A & B ) [53] , Mtb8 . 4 [54] , Mtb 9 . 9A [16] , EsxG [55 , 56] , 19kDa antigen [57] , and antigen 85b ( two pools , A & B ) [58] were synthesized . Peptides were resuspended in DMSO , and up to 50 peptides were combined into one pool such that each peptide in the pool was at a concentration of 1 mg/ml . Peptide pools were stored at −80 °C . EBV-transformed B cell lines , LCLs , were either generated in our laboratory using supernatants from the cell line 9B5–8 ( American Type Culture Collection ) or obtained from the National Marrow Donor Program ( http://www . marrow . org/ ) . LCLs were maintained by continuous passage as previously described [18] . Mtb-specific T cell clones were isolated from individuals with LTBI or active TB , using Mtb-infected DCs as APCs and limiting dilution cloning methodology as previously described [36] . Briefly , CD8+ T cells were isolated from PBMCs using negative selection using CD4 antibody-coated beads and then positive selection using CD8 antibody-coated magnetic beads per the manufacturer's instructions ( Miltenyi Biotec , http://www . miltenyibiotec . com/ ) or via flow cytometry . In this case , CD4-PE ( BD Biosciences , catalog #555347; http://www . bdbiosciences . com/ ) negative and CD8-APC ( BD Biosciences , catalog #555369 ) positive cells ( purity >99% ) were sorted on a Becton Dickinson LSR II ( http://www . bd . com/ ) . T cells were seeded at various concentrations in the presence of a 1 × 105–irradiated autologous Mtb-infected DC , generated as described below , and rIL-2 ( 5 ng/ml ) in cell culture media consisting of 200 μl of RPMI 1640 supplemented with 10% human sera . Wells exhibiting growth between 10–14 d were assessed for Mtb specificity using ELISPOT and Mtb-infected DCs as a source of APCs . T cells retaining Mtb specificity were further phenotyped for αβ T cell receptor expression and CD8 expression by FACS and expanded as described below . V beta usage was determined using the IOTest Beta Mark Kit from Beckman Coulter , catalog #IM3497 ( http://www . beckmancoulter . com/ ) . To expand the CD8+ T cell clones , a rapid expansion protocol using anti-CD3 mAb stimulation was used as described previously [18] . Monocyte-derived DCs were prepared according to a modified method of Romani et al . [18 , 59] . To generate Mtb-infected DCs , cells ( 1 × 106 ) were cultured overnight in the presence of Mtb at a multiplicity of infection = 50:1 . We have determined that this multiplicity of infection is optimal for detection of Mtb-specific CD8+ T cells , as heavy infection is required to optimize entry of antigen into the class I processing pathway [60] . After 18 h , the cells were harvested and resuspended in RPMI/10% human serum . The MHC peptide binding assay utilized measures the ability of peptide ligands to inhibit the binding of a radiolabeled peptide to purified MHC molecules , and has been described in detail elsewhere [61] . Briefly , purified MHC molecules , test peptides , and a radiolabeled probe peptide are incubated at room temperature in the presence of human β2-microglobulin and a cocktail of protease inhibitors . After a 2-d incubation , binding of the radiolabeled peptide to the corresponding MHC class I molecule is determined by capturing MHC/peptide complexes on W6/32 antibody ( anti-HLA A , B , and C ) or B123 . 2 ( anti-HLA B , C , and some A ) coated plates , and measuring bound cpm using a microscintillation counter . For competition assays , the concentration of peptide yielding 50% inhibition of the binding of the radiolabeled peptide is calculated . Peptides are typically tested at six different concentrations covering a 100 , 000-fold dose range , and in three or more independent assays . Under the conditions utilized , where [label] < [MHC] and IC50 ≥ [MHC] , the measured IC50 values are reasonable approximations of the true Kd values . The IFN-γ ELISPOT assay was performed as described previously [18] . For determination of ex vivo frequencies of CD8+ T cells responding to Mtb infection or Mtb antigens , CD8+ T cells were positively selected from PBMCs using magnetic beads ( Miltenyi Biotec ) such that >97% of the cell population were CD8+ T cells . These CD8+ T cells were used as a source of responder T cells and tested in duplicate at four different cell concentrations ( 5 × 105 , 2 . 5 × 105 , 1 . 2 × 105 , and 6 × 104 cells/well ) . Autologous DCs ( 20 , 000 cells/well ) were used as APCs , and DCs were either infected with Mtb or pulsed with peptide pools ( 5 μg/ml , final concentration of each peptide ) and then added to the assay . For assays using T cell clones , T cells ( 1 , 000 or 5 , 000 cells/well ) were incubated with autologous LCL ( 20 , 000 cells/well ) in the presence or absence of antigen . Negative and positive controls were included in each assay and consisted of wells containing T cells and DCs either without antigen or without antigen but with inclusion of phytohemagglutanin ( PHA , 10 μg/ml; EMD Biosciences , http://www . emdbiosciences . com/ ) , respectively . For all assays , responding T cells were incubated with APCs overnight . To determine the ex vivo frequency of antigen-specific T cells , the average number of spots per well for each duplicate was plotted against the number of responder cells per well . Linear regression analysis was used to determine the slope of the line , which represents the frequency of antigen-specific T cells . The assay is considered positive , i . e . , reflecting the presence of a primed T cell response , if the binomial probability [62] for the number of spots is significantly different by experimental and control assays , i . e . , if the experimental line is statistically significantly different from the control line . To determine differences in ex vivo T cell frequencies between groups , Wilcoxon/Kruskal–Wallis analysis was used . Ex vivo T cell frequencies and MHC binding assays were performed exactly as described above .
The accession numbers from TubercuList ( http://genolist . pasteur . fr/TubercuList/ ) for Mtb proteins discussed in the manuscript are as follows: 19kd ( Rv3763 ) ; Ag85B ( Rv1886c ) , CFP10 ( Rv3874 ) ; ESAT 6 ( Rv3875 ) ; EsxG ( Rv0287 ) , Mtb8 . 4 ( Rv1174c ) ; Mtb9 . 9A ( Rv1793 ) ; Mtb39 ( Rv1196 ) .
|
CD8+ T cells are essential for host defense to intracellular bacterial pathogens such as Mycobacterium tuberculosis ( Mtb ) , Salmonella species , and Listeria monocytogenes , yet little is known about the antigens recognized by human CD8+ T cells in response to tuberculosis ( TB ) . TB , the disease caused by Mtb infection , remains one of the leading causes of illness and death worldwide and is a frequent complicating infection in individuals with HIV/AIDS . Therefore , we undertook this study to define commonly recognized CD8 Mtb antigens . First , we measured the frequencies of CD8+ T cells recognizing Mtb antigens known to be recognized by CD4+ T cell antigens in persons infected with Mtb . In addition , we identified the Mtb antigen and epitope recognized by several CD8+ T cell clones isolated from infected individuals . The epitope was presented to the T cell clones by an HLA-B allele in all but one case . We conclude that Mtb-specific CD8+ T cells are found in high frequency in infected individuals and are restricted predominantly by HLA-B alleles . These findings provide an improved understanding of how the human immune system recognizes intracellular pathogens and may contribute to the development of an effective TB vaccine and improved immunodiagnostics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"infectious",
"diseases",
"immunology",
"homo",
"(human)",
"eubacteria"
] |
2007
|
Immunodominant Tuberculosis CD8 Antigens Preferentially Restricted by HLA-B
|
Locomotion is driven by shape changes coordinated by the nervous system through time; thus , enumerating an animal's complete repertoire of shape transitions would provide a basis for a comprehensive understanding of locomotor behaviour . Here we introduce a discrete representation of behaviour in the nematode C . elegans . At each point in time , the worm’s posture is approximated by its closest matching template from a set of 90 postures and locomotion is represented as sequences of postures . The frequency distribution of postural sequences is heavy-tailed with a core of frequent behaviours and a much larger set of rarely used behaviours . Responses to optogenetic and environmental stimuli can be quantified as changes in postural syntax: worms show different preferences for different sequences of postures drawn from the same set of templates . A discrete representation of behaviour will enable the use of methods developed for other kinds of discrete data in bioinformatics and language processing to be harnessed for the study of behaviour .
Animal behaviour can be difficult to study because it involves complex movements that vary continuously in time . This is especially true for limbed animals such as insects and mammals , because they have many degrees of freedom and can adopt an even larger number of different postures . Detailed characterization without explicit posture quantification is possible [1] , but researchers commonly use alternative methods for controlling behavioural complexity . In ethology , complexity is often controlled through careful observation to identify a set of behavioural categories that can then be scored to assess differences between individuals , between species , and over time [2] . This has the advantage that observations can be carried out in an animal’s natural habitat where it is able to behave freely . However , the choice of categories may introduce unwanted bias and inter-observer variability can reduce reproducibility [3] . In laboratory-based studies it is possible to impose constraints on the animal , ranging from physically restraining some parts of the body to training an animal to perform a predefined task such as pressing a lever or solving a maze . Constraints reduce measurement dimensionality and can aid interpretation but may also mask variation which may be important for understanding the genetic or neural control of behaviour [4] . Here we adopt an increasingly common [5 , 6] intermediate approach in which we record animals in a laboratory environment , but with minimal constraints . Caenorhabditis elegans is a useful animal for addressing questions of behavioural representation because it has a simple morphology and its movement can be confined to the two-dimensional surface of an agar plate . It has also been characterized exhaustively in several relevant respects: in addition to a complete genome sequence [7] , we know the developmental lineage of all of its cells from the single-celled embryo to the adult [8] , and it has the most complete connectome—the wiring diagram of its neurons—of any animal [9] . These reference data are powerful in part because of their completeness , making it possible to characterize differences due to genetic , evolutionary , or environmental perturbations systematically . An analogous systematic characterization of C . elegans locomotion could also be a useful tool for studying behaviour . To move on the surface of an agar plate , C . elegans propagates bends along its body . It is not clear whether the locomotor wave is generated by a central pattern generator [10] , but there is evidence that once generated , waves are driven by proprioceptive coupling between body segments [11] . During navigation , C . elegans switches between bouts of forward and backwards locomotion [12] and uses both abrupt and gradual turns during directed behaviours [13 , 14] and when searching in a relatively featureless environment [15 , 16] . While performing these manoeuvres , C . elegans adopts postures in a low dimensional shape space [17] . However , knowing the dimensionality of the space still leaves open the question of which regions of shape space worms visit and which trajectories they tend to follow . The method we describe here is meant to address this question directly . We previously described a method of constructing a dictionary of behavioural motifs [18] , which are short patterns of repeated motion , and showed that such a dictionary could be useful for identifying and comparing mutant phenotypes in C . elegans . However , because motifs can overlap there will be significant redundancy in the representation before completeness is achieved . To solve this problem , we have developed an alternative method of characterising locomotion in terms of a sequence of discrete postures , which serve as building blocks for more complex motifs . We found that 90 distinct postures are sufficient to account for 82% of postural variance . A discrete representation enables efficient enumeration of postural sequences and makes it possible to address questions about behavioural plasticity in response to stimulation and natural variation in spontaneous locomotion . We found that freely moving worms have a core set of frequently used sequences and a much larger set of rarely used sequences . When worms adapt their behaviour in response to environmental conditions such as a lack of food or the presence of an attractive chemical , we find that they adjust the syntax of their locomotion ( i . e . their preference for different postural sequences ) , but that their shapes are still accounted for using the same set of template postures . Similarly , between populations of C . elegans isolated from different parts of the world , we find that they have a similar rank-frequency distribution of behaviours , but with significant variations in sequence preference .
We tracked single worms using a Dino-Lite USB microscope mounted on a motorized stage , as previously described [19] . Worms were segmented from the background using the Otsu threshold and the skeleton was calculated by tracing the midline along the worm body connecting the head and the tail . We then measured the tangent angle at 49 points equally spaced along the worm and subtracted the average angle to achieve a position and orientation independent measure posture [17] . These angles change as the worm bends its body during crawling . Although a worm’s posture varies continuously through time , at any given moment the current posture can be approximated by the closest matching template from a pre-defined set of postures . This is analogous to constructing a histogram , in which discrete bins are used to represent data drawn from a continuous distribution . The first step was to select a set of template postures that was as small as possible while still having enough elements to capture most of the observed variability . Instead of using uniformly spaced bins to define the templates we used k-means clustering , which attempts to minimize the distance between k templates ( the cluster centres ) and the data ( the set of input postures measured from wild-type worms crawling on an agar plate seeded with bacterial food ) . As a distance metric , we used the squared difference between the tangent angle vectors of two worm skeletons . As the number of template postures is increased ( k in k-means ) , the R2 between the original postures and their nearest neighbours improves ( Fig 1A ) . After 90 postures , the rate of improvement plateaus at a small value ( Fig 1A , inset ) . We therefore used 90 postures for subsequent analyses . The most common postures in this set of templates are sinuous and correspond to different phases of locomotion at different curvatures ( Fig 1B ) . The rare postures include more highly curved shapes that worms adopt during turns as well as straighter shapes that are occasionally observed during dwelling . The full set of 90 postures ordered by frequency is plotted in S1 Fig . Using this set of template postures , locomotion can be represented compactly . At any point in time , a worm’s posture is matched to its nearest-neighbour in the set of template postures and so a bout of locomotion can be simply recorded as a series of labels indicating which of the representative postures is closest ( Fig 1C ) . Repeated patterns of behaviour can now be found simply by finding repeating sequences of postures , including subsequences sampled from longer segments . It is also possible to match similar behaviours performed at different speeds by removing repeated elements from a sequence . For example , if a worm is moving slowly , its nearest-neighbour template posture might not change over several frames leading to a sequence with repeats such as {3 , 3 , 89 , 5 , 5 , 5 , 5 , 87 , 87 , 87 , …} ( see durations bar in Fig 1C ) . We reduce this sequence to {3 , 89 , 5 , 87} so that it will be recognized regardless of how slowly the transitions are made . We used this simple kind of non-uniform time warping for all of the sequence results presented in this paper . In a discrete representation , the behavioural repertoire can be enumerated directly by counting the variety of sequences that are observed . We do this by counting the number of observed n-grams . An n-gram is any sequence of n states , including overlaps . In the sequence {3 , 89 , 5 , 87} the bigrams are {3 , 89} , {89 , 5} , and {5 , 87} , the trigrams are {3 , 89 , 5} and {89 , 5 , 87} , and the sequence is itself a single 4-gram . For longer sequences , there are more possible n-grams but because of repeats , the number of unique n-grams will be less than the total number observed . There is therefore a sublinear growth in the number of unique n-grams as more sequences are observed and the degree of this difference is a measure of the structure in the sequence . Fig 2A shows how the number of unique n-grams , for n = 2–5 , grows with total sequence length for the laboratory strain N2 . Individual blue lines show data from 100 worms , each recorded for 15 minutes , randomly selected from the total data set of 1262 individuals . Orange lines show accumulation curves for sequences that have been shuffled to destroy temporal order but maintain the relative frequency of postures . The accumulation curves grow less than linearly , reflecting the fact that worm locomotion is repetitive . It is notable though , that even after over 100 000 postures have been observed , new unique trigrams are still being observed at a rate of approximately 1 in 25 ( or about 1 previously unobserved trigram every 12 seconds after a total observation time of 14 hours ) . This combination of repetitiveness with a large total repertoire can be seen more clearly in a Zipf plot of n-gram frequencies ( Fig 2B ) , which shows the frequency of sequences plotted against their rank on a log-log plot . The distribution is heavy-tailed in the sense that there are more high-frequency behaviours than would be expected for an exponential distribution with the same mean ( S2 Fig ) . This corresponds to a highly skewed usage of the behavioural repertoire with the top 1% of trigrams being used 32 . 2 ± 0 . 6% of the time during crawling on food ( mean ± standard deviation for the blue curves in Fig 2B ) . We used the OpenGRM NGram library [20] to model posture sequences as Markov chains , in which the probability of the next posture in the sequence is a function only of the previous n– 1 states . In the simplest case of a bigram model , the probability of the next state in the sequence depends only on the current state , but for worm locomotion , a bigram model does not capture the distribution of longer n-grams ( S3 Fig ) . In contrast , when we fit a trigram model using observed sequences and used it to generate new sequences , the simulated sequences had a similar distribution not only of trigrams , but also 4- and 5-grams ( Fig 2A and 2B , black lines ) . Increasing n to 4 or 5 improves the fit quality , but only modestly ( S2 Fig ) . We therefore chose to focus on trigrams for most of the subsequent analysis . To determine how worms adapt their behavioural repertoire in response to stimulation , we first used optogenetics to induce behavioural transitions at predictable times in freely crawling worms . We chose two strains for these experiments that produce robust behavioural responses to optogenetic stimulation . ZX46 worms were a useful starting point because they express channelrhodopsin under the control of the unc-17 promoter in the worm’s cholinergic neurons , and thus activation by blue light results in the simultaneous contraction of body wall muscles and an obvious change in locomotion . Previous experiments on this strain showed that activation leads to a deep dorsal bend [21] . Although dorsal bends are rare under normal circumstances , they are represented in the set of 90 N2 on-food postures and the fit quality during activation actually increases somewhat ( Fig 3A ) . This is because curved postures have a high total variance leading to an R2 , the fraction of explained variance , near 1 . Plots of relative posture probability over time ( Fig 3B ) show some postures that are over-represented during the stimulus and a large number of under-represented postures . A rank sum test comparing posture probabilities during and outside of stimulation periods shows that dorsal bends are over-represented ( Fig 3C ) during stimulation , as previously reported for this strain [21] . The same effect is also seen in the bigram probabilities , although there was insufficient power to detect over-represented trigrams . The under-represented postures are sinuous postures common during normal locomotion . This is due to a decrease in forward locomotion during photoactivation of the cholinergic neurons as revealed by the under-represented bigrams and trigrams . Photoactivation of AQ2026 worms , which express channelrhodopsin under the control of the sra-6 promoter , reliably induces an escape response through the activation of the ASH sensory neurons [22 , 23] . The behaviour is readily observed , but is more naturalistic than the deep dorsal bends seen in ZX46 worms since it occurs through activation of a sensory neuron that normally detects aversive stimuli and elicits an escape response . Again , the discrete representation fits the induced postures well ( Fig 3D ) . The plot of posture probability over time shows both over- and under-represented postures during and after stimulation ( Fig 3E ) . Escape responses in C . elegans typically consist of a reversal followed by a turn , which we can detect by splitting the analysis into three parts: outside stimulus , during stimulus , and 0–5 seconds after stimulus . During stimulation the bigrams and trigrams representing reversals are significantly more likely while forward locomotion is less likely ( Fig 3F ) and in the 5 seconds after stimulus , ventral turns and reversals are more likely while forward locomotion is still supressed . We next recorded worm locomotion in different environments to see how they adapt their behavioural repertoire in response to variation in sensory stimulation . To add to the data for worms on bacterial food , we also recorded N2 worms crawling on an agar surface with no food , and with no food but in the presence of the chemoattractant benzaldehyde [24] . N2 animals rarely leave a patch of food [25] and spend some time dwelling leading to a confined path ( Fig 4A ) . In the absence of food , worms show a clear switch in behaviour , spend more time roaming and initiate a local search behaviour consisting of reversals and turns . After longer periods of starvation , they show increasingly directed locomotion [15 , 16] . During chemotaxis off food , their change in behaviour compared to worms off food with no chemo attractant is more subtle; they still spend most of their time moving , but their paths are less random as they will , on average , move up the gradient of attractant [13 , 14] . We used the same set of 90 templates derived for N2 animals on food to fit the shapes of worms off food and during chemotaxis . The fit quality is no worse in either of the off food conditions , indicating that there is not an increase in outlying postures that are not captured by the template set ( Fig 4B ) . In fact , the poorly fit tail of the distribution is reduced off food compared to on food . The better fit may be due too noisier skeletonization of worms imaged on food and partly because worms off food are less likely to adopt some postures characteristic of pauses that are among the worst fitting postures ( S4 Fig ) . As a positive control , we also measured the distribution of fit qualities for a visibly uncoordinated mutant unc-79 ( e1068 ) and found a clear increase in outlying postures not captured by the wild type on food template postures . We also tested for differences in n-gram frequencies that worms performed in the different conditions by pooling their sequences and getting the complete set of unique n-grams observed across the three conditions , for n = 1–3 . We counted the number of occurrences of each n-gram in each condition and used a rank sum test to find sequences that were used more or less frequently in one of the conditions . We excluded n-grams that were observed less than 5 times in total in the two conditions being compared . Note that we did not exclude behaviours that were rare in only one of the conditions . For example , an n-gram with a count of 10 in one condition and 0 in another would still be included in the comparison . We corrected for multiple comparisons using an adjusted p-value threshold calculated to control the false discovery rate at 5% [26] . Using this procedure , we found that for the on food vs . off food comparison 377 trigrams were significantly more common in one condition , whereas for the subtler off food vs . chemotaxis comparison we found 11 . These differences in trigram frequencies are not entirely explained by differences in posture preference , since in shuffled sequences that preserve posture frequency differences , only 2 trigrams were found to be significantly different between the on and off food conditions and none were significantly different between the off food and chemotaxis conditions . Four examples of distinguishing trigrams are shown in Fig 4C . The first one is a bout of forward locomotion that is common in all conditions , but progressively less common in worms performing chemotaxis , crawling off food , and crawling on food . This is consistent with expectations for the persistence of locomotion in these conditions . The second trigram starts with the same posture as the first , but it appears in a different context . Instead of appearing during forward locomotion , the posture appears during a pause with a head swing . This change in context makes trigram 2 much less common in the off food conditions even though trigrams 1 and 2 share a posture in common . This implies that behavioural adaptation is at least partly characterized by shifts in postural syntax . Trigram 3 shows that behaviours that are rare in all conditions can still show significant differences in frequency and trigram 4 shows an example of a reversal which is more common on food than in either of the off food conditions , consistent with worms showing more persistent forward locomotion when searching off food . In addition to environmental variation , we also quantified the behavioural variation that arises over short evolutionary times between different populations of the same species . We tracked 17 strains of C . elegans isolated from different regions of the world and quantified their behaviour using the discrete representation . We found that the template postures derived from N2 worms on food could also fit the wild isolate data and that the heavy-tailed frequency distribution of trigrams is observed in all strains ( Fig 5A ) . The distribution is skewed with the top 1% of trigrams being used 38 ± 4% of the time during crawling ( mean ± standard deviation , n = 17 strains ) . We performed rank sum tests between all strain pairs to find trigrams with significant frequency differences using a p-value threshold adjusted to control the false discovery rate at 5% across all comparisons . There are trigrams with significant differences across the frequency spectrum; in fact most behaviours with a significant frequency difference are rare in at least one of the strains where the behaviours were found as a hit ( Fig 5B ) . Not all of the hits counted in the histogram in Fig 5B correspond to unique trigrams because some trigrams had significantly different frequencies in more than one strain ( Fig 5C ) . The number of unique trigrams that are different in at least one strain is 1258 , which is approximately 5% of the 2 . 3 x 104 distinct trigrams observed in the full wild isolate data set . The relative frequencies for these 1258 trigrams are shown in a heatmap in Fig 5D . Trigrams 25 and 26 show dorsal turns . 27 and 28 are also bent more dorsally than ventrally , but 27 represents forward locomotion while 28 represents a reversal . 469–471 are all bouts of forward locomotion , 691–693 are pauses with relatively straight postures . 1227 is a ventral turn and 1234 and 1235 are reversals .
We have developed a simple representation of behaviour as a series of discrete postures to systematically enumerate the behavioural repertoire of C . elegans during locomotion . The concept of a complete description of behaviour was motivated in part by similar efforts devoted to other aspects of worm biology , most notably the genome , cell lineage , and connectome . However , the repertoire of behavioural sequences cannot be said to be complete in a similar way . Although we have a complete record of the observed sequences , accumulation curves suggest that more recordings would continue to reveal novel sequences , even when considering behaviours with durations of about a second in an animal with a simple morphology in a relatively uniform environment . A notable feature of the observed repertoire is the simultaneous presence of repetition ( the heavily-used top 1% ) and variety ( the large set of relatively rarely used sequences ) . This feature may result from two demands on an animal’s behavioural repertoire: locomotion should be efficient in a given environment but must also be flexible to deal with unexpected conditions or to respond to new sensory information . The repertoires of 17 wild isolates of C . elegans have a similar rank-frequency distribution and we hypothesize that such heavy-tailed distributions will generalize beyond nematodes and will in fact be a widespread characteristic of animal behavioural repertoires . Some of the rarely observed sequences in the repertoire are surely due to measurement noise , but since the frequency of some rare behaviours is modulated in response to sensory stimuli ( for example behaviour 3 in Fig 4C ) , measurement noise is not the only factor . It should also be emphasized that although the repertoire is large in this representation , that does not preclude the existence of an alternative representation that will reveal an underlying simplicity . Indeed , seemingly simple nonlinear systems are well known to be able to produce complex dynamics [27] and the search for such models in the context of animal behaviour remains an exciting challenge [28–30] . The observation that only 5% of the trigram repertoire is modulated by natural variation between wild isolates ( Fig 5D ) may be a reflection of a small number of behavioural modules that can be adjusted genetically within the constraints of natural selection [31 , 32] . New imaging technology has made it possible to record neural activity from the majority of C . elegans neurons simultaneously in restrained animals [33] and during locomotion [34] . The usefulness of this data will depend to some extent on how the corresponding behavioural data is represented and quantified . This is also the case for optogenetically induced behavioural changes as reported here and many times previously in C . elegans ( e . g . [35–39] ) . Recent work in Drosophila larvae suggests that an unsupervised detection of locomotion patterns can be informative [40] . The discrete approach that we report here is also unsupervised in the sense that repeated patterns of behaviour are detected based on locomotion patterns observed within strains without initially using information about different classes ( environmental conditions or strains ) . A conceptually similar approach has proven to be useful for quantifying drug-induced behaviour in mice [41] . It is likely that no behavioural representation will be optimal for all studies , but that different representations will provide complementary insight . The advantages of trigram counting are that it is relatively unbiased , can reliably connect behaviours performed at different speeds , provides a complete record of all of the observed behaviours ( up to the resolution of the template postures ) , and that it can be summarized intuitively as frequencies of short behaviours that can be directly visualized . For some applications the variance that is missed by the discrete templates may be critical . One option would be to increase the number of templates , but it may also be that a different representation would be called for . Another limitation of the discrete representation as presented here is that we have not used the information contained in the duration that worms spend in each posture . The time that worms spend performing each behavioural sequence is relatively straightforward to incorporate and would increase the power of the representation to resolve differences in dynamics between strains . In the wild , animals must adapt to a variety of environments that can change unpredictably . One strategy for dealing with this complexity is to use a finite set of behavioural primitives that can be composed in different sequences to generate adaptive responses [42] . This is precisely the kind of variation that we observed for worms changing their behaviour in response to new environments ( Fig 4 ) . Using the same set of postures , they change their behaviour at the level of postural syntax . Compositionality in behaviour also suggests a mechanism for generating complex behaviours by combining primitive actions . Hierarchical composition has long been recognized as a principle of animal behaviour [43 , 44] and has also been proposed as an efficient representation for learning in robotics [42] . The possible connection between motor learning and compositionality raises the interesting question of how the repertoire observed in adults is acquired and modified during development .
Locomotion data was recorded for single worms as previously described [19] . For on food tracking , single worms from each strain were picked to the centre of a 25 mm agar plate with a spot of E . coli OP50 at the centre , allowed to habituate to the new plate for 30 minutes , and then recorded for 15 minutes . The worm side ( whether it was on its left or right side ) was manually annotated using a stereomicroscope before transferring plates to the tracking microscope . In addition to the previously recorded N2 and DR96 unc-76 ( e911 ) data , we used the following wild isolate strains: CB4852 , CB4853 , CB4856 , ED3017 , ED3021 , ED3049 , ED3054 , JU258 , JU298 , JU345 , JU393 , JU394 , JU402 , JU438 , JU440 , LSJ1 , and MY16 . For experiments off food , worms were briefly transferred to plates without food and then picked to the centre of a 55 mm agar plate and recorded immediately . Recording was stopped once worms reached the edge of the plate . To maximize the time of recording , worms in the chemotaxis assays were picked to a location near the edge of a 55 mm agar plate opposite the location of the attractant spot . 1 μl of the attractant , benzaldehyde ( diluted 1:100 in EtOH ) , was added to the plate 10 mm from the edge . The strains used for optogenetics were ZX46 zxIs6 [punc-17::ChR2 ( H134R ) ::yfp] V [21] and AQ2026 ljIs105 [sra-6::ChR2::yfp , unc-122::gfp][22] . Worms were transferred to retinal plates at the L4 stage , left at 20°C , and assayed either 24 hours later as adults ( AQ2026 ) or in the next generation ( ZX46 ) . Retinal plates were made by seeding NGM plates with OP50 mixed with all-trans retinal ( 100mM in ethanol ) in a ratio of 1000:4 . For channel rhodopsin activation , worms were illuminated for 5 seconds with 1 mW/mm2 blue light ( 440–460 nm ) using LEDs ( Luxeon III LXHL-PR09 from Lumileds , CA ) controlled by a Mindstorms LEGO Intelligent NXT Brick . Each worm was stimulated three times , with an interstimulus interval of 30 seconds . The skeleton of worms in each frame was found after segmentation by tracing the midline between the two points of highest curvature ( the head and tail ) of the largest connected component in the image ( the worm body ) and down sampling to 49 equally spaced points [19] . The skeletons were then converted to a position and orientation independent representation by measuring the angle between each of the 49 skeleton points and subtracting the mean angle [17] . Missing skeleton angles were linearly interpolated . Missing data resulted from camera dropped frames , stage motion , or failure to skeletonize because of poor segmentation or coiled worms . On average 13 ± 9% ( mean ± standard deviation ) of frames cannot be segmented with each of the three causes contributing approximately equally to missing data . 5000 skeletons were randomly selected from each of 20 randomly selected N2 worms recorded crawling on food . Worms were flipped so that all worms appear to be on their right side when viewed on the page . This means that dorsal and ventral turns are consistent across individuals during analysis . This set of skeletons was then clustered using k-means clustering to identify a set of representative template postures . k was varied from 5 to 500 to determine the value for k after which increasing k only marginally improves the quality of the fit . k was chosen as 90 for all subsequent analysis . For each worm video , we down sampled the data from 30 frames per second to 6 frames per second and then found the best matching template in each frame of the down sampled data . The best match was determined in each frame by finding the template posture with the minimum squared difference to the skeleton in that frame . Locomotion was then represented as a series of state labels between 1 and 90 . Repeated states were collapsed to a single state ( e . g . {1 , 1 , 2 , 2 , 2 , 5} would become {1 , 2 , 5} ) . For the optogenetics data , the frequency of each posture over time is averaged over three trials per worm and further averaged over the individuals in each experiment ( n = 26 for ZX46 and n = 34 for AQ2026 ) . Each row was smoothed with a moving average filter with a width of 1 . 3 seconds . The smoothed frequencies of each posture over time were then normalized by subtracting the mean and dividing by the standard deviation of each row to show relative up- and down-regulation over time . To aid visualization , the rows were then clustered vertically so that rows with similar temporal profiles were near each other . The clustering was agglomerative hierarchical clustering using Euclidean distance and average linkage . The clustering was performed independently for the data in Fig 3B and 3E so the rows in each plot do not correspond to the same postures . The n-gram language model analyses were implemented in C and Python using the OpenFST [45] and OpenGRM n-gram [20] libraries . Postural sequences from N2 worms on food were compiled into finite-state archives ( FAR ) and standard n-gram count models were built for n = 1–5 and normalized into probabilistic models without additional smoothing ( see e . g . [46] ) . For the simulation studies and for each n , 1000 random sequences were generated from the normalized finite-state models . Beginning at the start state an outgoing arc is chosen at random proportionally to the log-probability associated with the arc , the postural state is emitted and the process continues until an end state transition is reached . The total set of unique n-grams was determined for each strain or condition ( for the on food , off food , and chemotaxis set , the total set contained 14 209 trigrams and for the 17 wild isolates it contained 22 638 trigrams ) . Counts of unique n-grams in each individual were then used to compare strains or conditions to each other . For each comparison , any n-gram that was observed a total of 5 times or less in both groups being compared was ignored . For the remaining n-grams , counts were compared using a rank sum test and p-value thresholds were chosen using the Benjamini-Yekutieli procedure to control the false discovery rate at 5% [26] .
|
Technology for recording neural activity is advancing rapidly and whole-brain imaging with single neuron resolution has already been demonstrated for smaller animals . To interpret such complex neural recordings , we need comprehensive characterizations of behaviour , which is the principal output of the brain . Animal tracking can increasingly be performed automatically but an outstanding challenge is finding ways to represent these behavioural data . We have focused on the movement of the nematode worm C . elegans to develop a quantitative representation of behaviour as a series of distinct postures . Each posture is analogous to a word in a language and so we can directly count the number of phrases that makes up the C . elegans behavioural repertoire . C . elegans has a very small nervous system but we find that its behavioural repertoire is still complex . As with human languages , there is a large number of possible phrases , but most are rarely used . When comparing different populations of worms or worms in different environments , we find that the difference between their behaviour is due to a subset of their entire repertoire . In the language analogy , these would correspond to idiomatic phrases that distinguish groups of speakers . A quantitative understanding of the nature of behavioural variation will inform research on the function and evolution of neural circuits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Changes in Postural Syntax Characterize Sensory Modulation and Natural Variation of C. elegans Locomotion
|
Diagnosis of the neglected tropical disease , Buruli ulcer , can be made by acid-fast smear microscopy , specimen culture on mycobacterial growth media , polymerase chain reaction ( PCR ) , and/or histopathology . All have drawbacks , including non-specificity and requirements for prolonged culture at 32°C , relatively sophisticated laboratory facilities , and expertise , respectively . The causative organism , Mycobacterium ulcerans , produces a unique toxin , mycolactone A/B ( ML ) that can be detected by thin layer chromatography ( TLC ) or mass spectrometric analysis . Detection by the latter technique requires sophisticated facilities . TLC is relatively simple but can be complicated by the presence of other lipids in the specimen . A method using a boronate-assisted fluorogenic chemosensor in TLC can overcome this challenge by selectively detecting ML when visualized with UV light . This report describes modifications in the fluorescent TLC ( F-TLC ) procedure and its application to the mouse footpad model of M . ulcerans disease to determine the kinetics of mycolactone production and its correlation with footpad swelling and the number of colony forming units in the footpad . The response of all three parameters to treatment with the current standard regimen of rifampin ( RIF ) and streptomycin ( STR ) or a proposed oral regimen of RIF and clarithromycin ( CLR ) was also assessed . ML was detectable before the onset of footpad swelling when there were <105 CFU per footpad . Swelling occurred when there were >105 CFU per footpad . Mycolactone concentrations increased as swelling increased whereas CFU levels reached a plateau . Treatment with either RIF+STR or RIF+CLR resulted in comparable reductions of mycolactone , footpad swelling , and CFU burden . Storage in absolute ethanol appears critical to successful detection of ML in footpads and would be practical for storage of clinical samples . F-TLC may offer a new tool for confirmation of suspected clinical lesions and be more specific than smear microscopy , much faster than culture , and simpler than PCR .
Buruli ulcer , a neglected tropical disease caused by Mycobacterium ulcerans , occurs in marshy environments in scattered countries and regions on most of the world's continents [1] . While its mode of transmission remains uncertain , it is now known that the principal virulence factor is a secreted cytotoxic lipid , mycolactone [2] , whose synthetic enzymes are encoded on a giant plasmid [3] . Both features are unique among mycobacteria . Until nearly 10 years ago , the only accepted mode of treating the disease was to surgically remove the lesion and surrounding tissue followed by skin grafting [1] . Using a mouse footpad model developed in the 1950s [4] and applying the lessons of tuberculosis and leprosy chemotherapy , combination regimens of antibiotics were tested in the early 2000s [5]–[7] . The most effective treatment was found to be a combination of rifampin ( RIF ) , an oral drug used for treatment of most mycobacterial infections , and streptomycin ( STR ) , an injectable drug originally used to treat tuberculosis . Subsequent clinical studies [8]–[11] supported the efficacy of this regimen and resulted in the replacement of surgery with antimicrobial treatment in most programs around the world [1] . Different forms of mycolactone are produced by M . ulcerans in different locales . They can be detected by mass spectrometry , cytotoxicity assays , or thin-layer chromatography ( TLC ) [12]–[17] . The major mycolactone is mycolactone A/B [18] . Total synthesis of the mycolactones was demonstrated [19] , [20] and synthetic mycolactone A/B has been made available for research purposes . Seeking to improve the TLC method by reducing background spots , Spangenberg and Kishi [17] developed a boronate-assisted fluorescent-TLC ( F-TLC ) method in which there is a marked reduction of background spots . The TLC plate is developed by immersion in a boronic acid acetone solution that binds to mycolactone and fluoresces on excitation by ultraviolet light . Interestingly , this method is specific to detect human mycolactones , but not fish or frog mycolactones , as well as some unknown contaminants . Unpublished results indicated that synthetic mycolactone spiked into tissue could be detected by F-TLC but detection in clinical samples was often problematic , probably due to the storage method of samples . These results prompted further development and refinement of the F-TLC assay , which is reported here . The mouse footpad model has the advantage of a readily visible and progressive swelling of the infected foot [1] , [5]–[7] . Harvested footpads can also be processed for enumeration of M . ulcerans colony forming units ( CFU ) by culture on mycobacterial media for up to 12 weeks at 32°C . Previous studies documented histological and microbiological changes after infection as well as changes in toxin levels in frozen footpads before and after treatment with RIF+STR [21]–[26] . Among the goals of the World Health Organization Global Buruli Ulcer Initiative is to find an all-oral regimen , in other words to replace STR with an oral alternative such as clarithromycin ( CLR ) [27]–[29] . In the current study , we documented weekly changes in footpad swelling , CFU counts , and toxin levels in infected mouse footpads before and after treatment , comparing the efficacy of RIF+STR and RIF+CLR . After preliminary studies with convenience samples , we were able to show that mycolactone may be best preserved not by freezing but by storage in absolute ethanol , a finding that could also be of practical benefit under field conditions .
M . ulcerans 1615 ( Mu1615 ) , an isolate originally obtained from a patient in Malaysia in the 1960s [30] , was kindly provided by Dr . Pamela Small , University of Tennessee . According to Dr . Small ( personal communication ) , this strain is a stable producer of mycolactone A/B whereas modern African strains often lose the capacity to produce mycolactone unless passaged in mice ( Drs . Stewart Cole and Laurent Marsollier , personal communication to JHG ) . Previous studies have confirmed that this strain produces mycolactone A/B and kills macrophages and fibroblasts [24] , [31] , [32] . The strain was passaged in mouse footpads before use in these studies . The bacilli were harvested from swollen footpads at the grade 2 level , i . e . , swelling with inflammation of the footpad [6] . All animal procedures were conducted according to relevant national and international guidelines . The study was conducted adhering to the Johns Hopkins University guidelines for animal husbandry and was approved by the Johns Hopkins Animal Care and Use Committee , protocol MO11M240 . The Johns Hopkins program is in compliance with the Animal Welfare Act regulations and Public Health Service ( PHS ) Policy and also maintains accreditation of its program by the private Association for the Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) International . RIF and STR were purchased from Sigma ( St . Louis , MO ) . CLR was kindly provided by Abbott ( Abbott Park , IL ) . STR and RIF were dissolved in distilled water , and CLR was dissolved in distilled water with 0 . 05% agarose for administration to mice . All drugs were administered 5 days per week in 0 . 2 ml . RIF ( 10 mg/kg ) and CLR ( 100 mg/kg ) were administered by gavage . STR ( 150 mg/kg ) was administered by subcutaneous injection . BALB/c mice , age 4–6 weeks ( Charles River , Wilmington , MA ) , were inoculated in the right hind footpad with approximately 4 . 54 log10 ( 3 . 45×104 ) CFU of Mu1615 in 0 . 03 ml PBS . Footpads were harvested weekly from 8 mice ( 5 for CFU count , 3 for ML detection ) at different time points after infection ( Table 1 ) and before treatment , up to ≥grade 3 swelling . After the onset of grade 2 swelling ( week 6 ) , treatment with RIF+STR or CLR was administered for 5 weeks ( week 11 after infection ) . Groups of treated mice were also sacrificed for these analyses . Footpad tissue was harvested , minced with fine scissors , suspended in 2 . 5 ml PBS , serially diluted , and plated on Middlebrook selective 7H11 plates ( Becton-Dickinson , Sparks , MD ) . Plates were incubated at 32°C and colonies were counted after 10 weeks with a final determination at 12 weeks of incubation . GraphPad Prism 4 was used to compare group means by student's T test and analysis of variance and linear regression analysis for comparison of slopes and intercepts
The most rapid but least specific method of assessing M . ulcerans infection in human disease is to check for typical lesions . In the mouse model where infection time is known , the method is straightforward and well documented [6] , [24] , [27] , [31] . Figure 2 shows a detailed assessment of swelling progression with weekly observations . Swelling was suggested as early as 4 weeks after infection with unambiguous enlargement ( grade 1±0 . 25 ) of footpads at week 5 . The average footpad-swelling grade increased to level 2 ( 2 . 25±0 . 66 ) at week 6 and increased again to grade 3 ( 3 . 17±0 . 38 ) at week 7 . At week 8 when the average reached 3 . 5±0 . 00 , mice were sacrificed per protocol . The contralateral uninfected footpads showed no indications of swelling throughout the experiment . What was unknown in this time course was whether swelling is preceded by the presence of detectable mycolactone or if the number of organisms present in the footpad determines swelling . In this experiment , footpads stored in absolute ethanol were shipped overnight to the chemistry lab and quantitative mycolactone A/B results were determined within 24–48 hours of footpad harvest . As shown in Figure 3 , mycolactone was detectable at ∼11 ng ( ∼7 . 5 ng×50/35 ) /footpad at week 4 , one week before the observation of unambiguous footpad swelling . The amount increased to 26±2 ng ( ( 18±2 ng ) ×50/35 ) /footpad at week 5 , 31±2 ng ( ( 22±2 ng ) ×50/35 ) at week 6 , the beginning of treatment , 40±2 ng ( ( 28±3 ng ) ×50/35 ) at week 7 , and 49±4 ng ( ( 34±3 ng ) ×50/35 ) at week 8 in untreated mice . These results indicate that the mycolactone toxin is present in “pre-clinical” lesions and can be detected in footpad tissue extracts by fluorescent TLC . Using quantitative culture at 32°C on selective Middlebrook 7H11 plates , countable colonies were present only after 10 weeks . At baseline , on day 1 after infection the CFU counts were 3 . 45±0 . 34 log10 per footpad . There was a gradual increase weekly with the CFU burden being 3 . 65±0 . 17 , 4 . 26±0 . 18 , 4 . 50±0 . 26 , 4 . 92±0 . 19 , and , at the time of detectable footpad swelling , just over 5 log10 at 5 . 20±0 . 10 log10 per footpad at weeks 1 , 2 , 3 , 4 , and 5 , respectively , after infection ( Figure 4 ) . After initial footpad swelling , there was a further increase in M . ulcerans CFU to 5 . 96±0 . 26 log10 at which point there was a plateau with counts at subsequent weeks ( 7 and 8 ) being 6 . 13±0 . 28 and 6 . 23±0 . 41 log10/footpad , respectively . However , swelling and mycolactone production continued to increase . From these data , we conclude that footpad swelling only occurs after there are approximately 5log10 organisms in the footpad and that bacterial multiplication increases only slightly after that time while footpad swelling increases dramatically .
The described developments in the fluorescent TLC procedure for the detection of mycolactone A/B in mice infected with M . ulcerans may have practical implications . This detection technique for the unique toxin of M . ulcerans may enable simpler and earlier specific diagnosis of Buruli ulcer in humans . Acid-fast microscopy for detection of M . ulcerans is also relatively rapid but lacks both sensitivity and specificity and histology requires expertise often not present in endemic areas . Molecular tests can also be applied but have similar limitations though PCR is relatively sensitive [33] , [34] . The most sensitive and specific method of detection is culture at 32°C on microbiological media but it requires up to 8 weeks for detection and up to 12 weeks for quantification in this model [24] , [33] , [35] and is prone to contamination . Here , detection of mycolactone , a specific marker of M . ulcerans , was achieved within days of tissue harvest and even before the presence of a lesion ( i . e . , footpad swelling ) in the mouse model . Compared to our previous studies [24] with frozen footpads and mycolactone detection by mass spectrometry , storage in absolute ethanol appears to be the key for detection at earlier phases of the infection and to increase sensitivity of TLC . Ethanol is also a more practical option than deep-freezing in the field . Experiments comparing ethanol with iso-propanol and ethyl acetate as the storage medium indicated that iso-propanol might be slightly superior to ethanol but that ethanol is clearly superior to ethyl acetate ( unpublished observations ) . The differences were principally in the degree of diffusion of the spots . In footpads not kept in any of these solvents , there was almost complete elimination of detectable mycolactone . We have also more precisely determined the relationship between footpad swelling , toxin production , and bacterial multiplication . Mycolactone A/B can be detected before the onset of footpad swelling when there are <105 bacilli per footpad and swelling occurs when there are >105 bacilli in the footpad . As observed previously [24] , there is a plateau in bacillary numbers soon after the onset of swelling , even though swelling continues to increase . Unlike the CFU counts , toxin concentrations continue to increase as swelling increases . It remains to be determined how this dynamic interaction occurs in human lesions , which are not at all as circumscribed as those in the mouse . In humans , it will also be important to determine the best sites within lesions and the best techniques ( e . g . , swab or fine needle aspirate ) to obtain toxin-containing specimens . The impact of drug treatment with RIF-STR on footpad swelling and cultivable M . ulcerans was confirmed in these experiments . Importantly , we also observed a reduction in detectable mycolactone after the onset of drug treatment . In all three cases , the observations were made on a weekly basis providing further precision to the observations . We also found that the all-oral alternative RIF+CLR regimen for Buruli ulcer treatment , though possibly less bactericidal , appears to be equally active as the standard RIF+STR regimen in reducing swelling and blocking toxin production . Thus , the inhibition of the enzymatic machinery involved in producing a virulence factor may be as effective as and possibly less toxic than the killing of the bacteria . The fluorescent TLC method could be an excellent tool for both the rapid and early detection of M . ulcerans infection and for monitoring the response to antimicrobial chemotherapy . The assay is practical in that absolute ethanol is readily available in all clinics and mycolactone is preserved in absolute EtOH at room temperature for at least 3 weeks ( data not shown ) . The assay should be practical in intermediate level laboratories , thus facilitating confirmation of diagnoses before the onset of ulceration or soon after the onset of treatment .
|
The diagnosis of Buruli ulcer , caused by infection with Mycobacterium ulcerans , is complicated by its resemblance to other diseases that may also cause ulcers in the skin . Clinical diagnosis can be supported by microscopic detection of acid-fast bacilli in the skin , by prolonged culture of at least 8 weeks , in a dedicated incubator set at 32°C , or by the polymerase chain reaction in a well-equipped laboratory usually far from the clinic where the patient comes for treatment . The treatment involves taking two drugs , one requiring injections , every day for two months , a burden for patients and their families . Since all drugs may have side effects , it is important that the treatment be appropriate for the patient's disease . We describe a new technique to rapidly and inexpensively detect the presence of the unique toxin produced by M . ulcerans in the mouse footpad model of Buruli ulcer . We show that the toxin can be detected in footpads before the development of signs of the disease , that more toxin is produced as the disease progresses , and that toxin levels decline in mice treated with either the current standard regimen of rifampin and streptomycin or a proposed all-oral drug regimen of rifampin and clarithromycin .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2014
|
Accelerated Detection of Mycolactone Production and Response to Antibiotic Treatment in a Mouse Model of Mycobacterium ulcerans Disease
|
Philadelphia chromosome-positive ( Ph+ ) acute lymphoblastic leukemia ( ALL ) is characterized by a very poor prognosis and a high likelihood of acquired chemo-resistance . Although tyrosine kinase inhibitor ( TKI ) therapy has improved clinical outcome , most ALL patients relapse following treatment with TKI due to the development of resistance . We developed an in vitro model of Nilotinib-resistant Ph+ leukemia cells to investigate whether low dose radiation ( LDR ) in combination with TKI therapy overcome chemo-resistance . Additionally , we developed a mathematical model , parameterized by cell viability experiments under Nilotinib treatment and LDR , to explain the cellular response to combination therapy . The addition of LDR significantly reduced drug resistance both in vitro and in computational model . Decreased expression level of phosphorylated AKT suggests that the combination treatment plays an important role in overcoming resistance through the AKT pathway . Model-predicted cellular responses to the combined therapy provide good agreement with experimental results . Augmentation of LDR and Nilotinib therapy seems to be beneficial to control Ph+ leukemia resistance and the quantitative model can determine optimal dosing schedule to enhance the effectiveness of the combination therapy .
The persistence of chemo-resistant leukemia-initiating cells in Philadelphia-chromosome positive ( Ph+ ) B-cell Acute Lymphoblastic Leukemia ( B-ALL ) in the bone marrow is a primary mechanism responsible for disease relapse , following treatment , which occurs in the majority of patients . B-ALL is due , in part , to chromosomal translocations ( 9;22 ) that result in the generation of a BCR-ABL fusion protein , which fosters the transformation of immature B cells [1] . BCR-ABL+ ( i . e . , Ph+ ) leukemia has a poor prognosis; this is particularly true when matched with deletions in Cdkn2a , the gene encoding the tumor suppressor protein ARF , which occurs frequently in B-ALL [2 , 3] . A significant breakthrough in the treatment of Ph+ ALL as well as the treatment of chronic myeloid leukemia ( CML is associated with p210 isoform , whereas ALL is associated with p190 isoform ) was the development of the tyrosine kinase inhibitor ( TKI ) Imatinib [1] ) . This drug , and the more potent second generation drugs Dasatinib and Nilotinib , are able to selectively inhibit the BCR-ABL mutant protein and thus significantly reduce Ph+ cell counts [2 , 4] . While TKI therapy has long-term efficacy in the treatment of CML , most ALL patients eventually relapse following treatment with TKI due to the development of resistance [5 , 6 , 7 , 8] . Thus a common treatment protocol for ALL patients is TKI therapy until the first remission [9 , 10] followed by stem cell transplantation . However , since stem cell transplantation itself carries many risks to patient survival , the ability to extend the efficacy of TKI therapy in Ph+ ALL patients is of great clinical interest . Combination therapy such as Nilotinib with inhibitors of various other pathways ( MEK , AKT , and JNK ) showed greater reduction in cell viability and lowered risk of resistance [11] . Ionizing radiation has been used for leukemia disease in limited cases , e . g . i ) disease involve in the central nervous system ( CNS ) , potential due to ineffective penetration of chemotherapy to CNS [12] , ( ii ) conditioning regimen with high doses of radiation and chemotherapy prior to stem cell transplantation for patients with high risk of relapse [13] . Taking advantage of leukemia radiosensitivity and the benefit of low dose radiation ( LDR ) in preserving bone marrow functions , we investigated whether the combination of Nilotinib and low dose radiation will be more effective treatment for BCR-ABL+ ( i . e . , Ph+ ) leukemia over Nilotinib alone . Furthermore , to optimize the effectiveness of this combination treatment , we developed a mathematical model , parameterized via cell viability experiments under Nilotinib treatment and radiation exposure , to predict cellular response to the combination therapy . The optimized mathematical model predicts a synergy between LDR and TKI treatment . We propose a combined Nilotinib dose-response function after LDR that accounts for a possible synergistic interaction between LDR and TKI treatment . Model parameters are obtained from in vitro viability measurements in the absence of TKI ( Fig 1 ( a ) ) , with zero LDR ( Fig 1 ( b ) ) and combination of LDR and TKI for several radiation doses ( Fig 1 ( c ) ) . The model is validated by precise prediction for the drug-dose responses and radiation-dose response to combination LDR and TKI treatment . It is important to emphasize that our model is focused on the relevance of LDR to prevent small-molecule inhibitor drug resistance . It does not address the efficacy of a successful radiation-drug combination treatment . Answering the question that whether the resistance is pre-existing , selected for or evolves de novo is out the scope of the current work . We do assume a small fraction of pre-existing Nilotinib resistant subtype , as well as possibility to transform into resistant type in the presence of the drug .
Our computational model is a coupled system of ordinary differential equations representing two populations . A Nilotinib sensitive and a Nilotinib resistant population . The use of ordinary differential equations is very common to describe the population dynamics and emergence of a new trait [15 , 16] Logistic terms impose limitation on growth of each population , and an additional term allows for conversion from Nilotinib sensitive to Nilotinib resistant phenotype in the presence of non-zero concentration of the drug . More specifically , we assumed the following for the dynamics of the two subpopulations of the sensitive and resistant phenotypes: The dynamical model that describes the above mechanisms is detailed in the Supplementary Information ( SI ) . We refer to this model as the proliferation-mutation model . To identify the response of Ph+ ALL cells to Nilotinib treatment , radiation , and the combination of both therapies , we proposed a simple functional form of this dependence and evaluated the fit against a large set of dose response data from experiment . This simple linear dose-response model can be written as follows: ( proliferation rate ) = r 0 - ( Nilotinib dose ) × r 1 + radiation dose × r 2 , ( apoptosis rate ) = d 0 + ( Nilotinib dose ) × d 1 + radiation dose × d 2 , ( 1 ) where the growth rate coefficients ri and di are constants that need to be identified based on the experimental results . It is common to model dose response functions using a Hill function structure ( 3- or 4-parameter logistic function ) . The Hill function imposes a low level of drug efficacy at low doses and a saturation of drug efficacy at high doses . A linear response is most accurate to approximate a Hill function dose-response near IC50 does—which is the case here . In the SI section , we show that how the above linear dose-response functions can be derived from a more common Hill -function . Eq ( 1 ) can be rewritten as r S = r S , 0 − ( r S , 1 + r S , 2 D ) c , r R = r R , 0 − ( r R , 1 + r R , 2 D ) c , d S = d S , 0 + ( d S , 1 + d S , 2 D ) c , d R = d R , 0 + ( d R , 1 + d R , 2 D ) c , ( 2 ) where c and D represent the Nilotinib and radiation doses respectively . The constants rS , 0 and rR , 0 denote the proliferation rates of sensitive and resistant populations in the absence of therapy , and dS , 0 and dR , 0 denote the death rates of sensitive and resistant populations in the absence of therapy . The coefficients rS , 1 , rR , 1 represent the dose-response relationship between Nilotinib and proliferation rate of sensitive and resistant cells , respectively . Similarly , the coefficients dS , 1 , dR , 1 describe how Nilotinib impacts the death rate of sensitive and resistant cells , respectively . Lastly , the coefficients rS , 2 , rR , 2 , dS , 2 , dR , 2 determine the strength of the radiation-drug interaction on proliferation and death rates sensitive and resistant cells , respectively . These coefficients were fit to experimental data sets studying proliferation and death rates at a variety of Nilotinib and radiation doses . For example , non-negligible positive fitted values of rS/R , 2 and dS/R , 2 reveal synergistic interaction between the therapies . We also incorporated an immediate cell-kill term after each radiation dose in accordance with the standard linear- quadratic model [17 , 18 , 19 , 20] . According to the LQ model the effects of radiation cell kill is given by the survival fraction after the radiation exposure Surviving fraction=exp [ − α × ( radiation dose ) − β × ( radiation dose ) 2 ) ] ( 3 ) where α and β are the radio-sensitivity parameters to be determined from the experimental data . In short , α represents the rate of cell kill due to single tracks of radiation and β represents cell kill due to two independent radiation tracks . The linear quadratic model is widely used due to its excellent agreement with empirical data for a wide range of radiation doses . In SI section we explain how to incorporate the linear quadratic framework for surviving fraction into our mutation-proliferation model . The above mathematical framework can predict the population fraction ( cell viability ) at each day given the initial values of Nilotinib and irradiation doses . We fit the model parameters in an iterative fashion . All parameter estimation are obtained by finding optimal parameter set that minimizes the square root distance of solution of Eq . S3 ( in SI ) with respect to the time series viabilities at days 0 , 3 , 6 , 8 , 10 . The steps are as follows:
We first measured the time-dependent cell viability of Ph+ ALL cells in vitro in response to Nilotinib , radiation , and combination therapy with both Nilotinib and radiation ( Fig 3 ( a ) ) . For the radiation-only arm , we observed an initial large reduction inviability by day 6 for 2 Gy and day 8 for 4 Gy . Experiments using Nilotinib monotherapy showed an incremental reduction in cell viability over the first 6 days to about 57 . 2% . Subsequently the cell populations began to develop a resistance to the drug and viabilities began to increase . When used in combination , Nilotinib + radiation induced a more effective initial cell killing and the cancer cell population was controlled at very low numbers ( under 10% viability ) for the duration of the experiment . In other words , there was nearly no cell population recovery , and resistance to Nilotinib was not developed . Under the Nilotinib + 4 Gy treatment arm , the cell population was entirely eliminated . Thus , the combination therapies were able to not only elicit a larger reduction in cell population , but also to maintain control of low cell viabilities without the development of resistance over a longer time period . The use of Triciribine was able to keep the cell viability as low as Nilotinib+ radiation group ( Fig 3 ( b ) ) . The radiation dose-response assay revealed an LD50 of 2 Gy in the absence of Nilotinib , and 0 . 5 Gy in the presence of 18 nM Nilotinib . The Nilotinib dose-response assay revealed an IC50 oof 18nM in the absence of radiation . To investigate how the combined therapy provides the synergistic effect , we evaluated one of the key pathways associated with cell proliferation , and chemoresistance . At day 3 after treatment , p-AKT was slightly increased by adding either Nilotinib or radiation ( Fig 4 ( a ) ) . However , the expression level was gradually decreased at large dose of radiation ( 6 Gy ) with or without Nilotinib ( Fig 4 ( b ) ) . Notably , the combined therapy eventually ( day 7 and10 ) decreased the p-AKT even at 2 Gy ( Fig 4 ( c ) ) . Using the numerical solutions of the proliferation-mutation model optimized for the best fit with experimental data points , we determined the coefficients in the Nilotinib dose-response function , ri and di , as well as the transformation rate ( Table 1 ) . The optimized coefficients indicate an increase in fitness of resistant cells relative to that of sensitive cells in the presence of Nilotinib . ( Fitness is defined as the difference between division rate and apoptosis rate in the cell population . ) Using a numerical sensitivity analysis , we confirmed that these parameter estimates are robust to perturbations in the initial frequency of resistant cells in the population . Since the experiments demonstrated an initially strong sensitivity of the populations to Nilotinib treatment , we set the frequency of resistant cells to be small . We set this to be 0 . 1% of the total population for the remainder of investigations . The optimized parameters are reported in Table 2 with the carrying capacity set to K = 4 . 2 in all cases . These results indicate the presence of synergistic effects between Nilotinib and radiation . In particular , the radiation tyrosine-kinase inhibitor interaction was strongest in the resistant population . This is not surprising as in the presence of Nilotinib the sensitive population is already highly disadvantaged in terms of growth . The value of transformation rate ν in this case is independent of radiation dose and is higher than the respective transformation rate in the Nilotinib-only case suggesting that radiation exposure contributes to the production of new resistant cells . The results for population fractions ( cell viabilities ) as a function of time ( days ) were plotted in Fig 5 . The agreement between mathematical model predictions and the in vitro measurements suggests that the proposed radiation-drug interaction term in the linear dose-response is a plausible choice . All the above results of the parameter estimation process are summarized in the Tables 1 and 2 . We next tested predictions of the fitted model with an independent set of experimental results . In particular , we compared model predictions to the experimental results of the dose-response assays for Nilotinib ( in absence of radiation ) and radiation ( at 0 and 18 nM Nilotinib ) . See Fig 6 for these comparisons . As can be seen , the results are in very good agreement . We then used the model to predict the Nilotinib dose-response curve with 1 , 2 , 3 and 4 Gy radiation . The results are plotted for 0-22nM of Nilotinib . Note that at higher doses we see a discrepancy with the experiment ( 25nM 0Gy ) . This is due to the fact that the linear approximation of the Hill dose-response curve only applies to the initial section of the curve and is expected to break down at higher doses . The combination therapy above was shown to reduce tumor cell viability , and through computational techniques an optimal schedule of dosing may be approached . Given the potential toxicity of Nilotinib to normal tissues , we first assumed a standard , constant dose of 18 nM Nilotinib . Next , we considered a five-day radiation treatment protocol , where the summed dose of the protocol is constant . As an example , we use a total dose of 2Gy; which , as can be seen in Fig 5 , has room for improvement with the combination therapies and does not irradiate the cells to a viability that is not interesting mathematically ( 4Gy in Fig 5 , for example ) . The aforementioned result will serve as a comparison for our proposed protocol , which will attempt to minimize the total tumor cell viability at day 10 . The control parameters are the radiation doses given at days 0 , 1 , 2 , 3 and 4 denoted by Di ( i = 1 , 2 , 3 , 4 , 5 ) . Placed under the constraint of the total allowable dose , the control parameters were varied by a nonlinear constrained minimization protocol that sought to minimize the tumor cell viability at day 10 . Under this minimization protocol , the minimum tumor cell viability was determined by searching the potential dose regimen in the answer space and producing the dosing protocol that best minimized the tumor cell viability at 10 days . This resulted in an optimal dosing schedule of ( in Gy ) D1 = 0 . 9371 , D2 = 0 . 5139 , D3 = 0 . 6445 , D4 = 0 . 045 , D4 = 0 . 0064 , which front-loads the radiation in the first three days . Notice that the negligible value for D4 indicates that optimal solutions are effectively that of a 4-fraction protocol , with variable doses per fraction . In between any two radiation doses , the cells undergo repopulation . For acute radiation protocols , the linear dose response function predicts a change in proliferation potentials of both sensitive and resistant cells which depends on the total radiation dose at day 0 . For a fractionated protocol , we assume the the proliferation potentials of the two cell types between the kth and k+1th radiation doses depends on total radiation dose until fraction k . The model prediction for total cell viabilities versus time for the optimal fractionation protocol is plotted in Fig 7 and compared with a constant 5-fraction radiation protocol ( Di = 0 . 4 ) and 2Gy acute radiation treatment ( D1 = 2Gy , D2 = D3 = D4 = D4 = 0 Gy ) . At larger time , that is , greater than 10 days , the optimal protocol has the lower cell viability more importantly in a downward trend beyond the 10 day point , whereas the acute treatment begins an upward trend and the constant fractionation protocol does not suppress the cell viability to the levels of the optimal or acute protocols . This suggests the synergistic interactions between the therapies is heightened with larger initial doses of fractionated radiation , and the optimal protocol for long term suppression falls between acute dosing and constant fraction protocols , which results in the front-loading protocol . This dose dependent fractionation may be generalized to various scenarios in the clinical setting as determined by clinical status . Further , the computational model may be extended to consider alternative Nilotinib dosing strategies in combination of the radiation protocol to minimize the cell viability at day 10 . Concurrent use of the 5-day optimal fractionation protocol with a varied Nilotinib dosing strategy shows an alternative , though equally efficacious ( similar cell viability at 10 day ) strategy . This alternative Nilotinib dosing strategy maintains the average daily dose of 18 nM , however , it begins at lower concentration and increases throughout the course of treatment . Specifically , the Nilotinib dose over the first 3 days is 10 nM , followed by 18 nM for 4 days , and finally 26 nM for 3 days , essentially back-loading the Nilotinib onto the irradiated cells . After 10 days , the dose returns to 18 nM daily . This protocol is visualized in Fig 7 as well , where at larger times the new strategy of Nilotinib dosing reduces the cell viability as well as the previous protocol . This is clinically relevant as dose titration is common , if needed , and there is no trade off with treatment efficacy should this dosing schedule be indicated .
Although the clinical outcome of BCR-ABL leukemia has been improved with the advent of TKI therapy , overcoming chemo-resistance has been a major challenge . This study demonstrated that low dose irradiation combined with Nilotinib provided enhanced and prolonged efficacy for leukemic cells in vitro . A companion theoretical model provided good agreement with experimental results with an opportunity for further optimization to enhance treatment efficacy . We explored here the possibility of using low dose radiation as a first line therapy in combination with chemotherapy to enhance the effectiveness controlling BCR-ABL leukemia . There are mainly two reasons for such a combination therapy approach: a ) advent of image guided targeted radiation allow more focused dose delivery [21 , 22 , 23] b ) known radiosensitivity of leukemia but not known if low dose radiation with chemotherapy could be an effective alternative to control over the TKI drug resistance . We found that combining low dose of radiation with a commonly used TKI drug could not only substantially reduce cell population , but also maintain low cell viabilities without the development of resistance over a longer time period . To elucidate the mechanism for which low dose radiation provide beneficial effect , we investigated the role of AKT pathway using western blot analysis ( Fig 4 ) . Our results revealed that high dose radiation ( i . e . 6 Gy ) reduced the phosphorylation of AKT . Interestingly , even low dose radiation was adequate to inhibit the phosphorylation of AKT when Nilotinib was also used . To support of this , the cell viability of chemo-resistant cells ( with increased p-AKT ) was significantly reduced when AKT inhibitor ( Triciribine ) was used in combination with Nilotinib . These results suggest that radiation can play roles not only in a conditioning regimen before stem cell transplant but also an alternative for an AKT inhibitor . In other words , low dose radiation therapy could be an alternative treatment option for AKT inhibitor in ALL patients . To our best knowledge , this is the first report that demonstrates the promising combined efficacy of Nilotinib and radiation for ALL , with minimum damage to vital organs . Because of low dose with limited toxicity , it can be delivered to the whole body . Since our study was performed in normal cell culture , however , in vivo study is required to take into consideration of microenvironmental factors . We also constructed a dynamical model to explain the observations and to predict response to additional combination therapy schedules . To tease apart the responses of Ph+ ALL cells to Nilotinib , radiation , and the combination of both therapies , we proposed a simple functional form of the combination dose-response relationship and evaluated the fit against a large set of dose response data . In particular , we proposed a simple Nilotinib dose-response function in which radiation dose may alter the strength of the Nilotinib response in a dose-dependent fashion . Parameter-fitting revealed an optimal parameter set that showed very good agreement with experimental results . Analysis of this optimal parameter set revealed dose-dependent synergistic effects between Nilotinib and radiation response . In particular the radiation tyrosine-kinase inhibitor interaction is strongest in the resistant subpopulation of cells . Our analysis also demonstrated that the model predictions are robust to variation in the initial frequency of resistant cells . We compared fitted model predictions with an independently generated second set of experimental data and found good agreement . We next utilized the validated model to investigate optimal combination strategies for Nilotinib and radiation in Ph+ ALL . As a simple test , we assumed a standard 18 nM Nilotinib dose and investigated strategies allowing up to 2 Gy over the course of a 5-day treatment . We determined that the optimal therapeutic schedule , given these constraints , spread most of the radiation dose over the first three days . Thus , a ‘sweetspot’ exists between acute radiation protocols and constant treatment protocols . These results suggest a promising direction for investigation of new treatment strategies in Ph+ ALL and for providing an optimal treatment regimen . However , we note that all experiments ( and thus parametrization of the model ) was done in vitro . Further in vivo studies are needed to determine treatment schedules for the clinic with more accuracy . To summarize , augmentation of LDR-Nilotinib therapy may be beneficial to control Ph+ve leukemia resistance and the model can determine optimal dosing schedule to enhance the effectiveness of the combination therapy .
|
High likelihood of evolution of resistance to therapy is common in most forms of leukemia . This issue persists for tyrosine kinase inhibitor drug treatments as well as other forms of therapies . In the current work , we suggest a combination therapy where Ph+ acute lymphoblastic leukemic cells are treated with low-dose radiation before chemotherapy ( Nilotinib ) . Our in vitro results of the combined therapy accompanied with a mathematical model shows successful suppression of resistance to Nilotinib . The mathematical model shows a synergistic interaction between Nilotinib and low dose radiation in the chemo dose response function . Beside acute radiation we investigate low dose fractionated therapies with model predicted optimal dosing schedules .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"cell",
"death",
"leukemias",
"medicine",
"and",
"health",
"sciences",
"radiation",
"therapy",
"dose",
"prediction",
"methods",
"cancer",
"treatment",
"clinical",
"oncology",
"cell",
"processes",
"cancers",
"and",
"neoplasms",
"mathematical",
"models",
"oncology",
"hematologic",
"cancers",
"and",
"related",
"disorders",
"clinical",
"medicine",
"pharmaceutics",
"research",
"and",
"analysis",
"methods",
"lymphoblastic",
"leukemia",
"mathematical",
"and",
"statistical",
"techniques",
"hematology",
"acute",
"lymphoblastic",
"leukemia",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy"
] |
2017
|
Combination therapeutics of Nilotinib and radiation in acute lymphoblastic leukemia as an effective method against drug-resistance
|
Establishing functional tissue-resident memory ( TRM ) cells at sites of infection is a newfound objective of T cell vaccine design . To directly assess the impact of antigen stimulation strength on memory CD8 T cell formation and function during a persistent viral infection , we created a library of mouse polyomavirus ( MuPyV ) variants with substitutions in a subdominant CD8 T cell epitope that exhibit a broad range of efficiency in stimulating TCR transgenic CD8 T cells . By altering a subdominant epitope in a nonstructural viral protein and monitoring memory differentiation of donor monoclonal CD8 T cells in immunocompetent mice , we circumvented potentially confounding changes in viral infection levels , virus-associated inflammation , size of the immunodominant virus-specific CD8 T cell response , and shifts in TCR affinity that may accompany temporal recruitment of endogenous polyclonal cells . Using this strategy , we found that antigen stimulation strength was inversely associated with the function of memory CD8 T cells during a persistent viral infection . We further show that CD8 TRM cells recruited to the brain following systemic infection with viruses expressing epitopes with suboptimal stimulation strength respond more efficiently to challenge CNS infection with virus expressing cognate antigen . These data demonstrate that the strength of antigenic stimulation during recruitment of CD8 T cells influences the functional integrity of TRM cells in a persistent viral infection .
Following TCR engagement , pathogen-specific naïve CD8 T cells rapidly expand to generate a large effector population to counter primary infection , with a small population of memory CD8 T cells concomitantly generated to provide accelerated immunity to re-infection . CD8 T cell activation and differentiation requires three signals: TCR stimulation ( signal 1 ) , co-stimulation ( signal 2 ) , and inflammatory cytokines ( signal 3 ) , with the duration and intensity of these signals determining whether an activated CD8 T cell is fated towards an effector or memory state [1–5] . The canonical naïve-to-effector/memory differentiation profile for CD8 T cell responses to microbial infections is derived from analyzing T cell responses in secondary lymphoid organs . Tissue-resident memory ( TRM ) cells apparently circumvent this differentiation schema by “locking” themselves in an effector-poised state having a transcription profile distinct from circulating central-memory and effector-memory T cells [6–10] . Most studies to date have characterized TRM cells in mucosal tissue barriers ( e . g . , skin , lung , gut , and female reproductive tract ) , where they act to provide rapid protection against secondary infections [11–16] . Less is known about the factors involved in establishment of TRM cells in non-barrier organs , particularly the CNS , an organ system susceptible to irreparable injury by viral infection . Several viral CNS infection mouse models have described the establishment of TRM cells in the brain . VSV encephalitis generates antiviral CD8 TRM cells that persist in the brain long after antigen clearance [9 , 17] . Using intracerebral ( i . c . ) inoculation with lymphocytic choriomeningitis virus ( LCMV ) , Steinbach et al . showed that virus-specific CD8 brain-TRM cells , in the absence of circulating antiviral CD8 T cells , conferred protection to CNS challenge with LCMV [12] . A variety of approaches have been devised to selectively define the role of TCR stimulation in T cell effector and memory differentiation . Antigen abundance has been controlled using mutant viruses that variably express a given T cell epitope , using low-dose inocula , and by limiting the duration of infection with antiviral agents [18 , 19] . Genetic disruption of proximal TCR signal transduction has also been used to isolate the strength of signal 1 [20–23] . These studies have typically assessed the impact of such manipulations on dominant CD8 T cell responses to acutely cleared infections in secondary lymphoid organs . Whether changes in TCR stimulation affect the generation or functionality of TRM cells remains to be determined . In this study , we conceived a strategy to directly assess the impact of TCR stimulation strength on memory T cell formation . First , we focused on a subdominant CD8 T cell response to avoid confounding effects on viral replication and virus-associated inflammation that may result from alterations in a dominant T cell compartment . Second , we studied the differentiation of TCR transgenic donor CD8 T cells to control the size , timing of recruitment , and clonality of the naïve T cell compartment . Third , we used mouse polyomavirus ( MuPyV ) , a natural mouse pathogen that establishes a persistent infection well controlled by the host immune system . Fourth , we altered an epitope in a nonstructural viral protein to avoid potential effects on host cell tropism that may occur with mutations in a capsid protein [24] . Using a library of MuPyVs with signal 1-altering mutations in a subdominant CD8 T cell epitope , we found that reduced TCR stimulation quantitatively and qualitatively improved the generation of memory T cells in both a lymphoid ( i . e . , spleen ) and a nonlymphoid ( i . e . , brain ) organ .
MuPyV-infected B6 mice generate a CD8 T cell response to three epitopes in the nonstructural viral T antigens , according to the following immunodominance hierarchy: Db-restricted Large T antigen ( LT ) amino acids 359–368 ( LT359 ) > Kb-restricted Middle T antigen ( MT ) amino acids 246–253 ( MT246 ) > Db-restricted LT amino acids 638–646 ( LT638 ) [25] . The DbLT359 and DbLT638 MuPyV epitopes share homology with SV40 LT epitopes in B6 mice; e . g . , mutagenizing 4 of 10 codons in LT359 to match its homologous sequence in SV40 LT ( residues 206–215 ) yielded an MuPyV mutant virus expressing a dominant epitope recognized by monoclonal SV40 LT-specific CD8 T cells [26] . Using this approach , we sought to develop a strategy to incisively assess the impact of antigen stimulation strength on the generation of CD8 memory T cells to MuPyV infection that avoided altering the kinetics and magnitude of infection . The subdominant MuPyV LT638 epitope and the “immunorecessive” SV40 LT epitope corresponding to amino acids 489–497 , designated TagV , differ by three residues in the amino-half of their epitopes ( Table 1 ) . We previously demonstrated that replacing the P1 Gln of TagV with Ala improved stimulation of TCR-V transgenic CD8 T cells specific for the TagV epitope [27] . Thus , we constructed the following nonameric peptides that we predicted would differentially stimulate TCR-V cells: the native TagV and LT638 epitopes; and three variant LT638 epitopes where the P4 Ala was replaced with Asn ( TagV ( N ) ) either alone , or in combination with substitution of the P1 Met residue with Gln ( TagV ( QN ) ) or Ala ( TagV ( AN ) ) ( Table 1 ) . Using the RMA/S peptide stabilization assay , we determined that the native TagV and LT638 peptides efficiently bound Db at equilibrium , but that the TagV dissociated from Db faster than LT638 ( Fig 1A and 1B ) . Each of the analogue peptides exhibited markedly weaker binding to and faster dissociation from Db . It is notable that these analogue peptides differentially associated with Db despite retaining the dominant Db anchors Asn at P5 and Leu at P9 [28] . We next compared these peptides for their efficiency in stimulating naïve TCR-V CD8 T cells . Using a range of peptide concentrations , we assayed the ability of these peptides to induce production of IFNγ , a cytokine typically used to assess T cell “functional avidity” , as well as Nur77 , an orphan nuclear receptor that is a direct measure of TCR signaling strength [29–31] . As shown in Fig 1C and 1D , TCR-V cells recognized the native TagV peptide but not the LT638 epitope from MuPyV , with intracellular Nur77 staining showing approximately 1-log higher sensitivity than intracellular IFNγ for detecting antigenic stimulation . Interestingly , both IFNγ and Nur77 readouts revealed a fine gradient of antigenic stimulation efficiency by the analogue peptides as follows: TagV > TagV ( AN ) > TagV ( QN ) > TagV ( N ) . Notably , the three analogue peptides differ in TCR stimulation and Db binding; e . g . , the TagV ( AN ) and TagV ( N ) peptides exhibit comparable binding to and dissociation from Db ( Fig 1A & 1B ) , but the former is approximately 2 logs more efficient in stimulating TCR-V cells . Collectively , these data indicate that these substitutions concomitantly affect MHC and TCR binding and provide a set of analogue peptides to interrogate the impact of antigen stimulation strength on T cell differentiation . Using site-directed mutagenesis of the MuPyV genomic DNA , we altered the coding sequence of the native LT638 epitope to that of TagV and to each of the three TagV analogue peptides , and isolated infectious virus . As depicted in Fig 2A , our approach involved adoptive transfer of a “physiologic” number ( 1 , 000 ) of naïve TCR-V CD8 T cells into CD45-congenically disparate B6 mice which were then inoculated with parental MuPyV or one of the four MuPyV variants carrying mutations in the LT638 epitope . We first assessed whether this experimental design altered the dynamics of acute ( day 8 p . i . ) and persistent ( day 30 p . i . ) MuPyV infection as well as the generation and maintenance of the CD8 T cell response to the dominant DbLT359 epitope . As shown in Fig 2B and 2C , the mutant viruses were indistinguishable from parental MuPyV in their replicative efficiency in vivo and recruitment of the dominant DbLT359-specific CD8 T cell anti-MuPyV response . Furthermore , effector-memory differentiation of the DbLT359 population based on the KLRG1 and IL-7Rα ( CD127 ) co-expression paradigm [32 , 33] revealed no differences between parental or analogue MuPyV infection ( Fig 2D ) . Infection with the analogue viruses uncovered marked differences in efficiency in recruitment of TCR-V CD8 T cells . During the acute phase of infection , TCR-V cells mounted the largest response to infection by mutant viruses carrying the TagV ( AN ) and TagV ( QN ) epitopes , which significantly exceeded the magnitude of the response to infection by MuPyV . TagV , which expresses the cognate TCR-V epitope ( Fig 3A ) . TCR-V cells were not detected in spleens of mice infected with parental MuPyV ( as expected ) or the mutant TagV ( N ) virus carrying the single P4 Ala-to-Asn substitution in the LT638 epitope; thus , all subsequent experiments used only the TagV , TagV ( AN ) , and TagV ( QN ) variant viruses . Based on KLRG1 and CD127 co-expression and expression of the T-box transcription factors T-bet and eomesodermin ( eomes ) , no differences were seen in markers of effector and memory differentiation by TCR-V cells recruited by MuPyV . TagV , MuPyV . TagV ( AN ) , and MuPyV . TagV ( QN ) infection ( Fig 3B–3D ) . Moreover , direct ex vivo stimulation with cognate TagV peptide induced similar expression of Nur77 and IFNγ/TNFα co-functionality by TCR-V cells ( Fig 3E ) despite differences between these three viruses in their capacity to stimulate naïve TCR-V cells ( Fig 1C & 1D ) . Because strong , sustained antigen stimulation dampens the functionality of CD8 T cells , we next asked whether lower TCR stimulation could improve memory T cell differentiation . At day 30 p . i . , TCR-V cells recruited in response to infection by TagV , TagV ( AN ) , and TagV ( QN ) mutant MuPyVs gave rise to a population of memory T cells , with MuPyV . TagV ( QN ) infection generating a significantly larger population ( Fig 4A ) . When stimulated ex vivo with 1μM cognate TagV antigen , TCR-V cells primed by MuPyV . TagV ( QN ) infection yielded memory cells that were capable of co-producing IFNγ and TNFα to a significantly higher level than those recruited by MuPyV . TagV or MuPyV . TagV ( AN ) infection ( Fig 4B , left panel ) . Notably , a significantly higher fraction of TagV ( QN ) epitope-primed memory CD8 T cells produced Nur77 as well , indicating that these T cells possessed improved sensitivity to the cognate antigen ( Fig 4B , right panel ) . To address this possibility , we performed peptide dose-response curves on TCR-V effector and memory cells from the spleen . Splenocytes from day 8- and day 30-infected mice were stimulated for five hours with TagV , TagV ( AN ) , TagV ( QN ) , TagV ( N ) , or LT638 peptide at concentrations ranging from 1 μM to 1 pM , then analyzed for intracellular IFNγ and Nur77 . Effector and memory TCR-V cells exhibited higher functional avidity to the cognate TagV peptide than to the analogue peptides irrespective of which of these viruses was used for the primary infection , suggesting that a recall response would be optimally elicited by cognate antigen ( S1 Fig ) . Effector TCR-V cells from MuPyV . TagV-infected mice stimulated with cognate TagV peptide had a lower EC50 for IFNγ and Nur77 expression than those from MuPyV . TagV ( AN ) - and MuPyV . TagV ( QN ) -infected mice ( Fig 4C ) . By day 30 post infection , however , memory TCR-V cells primed with MuPyV . TagV ( AN ) and MuPyV . TagV ( QN ) infection acquired higher functional avidity , a three-to-four log decrease in EC50 , than those recruited with MuPyV . TagV infection ( Fig 4D ) , indicating an evolution toward lower activation threshold for cognate antigen for memory cells primed with a weaker TCR stimulus . We next compared the in vivo antigen recall responses of TCR-V memory cells generated by infection to TagV , TagV ( AN ) , and TagV ( QN ) mutant MuPyVs . Following systemic infection with MuPyV , mice mount a robust neutralizing antibody response that prevents repeat infection with the same virus . Thus , we used TagV epitope-expressing fibroblasts , 116A1 cells , to present a new source of TagV antigen [34] . 116A1 cells were injected intraperitoneally ( i . p . ) at day 30 p . i . , and splenic TCR-V cells were enumerated five days later ( Fig 4E ) . Both MuPyV . TagV ( AN ) and MuPyV . TagV ( QN ) infected mice showed a significant increase in TCR-V cell expansion upon 116A1 cell challenge compared to infection-matched unchallenged mice , with TagV ( QN ) -primed TCR-V cells exhibiting the highest level of expansion ( Fig 4F ) . When stimulated ex vivo with cognate TagV antigen , TagV ( QN ) -primed secondary effector TCR-V cells had significantly improved co-production of IFNγ and TNFα ( Fig 4G ) . Together , these data show a marked increase in memory potential in TCR-V cells primed with lower TCR stimulation . TRM cells protect against microbial infections in non-lymphoid tissues [11–16] . Because JC polyomavirus causes a devastating demyelinating brain disease [35] , we asked whether differential TCR signaling from systemic infection affected establishment of brain-resident TCR-V memory cells . CD11a and CD49d , subunits of the LFA-1 and VLA-4 integrins , respectively , involved in CNS entry by circulating T cells [36 , 37] , were upregulated in circulating TCR-V cells at day 8 p . i . irrespective of infection with MuPyV . TagV , Tag ( AN ) , or Tag ( QN ) viruses . ( Fig 5A ) . As shown in Fig 5B , the TCR-V cell population responding to infection by viruses with the cognate TagV epitope and the TagV ( AN ) and TagV ( QN ) analogue epitopes underwent expansion and contraction in the spleen , and contracted after migrating into the brain but to a lesser extent than in the spleen . Compared to splenic TCR-V cells ( S2A Fig ) and brain-infiltrating TCR-V cells at day 8 p . i . , brain-derived memory TCR-V cells for each virus expressed low CD62L , elevated CD69 , as well as elevated CD8α ( Fig 5C ) , a phenotype consistent with TRM cell differentiation [6] . CD103 expression was not detected on these cells ( S2B Fig ) , a finding in line with evidence from us and others that CD103 is not a faithful TRM marker [38] . Further confirmation that these are TRM cells comes from the finding that their presence in the brain was unaffected following depletion of circulating CD8 T cells ( Figs 5D & S3 ) . Brain-resident TCR-V cells generated in response to infection by TagV , TagV ( AN ) , or TagV ( QN ) MuPyVs exhibited similar cytokine effector capability upon stimulation by the cognate TagV peptide ( Fig 5E ) . Together , these data indicate that lower TCR stimulation in the context of a systemic viral infection generates functional brain-TRM cells . We next compared the recall responses by brain TRM cells recruited by infection with MuPyV . TagV , MuPyV . TagV ( AN ) , and MuPyV . TagV ( QN ) viruses . A strongly neutralizing antibody response in MuPyV-immune mice negates the ability to rechallenge mice systemically with MuPyV . Because the blood-brain barrier largely excludes circulating immunoglobulins , we investigated the possibility that i . c . injection of MuPyV would allow reinfection in the brain in MuPyV-immune mice . On day 30 p . i . , mice were challenged i . c . with MuPyV . TagV virus and sacrificed five days later ( Fig 6A ) . To confine analysis to brain-TCR-V TRM cells , mice received CD8 depleting mAb starting day 10 p . i . after the cells had migrated to the brain . We observed that only TCR-V TRM cells generated by MuPyV . TagV ( AN ) infection underwent significant expansion to MuPyV . TagV challenge infection ( Fig 6B ) . Despite these differences in size of the recall response , no significant differences in cytokine effector capability were seen ( Fig 6C ) . Together , these data demonstrate that infection with MuPyV mutants carrying a suboptimally stimulating epitope can promote establishment of functional TRM cells having an improved capacity to counter re-infection .
In this study , we show that strength of TCR stimulation is a central determinant guiding the differentiation of functional antiviral CD8 TRM cells in persistent infection . To insulate effects of TCR stimulation from dynamic changes associated with host immunity , and do so in a natural host viral infection , we mutated a weak subdominant CD8 T cell epitope in MuPyV and monitored the differentiation of donor CD8 T cells from a TCR transgenic mouse . By altering a subdominant CD8 T cell epitope within a virus and not impacting viral fitness or tropism , we circumvented variations in antigen processing , virus-associated inflammation , and host antiviral immunity that could impact T cell fate . In addition , adoptive transfer of a physiologic number of TCR transgenic CD8 T cells served to eliminate evolution of the polyclonal response and timing of T cell recruitment on T cell differentiation [39 , 40] . Using this strategy , we found that infection by MuPyVs carrying altered epitopes that reduced TCR stimulation strength ( 1 ) recruited a larger number of antiviral effectors than those elicited by cognate antigen , and ( 2 ) generated a memory population in both lymphoid and nonlymphoid tissues endowed with superior recall response capability . These findings provide direct evidence that TCR signal strength , as an isolated variable , plays a dominant role in the differentiation of TRM cells . A number of approaches have been used to investigate the influence of TCR signaling strength in T cell differentiation , including genetic alterations in TCR signaling , and modulating the level and duration of antigen and inflammation [18 , 41 , 42] . Inducible systems have been developed to disrupt proximal TCR signaling at the level of Lck [22] , SLP-76 [23] , and the TCR [20] . These studies collectively suggest that while TCR signaling is required for activation and expansion of naïve CD8 T cells , it is not required for memory T cell maintenance , self-renewal , or a secondary response . In contrast , OT-I cells with a point mutation in the TCR transmembrane domain that retains peptide:MHC ligand binding and proximal signaling , but has reduced NF-κB activity , undergo accelerated and deeper contraction and exhibit defective memory development [21] . Antigen availability has also been shown to affect the magnitude of CD8 T cell recruitment , but without impacting memory function , implicating an “all-or-nothing” phenomenon [19] . Temporal ablation of dendritic cells carrying a diphtheria toxin receptor transgene to vary the duration of antigen presentation curtailed the magnitude of the T cell response , but did not impede memory generation [43] . Other studies using antibiotic/antiviral treatment [32 , 44] or transfer of early effector cells into infection-controlled hosts [33] to shorten the duration of antigen and inflammation have shown improvement in memory formation . We previously demonstrated that reducing the MuPyV inoculation dose was associated with increased memory T cell fitness [45] . It merits pointing out that each of these prior studies assessed T cell expansion and effector memory differentiation on a population level . At the single-cell level , TCR signaling appears to behave in a digital fashion , such that clonal expansion is engaged once a minimum threshold of TCR activation is reached [31] . Our finding that MuPyVs with altered subdominant epitopes drive equivalent or greater T cell clonal expansion than the native epitope fit with this concept; our data are also at odds with the notion of a positive correlation between TCR stimulation strength and clonal burst size . Alteration of memory T cell differentiation as a function of TCR stimulation has been studied using analogue peptide epitopes that either differentially engage TCRs ( altered peptide ligands; APLs ) or MHC molecules ( MHC variant peptides; MVPs ) . Using APLs of the Kb-restricted SIINFEKL epitope with varying affinities for OT-1 cells expressed by recombinant Listeria monocytogenes , Zehn et al . found a direct correlation between affinity of the priming APL and OT-1 cell numbers . This correlation was explained by preferential retention of OT-1s in secondary lymphoid organs when priming involved the higher affinity APLs , such that cell numbers peaked earlier than with the lower affinity APLs; irrespective of APL affinity , OT-1 cells differentiated into functional memory cells [46] . In contrast , we found that weaker ligands for TCR-V cells elicited a larger expansion , and did so without affecting the kinetics of the response . This discrepancy may result from differences in innate signals induced by L . monocytogenes vs MuPyV infection , in acutely vs persistently infecting pathogens , the dominant Kb/SIINFEKL OT-1 epitope vs the weak subdominant Db/TagV TCR-V epitope , and/or in the TCR stimulation strength of the analogue epitope ligands used in these infection models . Regardless of the MuPyV mutant used , TCR-V cells were not detected in the lymph nodes or the spleen prior to day 6 p . i . ( S4 Fig ) . Furthermore , the relative percentage of TCR-V cells in the spleen at day 6 p . i . mirrored the trend seen at day 8 p . i . , suggesting that the increased magnitude of TCR-V expansion in MuPyV . TagV ( AN ) infected mice was not due to earlier recruitment or delayed egress . Using an LCMV mutant carrying an MVP of the GP33 epitope recognized by P14 transgenic CD8 T cells , Evavold and colleagues observed early contraction of P14 cells due to accelerated apoptosis [47] . Although none of the analogue TagV peptides had changes in the dominant Db anchoring residues ( P5 asparagine and P9 leucine ) , these analogues reduced stabilization of Db by RMA/S cells ( Fig 1A ) ; thus , the TagV analogues appear to straddle the line between APLs and MVPs . In any event , MuPyVs expressing analogue epitopes that have weaker TCR stimulatory capacity elicit a larger expansion of donor TCR-V cells than those carrying the cognate epitope ( Fig 3 ) . A central finding in this study is that the strength of TCR stimulation affects the generation of CD8 TRM cells . We found that lower TCR stimulation in the setting of a persistent viral infection generates brain TRM cells with superior anamnestic potential and having increased functional avidity ( Figs 4 & 6 ) . It is possible that the increased functionality of TCR-V cells recruited by lower stimulation was due to unique programming . No differences in expression of T-bet/eomes among memory TCR-V cells generated in response to infection with the analogue viruses were detected ( Fig 3 ) ; however , we cannot exclude the possibility for differences in epigenetics or other transcription factors shown to guide memory differentiation [48] . What we did observe , however , was an improvement in polyfunctionality and a dramatic increase in functional avidity of TCR-V cells primed with lower stimulation ( Fig 4 ) . While effector TCR-V cells recovered at day 8 p . i . from MuPyV . TagV-infected mice displayed slightly increased sensitivity to cognate TagV peptide , memory T cells recovered from MuPyV . TagV ( AN ) - and MuPyV . TagV ( QN ) -infected mice had superior functional avidity for cognate antigen , with EC50 values approximately three-to-four logs lower than the MuPyV . TagV-infected mice ( Fig 4C & 4D ) . This result fits with the possibility that persistent low TCR stimulation , here via modulating residues in a cognate epitope , fosters generation of memory T cells having elevated functionality to the cognate epitope . Functional avidity , an experimental readout reflective of TCR-pMHC affinity , TCR levels and topology , co-receptor levels , and number and activation of signaling molecules [49] , has been shown to evolve with the state of differentiation of both polyclonal and TCR transgenic cells [50–52] . Indeed , our results show that memory TCR-V cells have increased functional avidity compared to naïve TCR-V cells ( Figs 1 , 4 & S1 ) . Both TCR ( CD3ε gMFI ) and co-receptor ( CD8α gMFI ) levels were unchanged in either the spleen or the brain over the course of infection with MuPyV . TagV or the analogue viruses ( S5 Fig ) . An increase in functionality of monoclonal populations of effector and memory cells , as seen with the TCR-V:MuPyV infection system , has been attributed to clustering of TCRs and signaling molecules to immunological synapses [53–55] , and increased efficiency of signal transduction [50 , 56] . Although the theme of lower TCR stimulation conferring greater memory potential holds true in both organs , we noted a disparity in the best responders to secondary challenge between the splenic memory T cells and the brain TRM cells , in that cells generated to MuPyV . TagV ( QN ) infection exhibited higher recall responses in the spleen , but those in the brain were higher for memory TCR-V cells recruited by MuPyV . TagV ( AN ) infection ( Figs 4 & 6 ) . This difference might be explained by the different antigen challenge approaches ( s . c . implantation of transformed TagV+ cells vs i . c . inoculation with TagV+ MuPyV ) . The superior recall response exhibited by the TagV ( AN ) - or TagV ( QN ) -primed TCR-V cells may in part also reflect the lower frequency of these cells at the time of secondary infection , which could extend the period of antigen availability and enable a longer secondary expansion phase . Although the brain-resident CD8 T cells in our study lacked expression of CD103 , a surface marker implicated in defining TRM cell populations , it should be noted that CD103 appears to not be a reliable indicator for identifying TRM cells [6 , 57] . Expression of CD103 may be dependent on route of infection and the subsequent level of inflammation and recruitment of cells into the brain parenchyma , both of which are low in the brain following a systemic MuPyV infection . A defining characteristic of TRM cells is their lack of dependence on replenishment from the circulation; indeed , depletion of CD8 T cells from the circulation did not affect stability of TCR-V cell numbers in the brain ( Fig 5 ) . Further studies are needed to assess the impact of route of inoculation and subsequent changes in local inflammation and antigen abundance on the generation of TRM cells . A sizeable body of literature indicates that subdominant CD8 T cells are an integral component of anti-microbial immunity . Studies in respiratory syncytial virus ( RSV ) and Mycobacterium tuberculosis have established a protective role for subdominant CD8 T cells , which in some cases have been shown to be more effective at clearing pathogens than the dominant T cell population [58 , 59] . A study of HIV-1 vaccine design approached the idea of subdominant epitope vaccinations by inactivating immunodominant epitopes to foster compensatory expansion of the subdominant antiviral CD8 T cell populations , and showed that they were capable of inducing robust , protective responses [60] . A vaccine formulation that elicited multiple Ebola virus epitope-specific CD8 T cell populations , including those to subdominant epitopes , displayed significant protective capability [61] . T cell vaccine design typically focuses on generating high-affinity , dominant epitope-specific CD8 T cells . Weak TCR-antigen interactions , however , are capable of inducing expansion and phenotypic differentiation of naïve CD8 T cells into effector and memory cells [46 , 62 , 63] . Strong , sustained interaction of antigen with TCRs upregulates inhibitory receptors on CD8 T cells , rendering them dysfunctional [64–67] . Moreover , host-pathogen interactions are dynamic , leading to selection of pathogens with mutations in epitopes that interfere with recognition by pathogen-specific T cells [68–72] . Epitopes targeted by dominant T cell responses are especially susceptible to mutations that handicap binding to an MHC molecule and/or interfere with TCR engagement . Our data demonstrate that TRM cells directed to a subdominant epitope are generated by infection with a persistent viral pathogen; these subdominant TRM cells are endowed with strong memory potential such that they expand locally and produce antiviral cytokines during secondary challenge ( Figs 4 & 6 ) . By extension , evidence presented here highlights the importance of investing in vaccine strategies to generate subdominant memory T cell populations in nonlymphoid tissues . In summary , we have explored the effects of TCR stimulation strength in a subdominant T cell population on the formation of lymphoid and brain-resident memory cells . Whereas clonal expansion and CD8 effector function have typically been considered to correlate directly with TCR stimulation , we have shown that lowering TCR stimulation strength results in larger T cell clonal expansion and maintains effector function while improving memory potential . Furthermore , our data provides evidence that systemic infection by a persistent virus expressing analogue epitopes with decreased TCR stimulatory capacity can recruit higher functioning tissue-resident memory cells to the brain .
All experiments involving mice were conducted with the approval of Institutional Animal Care and Use Committee ( Protocols 45575 and 46194 ) of The Pennsylvania State University College of Medicine in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The Pennsylvania State University College of Medicine Animal Resource Program is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . The Pennsylvania State University College of Medicine has an Animal Welfare Assurance on file with the National Institutes of Health’s Office of Laboratory Animal Welfare; the Assurance Number is A3045-01 . Female C57BL/6 ( B6 ) were purchased from the National Cancer Institute ( Frederick , MD ) . CD45 . 1 TCR-V transgenic mice expressing a TCR specific for Large T antigen ( LT ) amino acids 498–505 ( “SiteV” ) of SV40 have been previously described [73] . Mice were used at 8–10 weeks of age . RMA/S cells [74] were cultured in RPMI 1640 medium supplemented with 10% FBS , 100 units/ml penicillin , and 100 μg/ml streptomycin . TagV-only expressing 116A1 cells ( B6/T 116A1 Cl-C ) [75] , were cultured in DMEM supplemented with 10% FBS , 100 units/ml penicillin , 100 μg/ml streptomycin . HPLC-purified peptides synthesized by Peptide 2 . 0 , Inc . ( Chantilly , VA ) were dissolved in PBS and stored at -20°C . Peptides are listed in Table 1 . The A2 strain of MuPyV was prepared in baby mouse kidney cells as described [76] . TagV variant viruses were created using the QuikChange Site-Directed Mutagenesis kit ( Agilent Technologies ) . Primer sets ( Integrated DNA Technologies ) are listed in Table 1 . Viruses were titered by plaque assay on BALB/3T3 clone A31 cells ( American Type Culture Collection , Manassas VA ) as described [77] . Mice were infected in hind footpads with 2 x 106 PFU . For MHC-I stabilization assays , RMA/S cells were cultured overnight at 26°C , resuspended in RPMI containing 10% FBS ( 10% RPMI ) and peptides of different concentrations , then incubated for 1 h at room temperature followed by 2 h at 37°C . For peptide dissociation assays , RMA/S cells were cultured overnight at 26°C in the presence of 100 μM peptide , then resuspended in 10% RPMI at 37°C . Each hour for 4 h an aliquot of cells was stained for 30 mins at 4°C with phycoerythrin-conjugated anti-Db ( clone 28-14-8; eBioscience ) , acquired on a BD FACSCanto10 instrument , and the geometric mean fluorescence intensity ( gMFI ) determined using FlowJo Software ( Tree Star ) . The percent maximum gMFI was calculated as ( gMFIpeptide-gMFIno peptide ) / ( gMFImax- gMFIno peptide ) x 100 . EC50 and T1/2 were calculated using Prism software ( GraphPad , La Jolla CA ) . Splenocytes were cultured for 5 h in DMEM containing 10% FBS , 100 units/ml penicillin , 100 μg/ml streptomycin and supplemented with 1ug/ml brefeldin A ( Sigma-Aldrich ) . Cells were exposed to Fixable Viability Dye eFluor 780 ( eBioscience ) , then surface stained with mAbs to CD8α ( clone 53–6 . 7; eBioscience ) , CD44 ( clone IM7; eBioscience ) , and CD45 . 1 ( clone A20; BioLegend ) , permeabilized with Cytofix/Cytoperm buffer ( BD Biosciences ) or FoxP3 buffer ( eBioscience ) , and stained for intracellular IFNγ ( clone XMG1 . 2; Biolegend ) , TNFα ( clone TN3-19 . 12; eBioscience ) , IL-2 ( clone JES6-5H4; BD Biosciences ) , and Nur77 ( clone 12 . 14; eBioscience ) . DbLT359 and SiteV tetramers [25 , 75] were provided by the NIH Tetramer Core Facility ( Atlanta , GA ) . CD8 T cells were purified from CD45 . 1 TCR-V mice using a negative selection CD8 T cell isolation kit ( Miltenyi Biotec ) according to manufacturer’s instructions; transferred cells were >90% CD8+ . 1 x 103 TCR-V cells were injected i . v . per tail vein one day prior to infection . TaqMan real-time PCR was performed using 10 μg template DNA purified from snap frozen tissues using the Maxwell 16 nucleic acid isolation system ( Promega , Madison WI ) as described [25] . Spleens and brains were harvested at indicated days p . i . Brains were minced and digested with collagenase ( 100 U/ml in DMEM with 2% FBS , 200 U/ml penicillin , 200 g/ml streptomycin , 2 mM L-glutamine , 5 μM HEPES , 1 μM MgCl2 , 1 μM CaCl2 ) for 30 min at 37°C , passed through a 70 μm nylon cell strainer ( BD Biosciences ) . Cells were isolated by centrifugation on a 44%:66% Percoll gradient . Spleens were passed through a 70 μm nylon cell strainer , then treated with ACK buffer ( 0 . 15 M NH4Cl , 1mM KHCO3 , 1mM Na2EDTA , pH 7 . 0 ) to lyse RBCs . Cells were surface-stained in FACS Buffer ( PBS , pH 7 . 2 with 1% BSA , 0 . 1% sodium azide ) for 30 min at 4°C with mAbs to CD8α ( clone 53–6 . 7; Biolegend ) , CD44 ( clone IM7; eBioscience ) , CD45 . 1 ( clone A20; Biolegend ) , CD62L ( clone MEL-14; BD Biosciences ) , CD69 ( clone H1 . 2F3; BD Biosciences ) , PD-1 ( clone RMP1-30; Biolegend ) , CD11a ( clone 2D7; BD Biosciences ) , CD49d ( clone MFR4 . B; BioLegend ) , KLRG1 ( clone 2F1; BD Biosciences ) , and CD127 ( clone AFR34; Biolegend ) . Samples were collected on a BD LSR Fortessa or FACSCanto10 flow cytometer . Fluorescence-minus-one ( FMO ) samples were used to set positive gates for each surface molecule examined . Mice were injected i . p . with 250 μg rat anti-CD8α ( YTS169 . 4; Bio X Cell , West Lebanon NH ) or ChromPure whole rat IgG ( Jackson ImmunoResearch Laboratories , West Grove PA ) at 10 , 12 , 19 , and 26 days p . i . Depletion was confirmed in the blood by flow cytometry Absolute Count Standard ( Bangs Laboratories , Fishers IN ) . To assay splenic TCR-V recall , 5x107 116A1 cells in 0 . 5 ml PBS were injected i . p . To assay TCR-V recall in the brain , 2 x 106 PFU MuPyV . TagV virus was injected i . c . in 30 μl DMEM containing 2% FBS . Mice were challenged at day 30 p . i . and sacrificed five days post-challenge . All data are displayed as mean ± SD unless otherwise indicated . p values were determined using an unpaired Student’s t test assuming equal variance or one-way ANOVA using GraphPad Prism software . All p values ≤ 0 . 05 were considered significant .
|
Tissue-resident memory ( TRM ) cells are a subset of memory T cells that primarily reside in non-lymphoid tissues and serve as sentinels and effectors against secondary infections . TRM cells have been extensively characterized in mucosal barriers , but much less is known about this population in non-barrier sites such as the brain . In this study , we designed a novel strategy to evaluate the impact of T cell stimulation strength on the generation and functionality of memory CD8 T cells in both lymphoid and nonlymphoid tissues . Using a mouse polyomavirus ( MuPyV ) library expressing variants of a subdominant epitope recognized by TCR transgenic CD8 T cells , we found that systemic infection producing weaker responses during T cell priming was sufficient for recruitment of effector cells to the brain . Furthermore , lower stimulation conferred greater functionality to memory T cells in the spleen and to brain TRM cells . Our findings demonstrate that the strength of antigenic stimulation experienced by a naïve T cell early in infection is a determinant of memory functional integrity during viral persistence in a non-barrier organ .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"spleen",
"immunology",
"cloning",
"neuroscience",
"learning",
"and",
"memory",
"cell",
"differentiation",
"developmental",
"biology",
"cognition",
"memory",
"cytotoxic",
"t",
"cells",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"white",
"blood",
"cells",
"memory",
"t",
"cells",
"animal",
"cells",
"proteins",
"t",
"cells",
"molecular",
"biology",
"biochemistry",
"signal",
"transduction",
"t",
"cell",
"receptors",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immune",
"receptors",
"cognitive",
"science"
] |
2017
|
TCR stimulation strength is inversely associated with establishment of functional brain-resident memory CD8 T cells during persistent viral infection
|
Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk . Yet , little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data , which may result in inaccurate risk projection when several vector species are present , and when little is known about their likely role in local transmission . Here , we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama . Our exploratory space-time modelling approach detected the presence of five clusters , which varied by duration , relative risk , and spatial extent after incorporating vector species as covariates . The Ae . aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels , followed by the model accounting for both Ae . aegypti and Ae . albopictus . This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation , potentially improving efforts to understand outbreak dynamics at national scales .
Dengue disease , a viral infection transmitted to humans by Aedes mosquitoes , is endemic to 128 countries , with 3 . 9 billion people considered at-risk globally [1] . Dengue disease cases have increased dramatically worldwide throughout the previous several decades [2] , likely a result of urbanization [3] , globalization [4] , climate change [5] , the breakdown of regional control programs [6] , and the spread of the invasive Aedes albopictus [7] . As a result of both recent and historical risk , many countries employ national surveillance programs to monitor trends in dengue and inform local health authorities to the places and times where preventative practices are most required . However , despite the commonality of these programs and unforeseen cost of cutting them [8] , surveillance budgets are often limited [9 , 10] , restricting the scope and quality of the work . This is concerning in developing regions such as Central America , where the burden of disease is high [1] and per capita public health expenditure is among the lowest of any region of the world [11] . Surveillance of both diseases and vectors is an essential component of integrated disease management programs that can be used to determine risk changes in space and time , thus providing the evidence for more targeted prevention and control interventions [12] . Nevertheless , with few exceptions , it is rare for surveillance programs to concurrently monitor both arbovirus cases and vector populations in the same locations and at regular intervals . Most projections of disease risk used to justify public health actions are derived purely from disease case data , ignoring vector population dynamics , which is a key aspect of the vector transmission model . This is particularly concerning when more than one vector species is present , and little is known about their likely role in local transmission , which may result in inaccurate or incomplete risk projection or case clustering models . The Republic of Panama has been monitoring dengue cases alongside vector presence through the National Department of Epidemiology ( NDE ) since 1988 , making it one of the most long-standing surveillance programs of its kind in Latin America . Of the two known dengue mosquito vectors , Ae . aegypti is considered resident to Latin America and Panama since the 19th century , and the primary source of transmission [12] while Ae . albopictus , considered a secondary vector , has been spreading throughout the region ever since it got introduced in Panama in 2004 [13 , 14] . Widespread extirpation of Ae . aegypti by a superior ecological competitor like Ae . albopictus has occurred throughout the world in recent decades [15–17] , with unknown consequences on arbovirus transmission risk . Encompassing this period of growing interspecific competition among two vector species , Panama’s surveillance system is particularly unique and potentially useful to modelling dengue transmission risk while considering Aedes species interaction . Attaining a better understanding of dengue outbreak dynamics over time may improve the capacity of public health authorities to combat the spread of other arboviruses , such as Zika Virus and Chikungunya Virus . Our overall aim is to examine the influence that concurrent dengue case surveillance and Aedes species monitoring can have on cluster detection and relative risk estimation . Based on previous studies incorporating covariates into spatiotemporal cancer cluster detection [18 , 19] , we hypothesize that the inclusion of vector surveillance data will alter the identification of dengue clusters in space-time . In so doing , we describe the results of 13 years of dengue and Aedes surveillance data , including two competing vector species plus virus data originating from long-term cooperatively organized surveillance programs . We believe this is the first effort to adjust for vector presence and absence in a vector-borne disease cluster detection model , which we hope sheds light on the characteristics of space-time clusters and relative risk estimation of dengue after Aedes species are used as model covariates .
We utilized dengue prevalence data collected by the National Department of Epidemiology ( NDE ) , housed within the Panamanian Ministry of Health ( MINSA ) [20] . Systematic national surveillance of dengue cases in Panama have been continuous since 1988 . Suspected cases are defined by a patient with a fever and one or more of the following symptoms: headache , retro orbital pain , myalgia , exanthema , rash , vomiting , malaise , leukopenia , and jaundice . A confirmed case is defined as a suspected case with a positive dengue test , conducted using either viral isolation , reverse transcription polymerase chain reaction ( RT-PCR ) , IgM enzyme-linked immunosorbent assay platform ( ELISA ) , or secondary IgG ELISA . RT-PCR was established as the original standard by the National Reference Laboratory at the Gorgas Memorial Institutes for Health Studies ( ICGES ) in 2003 . Yet since 2009 , MINSA established national decentralization of serological confirmation of dengue using ELISA tests , which has improved efficiency by allowing district health officials to confirm cases without needing to send samples to a single central facility in Panama City . Data is recorded at the Corregimiento , or neighborhood , scale as the number of confirmed cases in a given year at a given location . This is the lowest scale of data granularity available , and thus , we do not have patient-level detail nor temporal detail at smaller units than year . We utilized vector data from the Vector Control Department ( VCD ) at MINSA . Systematic entomological surveillance has occurred in Panama since 2000 in order to establish Aedes infestation rates , and thus , areas of potential dengue transmission risk . Surveys of both Ae . aegypti and Ae . albopictus are performed annually at the Corregimiento-scale and consist of solely larval surveillance . Each year , a random block of houses is chosen and all houses in the block are searched for containers holding Aedes larvae . The larvae are collected and allowed to mature to the fourth instar , at which point they are taxonomically identified to species based on morphological keys [21] . The number of houses positive for Ae . aegypti , Ae . albopictus or both are recorded in the raw datasets . However , because there is no record of the number of houses in each block , we have transformed the data into a presence-absence format in each Corregimiento rather than analyzing the number of positive houses . We conducted our analyses on dengue and vector data from 2005–2017 , encompassing the period in Panama when both Ae . aegypti and Ae . albopictus have been interacting . Overall , data was collapsed from the original Corregimiento scale to the district scale . This is due to unreliable human population estimates at scales smaller than the district . Human population data was gathered from the National Institute of Statistics and Census ( INEC ) , which conducts a national census every 10 years . Because the national census is only conducted every ten years , we used the population levels from 2010 to calculate PR for data from 2005–2017 . While this is not ideal , and incurs inherent error in the year to year accuracy of the PR estimate , there is no more frequent population estimate available . This is an unfortunately common situation , especially in Central America , where no country conducts national population assessments more frequently than every 10 years . For the spatial analyses , we utilize discrete Poisson space-time modelling STSS [22] , which systematically moves cylindrical search windows across the geographic and temporal space to detect space-time clusters . Essentially , STSS determines if the observed disease cases in a particular region and time period exceed the expected cases under baseline conditions . In vector-borne disease research , STSS have been used to examine outbreaks of dengue [23–25] , chikungunya [26] , malaria [27 , 28] , Chagas [29] , and West Nile [30 , 31] , for example . STSS have also been used to examine the co-circulation of dengue and chikungunya in Colombia [32] . However , none of these prior studies have utilized vector surveillance data as a covariate . The cylinders are centered on the centroids of the Panamanian districts while the base of a cylinder is defined as the spatial scan , and the height of a cylinder represents the temporal scan . The number of observed and expected dengue cases are computed for each cylinder . Conceptually , a vast number of cylinders of various space-time dimensions are generated until an upper bound is reached , while each cylinder is a potential cluster . For this study , the maximum spatial scan was set to 25% of the total population in Panama , while the maximum temporal scan was set to 4 years . A Poisson-based likelihood ratio is calculated for each cylinder , which is proportional to ( n/μ ) n[ ( N-n ) / ( N-μ ) ]N-n[33] . For the parameters , μ is the expected number of dengue cases in a cylinder , and n is the total observed dengue cases in the cylinder . The expected number of dengue cases is computed by multiplying the fraction of population that lives within the cylinder ( p ) by the total number of cases in Panama ( C ) divided by the total population ( P ) , that is: E[c] = p*C/P - The cylinder with the highest likelihood ratio is the most likely space-time cluster . To evaluate the statistical significance of the candidate space-time clusters , 999 Monte Carlo simulations are performed under the null hypothesis that there are no significant clusters . Subsequently , we report secondary space-time clusters with a p-value less than 0 . 05 . It must be noted that the cylindrical shape of the clusters does not represent the true shape of the clusters . While it is possible to use irregular search windows [34–36] , cluster borders in the model outputs are not perfectly in line with the borders of the risk in reality . For this study , we ran four STSS models: ( 1 ) dengue cases only; ( 2 ) dengue cases controlled for the presence and absence of Ae . aegypti and/or Ae . albopictus ( i . e . absence of both species , Ae . aegypti presence , Ae . albopictus presence , and presence of both species ) ; ( 3 ) dengue cases controlled for Ae . aegypti presence/absence only; and ( 4 ) dengue cases controlled for Ae . albopictus presence/absence only . For the covariate adjusted models , the expected number of dengue cases is defined the same way for the non-adjusted model , but includes covariate category i . That is: E[c] = ∑ipi*CiPi . In other words , the adjusted STSS searchers for clusters “above and beyond that which is expected due to these covariates” [37] . For each model , we also report the relative risk of prevalence in each district that belongs to a space-time cluster , which is defined as ( c/e ) /[ ( C-c ) / ( C-e ) ] , where c is the total observed dengue cases in a particular district; e is the expected cases in a district; and C is the total observed dengue cases in the country of Panama . Clusters with a relative risk > 1 indicates that there were more observed dengue cases than expected under baseline conditions . We created all maps in ArcGIS [38] .
From 2005–2017 , there were a total of 49 , 910 cases of dengue in Panama ( Fig 1 ) , with 2009 and 2014 being the most severe at 6 , 941 and 7 , 423 cases respectively . These two years represented 28% of the total dengue cases during the 13-year period . The spatial distribution of the total number of dengue cases and the crude rate per 10 , 000 people per district is shown in Fig 2A and 2B , respectively . The district with the largest number of dengue cases between 2005–2017 was San Miguelito ( 13 , 109 cases ) , which is situated in Metropolitan Panama City . The highest rate of dengue per 10 , 000 people during this same time frame in Panama was shared among major urban centers across the country , including again the districts of San Miguelito ( 416 . 13 ) , Arraijan ( 310 . 0 ) , and Chepo ( 294 . 76 ) in the Province of Panama plus Aguadulce ( 384 . 45 ) , Santiago ( 338 . 21 ) , Nata ( 269 . 69 ) , Guarare ( 277 . 42 ) in central Panama , and Bocas del Toro ( 758 . 59 ) and Changuinola ( 298 . 74 ) in northwestern Panama ( Fig 2A and 2B ) . Additionally , surveillance of each vector species indicated prolonged endemic presence of Ae . aegypti alongside increasing presence of Ae . albopictus from just one district in 2005 to 53 in 2016 ( Fig 3 ) . The results of our space-time modelling detected the presence of five clusters in each of the four models , varying by cluster center and duration ( Figs 4–7 and Table 1 ) . Incorporating covariates into the models had considerable effects on the duration , relative risk ( RR ) , and spatial extent of clusters ( Table 2 ) . The model adjusting for the presence of Ae . aegypti encompassed the greatest spatial range and highest number of districts with a RR > 1 , while the model adjusting for the presence of Ae . albopictus encompassed the smallest spatial range and the lowest number of districts with a RR > 1 . The duration of the space-time clusters is notably different when adding the vector surveillance data to the model , however , the one exception is cluster 1 for each model ( most likely cluster ) . For example , the duration of cluster 2 was 2015–2017 for the no covariate and Ae . aegypti model; while the Ae . albopictus and Aedes ( both ) model reported a duration of only 1 year , which occurred six years earlier ( 2009 ) . Furthermore , cluster 2 was found in different geographic locations for the Aedes ( both ) and Ae . albopictus models . This variation in duration of the clusters between the four models is a result of adjusting for the presence of Aedes during the 13-year study period . In other words , the start , end , and duration of the clusters is substantially affected by the presence of one or more Aedes species . The relative risk may be higher if Aedes was found in a district during the entire duration of a space-time cluster . During the 13 years of our study period combined with the 63 districts containing data ( 13 * 63 = 819 ) , Ae . aegypti was present 690 times , Ae . albopictus was present 245 times , while both Aedes species were found in a district 224 times . As a result , the difference in species presence during the study period partly explains why the clusters for the Ae . albopictus model contained 19 less districts than the Ae . aegypti model , and 10 less districts that the model adjusting for both species .
Overall , our results highlight the extreme heterogeneity of dengue is Panama . We find that clusters and risk vary widely across both time and space , with adjacent districts and years experiencing vastly different rates of transmission . While this may complicate response programs that operate under assumptions of spatially and temporally homogenous risk , it highlights the need for comprehensive risk projection studies which can identify particularly regions that most require attention . Additionally , our results specifically highlight the changes incurred by adding vector data into systematic dengue risk projections . In the model where Ae . aegypti presence and absence was accounted for , more than double the number of districts were contained in clusters than the model where Ae . albopictus presence and absence was accounted for . The Ae . aegypti model also contained the highest number of districts with a relative risk > 1 , indicating more dengue cases than would be expected given baseline population levels . As an invasive species that has systematically replaced Ae . aegypti throughout numerous regions in its endemic range [15] , Ae . albopictus has been spreading throughout Panama for the previous 13 years [13 , 14] . Globally , while Ae . albopictus has been implicated in several small outbreaks [39] , the majority of dengue serotypes are thought to be transmitted by Ae . aegypti , due to its preference for both urbanized habitat [17 , 40] and human hosts [41 , 42] . Overall , based on our findings , we suggest that efforts be undertaken to further understand how the incorporation of vector surveillance data can affect risk projections , especially given the ubiquity of exploratory space-time scan statistics studies in the literature that did not utilize covariate surveillance data [26 , 43 , 44] . SATSCAN results can be useful in assessing risk when data granularity is low , as is often the case in developing regions with low-resource surveillance programs . Overall , these results can direct efforts to attain finer scale data in regions where general risk is deemed the greatest , at which point more confirmatory models can be applied , validating the role of certain covariates in space-time risk estimation . Balboa , for example , was identified as a cluster in all four models and had a steady presence of Ae . aegypti throughout the sample period as well as increasing presence of Ae . albopictus since 2006 . This district is semi-urban with approximately 2400 people spread across 400km2 area . Another district , San Miguelito in metropolitan Panama City , contained the most observed cases during our study period , despite being only 49 . 9km2 . This district can be characterized by high density housing and residents of relatively low socioeconomic status , which has previously been linked to increased vector-borne disease risk [45–48] . The staggering number of cases should be a cause for concern , yet its small geographic area may facilitate public health interventions such as vector control and community education . Overall , now that the identification of high risk districts at the national scale has been completed and informed by vector presence , the subsequent step of illustrating the comparative characteristics of each district relative to dengue transmission risk can be undertaken . Understanding what caused Balboa and San Miguelito to experience such high relative risk , for example , is the next task necessary for adjusting public health interventions to effectively address the needs and conditions of each district . Despite the longevity of our data and thoroughness of the surveillance efforts , there are clear considerations and limitations of our work which we would like to see addressed in future studies . First , we recognize that there is no way to statistically compare the outputs of the four models . This exploratory analysis was meant to illustrate that vector surveillance data can impact the location and duration of detected clusters , yet ranking the four models by predictive power is not possible with the statistical methods employed . Second , we recognize the spatial uncertainty by using districts for the analysis and our study is subject to the modifiable areal unit problem ( MAUP ) [44] , however , this is an exploratory study that seeks to guide targeted interventions . Finer-level data can identify the locations ( e . g . towns , blocks , homes ) within each district that are experiencing the greatest burden of dengue , yet since such fine scale surveys cannot realistically be employed at the national-scale , studies such as ours can indicate where to direct future surveillance efforts . Third , it is possible that the reported cases of dengue in certain districts are travel cases ( seeking treatment in a district different than actual residence ) , and while adjusting for the presence of Aedes can shed light on the districts where an individual is more likely to get infected with dengue , we cannot confirm that every patient was specifically infected in Panama . The lack of population data for more than one year across such a lengthy period is another limitation of this work . While the frequency of a census in Panama is on par with much of Latin America , this greatly impacts our ability to determine accurate prevalence rates year to year . Since linear interpolation is often inaccurate for non-linear trends like population growth rate , we would like to see more frequent population assessments conducted in regions where dengue is an ongoing risk , and while we understand that resources may not easily allow for this , the role of national census efforts in public health is often under-appreciated . An additional limitation originates with the vector surveillance methods employed . Values are reported as the number of houses containing larvae of each respective species . No information is given on the number of houses surveyed , and thus we were forced to transform the data into presence and absence . Had the total number of surveyed houses been reported , we would have been able to compute each district’s infestation rate , which would have provided a scaled and more nuanced covariate in the model . Lastly , no data was available on short-term cross immunity and long-term serotype specific immunity , which may have incurred bias in the model parameter estimations . Overall , it is key to recognize that adding vector surveillance data as a covariate changes the location , duration , and relative risk of dengue case clusters . As methods such as SaTScan are often used in mapping areas for public health intervention , we suggest that future efforts to utilize these tools in spatial vector epidemiology are conducted with an awareness that outputs can change when vector data is utilized as a covariate . Maps of clusters created without vector surveillance data may be missing key information that alters the distribution of the clusters in space-time , thus creating possible situations where public health efforts may not be planned in the areas which require it most . Although unadjusted cluster analysis is a valuable and commonly utilized tool for public health officials to identify high risk areas of vector-borne disease , our study illustrates the role that incorporating relevant covariates can play in altering the model output . While this has been demonstrated in cancer cluster studies [48 , 49] , this is the first use of covariates in space-time cluster detection modelling of neglected tropical disease . With this comes potential to expand into other classes of covariates . For example , in addition to vector surveillance data , we support the incorporation of additional covariates such as vector genetic background , climate , vegetation , and land cover to dengue cluster models . Host population characteristics , such as housing density , relative isolation , and connectivity may also influence dengue risk in space-time scan statistic models . In general , the dengue transmission model contains numerous variables that we would have been interested in incorporating , had adequate data been available . Still , we demonstrate that vector surveillance clearly provides valuable information in the determination of virus case clusters , and thus should be conducted alongside virus surveillance so that it may be included in modelling efforts .
|
Dengue cases have increased in tropical regions worldwide owing to urbanization , globalization , and climate change facilitating the spread of Aedes mosquito vectors . National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed . Yet , most estimations of dengue risk so far derive only from disease case data , ignoring Aedes occurrence as a key aspect of dengue transmission dynamic . Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"geographical",
"locations",
"panama",
"animals",
"north",
"america",
"mathematics",
"algebra",
"infectious",
"disease",
"control",
"insect",
"vectors",
"central",
"america",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"aedes",
"aegypti",
"epidemiology",
"vector",
"spaces",
"disease",
"vectors",
"insects",
"arthropoda",
"people",
"and",
"places",
"infectious",
"disease",
"surveillance",
"mosquitoes",
"eukaryota",
"linear",
"algebra",
"disease",
"surveillance",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"physical",
"sciences",
"organisms"
] |
2019
|
Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data
|
Mozambique suffers recurrent annual cholera outbreaks especially during the rainy season between October to March . The African Cholera Surveillance Network ( Africhol ) was implemented in Mozambique in 2011 to generate accurate detailed surveillance data to support appropriate interventions for cholera control and prevention in the country . Africhol was implemented in enhanced surveillance zones located in the provinces of Sofala ( Beira ) , Zambézia ( District Mocuba ) , and Cabo Delgado ( Pemba City ) . Data were also analyzed from the three outbreak areas that experienced the greatest number of cases during the time period under observation ( in the districts of Cuamba , Montepuez , and Nampula ) . Rectal swabs were collected from suspected cases for identification of Vibrio cholerae , as well as clinical , behavioral , and socio-demographic variables . We analyzed factors associated with confirmed , hospitalized , and fatal cholera using multivariate logistic regression models . A total of 1 , 863 suspected cases and 23 deaths ( case fatality ratio ( CFR ) , 1 . 2% ) were reported from October 2011 to December 2015 . Among these suspected cases , 52 . 2% were tested of which 23 . 5% were positive for Vibrio cholerae O1 Ogawa . Risk factors independently associated with the occurrence of confirmed cholera were living in Nampula city district , the year 2014 , human immunodeficiency virus infection , and the primary water source for drinking . Cholera was endemic in Mozambique during the study period with a high CFR and identifiable risk factors . The study reinforces the importance of continued cholera surveillance , including a strong laboratory component . The results enhanced our understanding of the need to target priority areas and at-risk populations for interventions including oral cholera vaccine ( OCV ) use , and assess the impact of prevention and control strategies . Our data were instrumental in informing integrated prevention and control efforts during major cholera outbreaks in recent years .
Cholera is an acute , diarrheal illness caused by infection of the intestine with the bacterium Vibrio cholerae . Its epidemics have been continuously reported from Southern Africa since its reintroduction on the continent in 1970 [1 , 2] . Today , cholera remains a significant cause of morbidity and mortality , and a key indicator of lack of adequate infrastructure and structural development , specifically an insufficient supply of drinking water and inadequate sanitation [3 , 4] . While there has been significant work done in Africa to quantify the magnitude of cholera as a public health problem in recent years , individual and population characteristics in specific settings remain ill defined . Since 1973 , Mozambique has reported cholera almost yearly . Cholera in Mozambique is an endemic disease with seasonal epidemic peaks . The usual cholera season is between January and March with annual incidences ranging from 0 to 211 per 100 , 000 population with periodically high case-fatality ratios ( CFRs ) [5–9] . Disease burden data are critical for making evidence-based decisions on public health interventions , including water , sanitation , and hygiene ( WASH ) and use of oral cholera vaccine ( OCV ) . In Mozambique , epidemiological surveillance data are used to identify districts at risk of cholera and to identify contributing factors such as an insufficient supply of drinking water , sanitation practices harmful to health ( such as open defecation , using dirty or contaminated water , and handling and selling unhygienic food ) , and natural disaster [5] . Africhol is a multi-centric project that was launched in 2009 with funding from the Bill & Melinda Gates Foundation . It consists of a consortium of 11 African countries and non-governmental organizations , seeking to collect epidemiological and microbiological information regarding the occurrence of cholera in Africa to better inform intervention strategies ( http://amp-vaccinology . org/activity/cholera-in-africa ) [10] . In Mozambique , the Africhol project was implemented in 2011 , to conduct prospective surveillance in dedicated surveillance zones with the main objective of assessing cholera burden . The current study presents the results from five years of surveillance in terms of disease burden , population characteristics , and risks factors in specific surveillance zones to support appropriate interventions for cholera control and prevention .
The surveillance protocol was approved by the Mozambican National Bioethics Committee for Health . The informed oral consent was obtained from all participants and documented by a specific question on the case report form . The Ministry of Health ( MOH ) considered that the Africhol project was integrated in their national epidemic disease surveillance and response and subject to the laws and regulations , and thus did not require written consent . For minors below 18 years , informed oral consent was obtained from parents , guardians , or next of kin , on behalf of the child . Oral consent information provided enough details to the study participants about the stool and blood sample collection process as well as the planned use of these specimens . In 2011 , the project was first implemented in two enhanced surveillance zones: Beira city in Sofala province , and Mocuba district in Zambézia province . In 2013 , a third surveillance zone was established in Pemba city , Cabo Delgado province , following several cholera outbreaks there . These three areas were among the 8 districts out of the country's 145 districts with the highest number of cholera cases ( >1000 ) reported to the Ministry of Health from 2009 to 2011 [5] . Beira city is the third largest city in Mozambique with a population of 463 . 442 . Mocuba district includes 404 . 749 inhabitants and is rural area . Pemba City , with a population of 218 . 152 , is the capital city of northernmost Cabo Delgado province . It has preserved largely rural characteristics with low population density . The enhanced surveillance zones were selected based on: a high incidence of cholera during the previous five years; a sufficient large population denominator to determine incidence; a history of reliably providing surveillance reports on suspected cholera cases to the National MOH—Surveillance System; reliable health care access for the local population and diagnostic facilities to allow case identification . In these areas we included all health centers that treated the local population for severe diarrhea and collected additional data through community investigations . Usually , specific cholera treatment centers ( CTC ) were open only during cholera outbreak periods when declared by health provincial authorities . According to the area , the delay to open CTCs varied . Outside outbreaks patients with severe diarrhea were treated at general health centers or hospitals . Africhol was integrated into the routine cholera surveillance and provided additional support beyond that supported by the MOH . For example , Africhol provided funding for specific additional staff such as a country focal point , a coordinator and a surveillance officer for each zone who ensured the proper conduct of the study , data collection , data quality and supervision of data entry . Also , in the enhanced surveillance zones , whenever possible most suspected cases were culture-tested , while in the routine system , only a few suspected cases at the start , during and at the end of an epidemic were usually tested for cholera . Africhol provided repeated refresher training for laboratory staff . Finally , Africhol provided standard case-report forms ( CRFs ) for data collection , laboratory standard operating procedures , support for specimen transport , support for serotyping , and technical assistance from the Africhol international team . Data was collected prospectively through Africhol since its implementation in October 2011 until December 2015 in the three surveillance zones of Beira , Mocuba , and Pemba ( Fig 1 ) . This information was collected year round [11] . In addition , outside these enhanced surveillance zones , and using the same Africhol tools , we also investigated individual cholera outbreaks in three sites that experienced the greatest number of cases during the same time period , in Montepuez district ( Cabo Delgado province ) during February 2012 , Cuamba district ( Niassa province ) during January-February 2012 , and Nampula city ( Nampula Province ) every year from 2012 to 2015 , as well as a few cases from other zones ( mainly from Tete , Quelimane and Lichinga districts during outbreaks occurring in 2015 ) . Those cases were investigated mainly from CTCs . For Africhol , we defined a suspected case as an individual at least one year of age or older , with acute watery diarrhea and dehydration or must have died from acute watery diarrhea . A cholera case was considered confirmed when Vibrio cholerae O1 was isolated from a stool sample or rectal swab specimen by microbiological culture . These definitions differed slightly from those used by the national MOH routine surveillance system for which a suspected case is defined as any patient aged two years or older , with acute watery diarrhea and abdominal pain , and profuse watery stools ( type rice water stools ) , with vomiting or rapid dehydration . According to MOH definitions , once a case of cholera in a neighborhood has been confirmed , suspected cases are then considered cholera for a varying period of time , ranging from two to six months depending on the district . Africhol data were collected through a CRF which included demographic , clinical , behavioral , and laboratory information . The rainy season was defined as the months of October to March , and the dry season from April to September . Rectal swabs from suspected cholera patients were collected by health staff for laboratory investigation , and then transported from the health unit to the regional or provincial laboratories in Cary Blair transport media at room temperature conditions by a private courier services company once a week . In local laboratories samples were pre-enriched in alkaline peptone water and pre-incubated at 35–37°C for six to eight hours , followed by V . cholerae diagnostic tests carried out by sub-culturing on thiosulphate citrate bile salts sucrose agar ( TCBS ) . After pre-processing at provincial level , the isolates cultured positive ( showing yellow colonies ) on TCBS were sent to the National Microbiology Laboratory at the Instituto Nacional de Saúde ( INS ) in the capital Maputo for further confirmation and quality control by standard biochemical tests and serology using polyvalent , anti-Ogawa , and anti-Inaba antisera . An aliquot of V . cholerae isolates was stored in the INS lab at -80°C and a copy was sent to the National Institute for Communicable Diseases ( NICD ) in South Africa for quality control and molecular subtyping and results were previously published elsewhere [12] . Blood samples were systematically taken for malaria and HIV testing in all sites from those patients that were in conditions to consent and accepted to have an HIV test . HIV counseling and testing were performed according to Mozambique’s national guidelines , which include confidentiality , counseling , and informed consent . Current guidelines for rapid testing call for a two test serial testing algorithm that screens with Determine HIV-1/2 ( Alere , USA ) , and confirmation with Uni-Gold HIV ( Trinity Biotech , Ireland ) . Malaria tests were performed by a health professional according to the national guidelines using one of several rapid tests available to the National Health Service .
From October 2011 to December 2015 , a total of 1 , 863 suspected cholera cases were reported through the Africhol surveillance system . Among them , 1 , 010 ( 54 . 2% ) were reported from the surveillance zones ( Fig 2 ) and 853 ( 45 . 8% ) from cholera outbreaks investigated in Cuamba , Montepuez , and Nampula ( Fig 3 ) . Additionally , a few cases were reported episodically from other zones , including mainly from Tete , Quelimane , and Lichinga during outbreaks occurring in 2015 ( n = 151 ) . Overall , the majority of cases ( 81 . 7% ) were reported during the rainy season ( October-March ) , especially in outbreak zones compared to surveillance sites ( 96 . 0% vs . 69 . 7% , p<0 . 001 ) . Male-to-female sex ratio was 1 . 14 and varied between sites , from 1 . 49 in Nampula to 0 . 75 in Montepuez . The median age was 20 years ( interquartile range ( IQR ) , 9–32 ) . There was a similar age distribution in all zones , except in Beira , which had the lowest median age ( nine years ) with the highest proportion of suspected cases under or equal to five years ( 38 . 4% , p<0 . 001 ) ( Table 1 ) . There were 101 children aged 12–23 months–mainly in Beira , thus representing 33% of the overall ‘below 5 years-old” age category ( 308 ) . The annual district-level incidence of suspected cases in surveillance zones ranged from 0 . 04 per 10 , 000 in Beira ( 2015 ) to 29 . 7 in Pemba ( 2013 ) . In outbreak sites , the maximum attack rate was seen in Nampula city with 16 . 1 cases per 10 , 000 during the 2015 outbreak ( Table 2 ) . A total of 972 suspected cases ( 52 . 2% ) were tested and 228 ( 23 . 5% ) of them were confirmed ( Table 3; Fig 4 ) . The rate of confirmed cholera varied between age groups ( p<0 . 001 ) , with the lowest rate among the age group ≤5 years ( 26/216 , 12% ) , compared with confirmation rates ranging from 21% to 31% for other age groups . However , after adjusting these results on the surveillance zone , the confirmation rate was no longer associated with age . Only 4 . 5% of the children 12–23 months who were tested ( 4/89 ) were confirmed to be cholera by culture . Among the 207 children aged 2–5 years , 17 . 3% of those tested ( 22/127 ) were culture positive . A total of 23 deaths were identified ( CFR suspected , 1 . 2% ) , all of which occurred during the rainy season . The CFR was significantly higher in outbreak zones than surveillance sites ( 2 . 0% vs 0 . 6% , p<0 . 01 ) , and in males compared to females ( 1 . 8% vs . 0 . 6% , p = 0 . 02 ) . A total of 3 deaths occurred out of 228 cholera confirmed cases ( CFR confirmed cases = 1 . 3% ) , all of them in Pemba city ( Table 3 ) . In outbreak zones , most suspected cases were notified from CTCs , whereas in surveillance zones it varied ( Table 1 ) . The majority of cases consulted the health facility the same day or the day following symptom onset ( 70 . 4% ) . Most suspected cases were hospitalized ( 66 . 1% ) , except in Beira ( 5 . 6% ) . The majority of cases reported symptoms of watery stools ( 78 . 0% ) , dehydration ( 66 . 6% ) , and vomiting ( 59 . 9% ) , but this varied between sites ( S1 Table ) . Among all suspected cholera cases , 5% tested HIV positive , but most cases did not have HIV status determined ( 70% ) . The highest positivity rate was seen in Beira city ( 14% ) . For malaria , 8% of all suspected cholera cases had a positive rapid test result , with a high proportion in Mocuba district ( 24% ) , while 62% had unknown malaria status . Most of the suspected cases had no contact with another suspected case , nor had they attended a funeral or a social event in the seven days prior . The public tap was the most common source of drinking water at home ( 48 . 2% ) , followed by a shallow well ( 18 . 3% ) . The majority of cases reported drinking untreated water ( 62 . 3% ) . Among those who treated water , bleach/chlorine was the most common treatment procedure ( 38 . 2% ) , followed by boiling water ( 22 . 3% ) ( S2 Table ) . Factors independently associated with confirmed cholera in the multivariate analysis were: living in Nampula city district , the year 2014 , HIV positive status , and the primary water source for drinking ( Table 4 ) . Factors associated with hospitalization included: male gender; young age; location; longer duration between date of onset and consultation; presence of rice water stools; vomiting; abdominal pains; leg cramps; HIV positive status; and receiving IV fluids before the consultation ( S3 Table ) . Factors associated with death of suspected cholera cases included: male gender; short duration between disease onset and consultation; rice water stools; abdominal pain; and leg cramps ( S4 Table ) .
Our analysis is in line with previous published data showing that cholera in Mozambique is marked by spatial heterogeneity and seasonality , with a high concentration of cases during the rainy period between January and March [5] and inter-epidemic periods largely free of confirmed cases . Each surveillance zone showed a different pattern of suspected cases distributed over time , although there were clear similarities in the seasonality of suspected cases between the surveillance and outbreaks zones . This heterogeneity may be explained by the hypothesis that in Mozambique outbreaks do not evolve locally , but rather follow cholera re-introduction from distant epidemic regions [7] . Our study allowed for the measurement of cholera incidence prospectively . Incidence of suspected cases varied widely between sites and between years within the same site . The incidence based on suspected cases only may be overestimated , given the low culture confirmation rate , as was shown previously in other Africhol countries [9] . There were 23 cholera deaths during the enhanced surveillance period ( CFR , 1 . 2% ) , similar to what was found previously in Mozambique ( 0 . 9% during reported outbreaks in the period 2009–2011 ) . Other studies showed the CFR was likely much higher under non-research conditions when immediate rehydration and transportation to hospitals may not be available [13] . Previous studies in Mozambique using surveillance data showed that 90% of deaths and 70% of cases occurred during the first six weeks of the outbreak [5] . Case distribution by sex and age group showed common patterns in all zones , except for Beira , where a high proportion of suspected cases occurred in persons under five years of age . In these areas , young children with symptoms of acute diarrhea are often brought to healthcare facilities by their mothers for treatment , while adults may be more hesitant to visit healthcare facilities . The proportion of confirmed cases in this age group in Beira was low ( 3 . 8% ) , but was similar to the other age groups from this zone ( ranging from 0 to 9% ) . In another study in Beira , cholera incidence was higher among children below five years of age compared to older age groups [14] . Overall , the proportion of confirmed cases was lower in the one- to five-year-old age group than in older age groups . As we did not have the mandate or resources to study other pathogens that might also be associated with acute diarrhea in young children we cannot ascertain the attributable fraction of cholera to all-cause diarrhea . In the Global Enteric Multicenter Study ( GEMS ) conducted in four African sites and three Asian sites among children below 5 years , most attributable cases of moderate-to-severe diarrhea were due to four pathogens: rotavirus , Cryptosporidium , enterotoxigenic Escherichia coli producing heat-stable toxin ( ST-ETEC ) , and Shigella [15] . Nonetheless , in one of the study sites located in southern Mozambique ( Manhica ) , with traditionally low cholera incidence , the main pathogens identified among children 2–5 years-old were Shigella ( 14 . 9% of all moderate-to-severe diarrhea ) and Vibrio cholerae ( 8 . 3% ) . Our results indicate the suspected cases tended to rapidly seek care . This might be explained by the fact that people have the perception that cholera is a serious disease likely to result in death if left untreated . In Beira , there was a very low cholera confirmation and hospitalization . One of the possibilities for this finding is that some of the large outbreaks in Beira might be attributed to other pathogens . Of the expected known exposures or risk factors , the only one that was independently associated with confirmed cholera in our study was the primary source of drinking water . Drinking water from unknown sources posed a greater risk for cholera . This could indicate wide-spread circulation of V . cholerae in the drinking water sources of the affected areas during outbreak periods resulting in a higher proportion of common source versus person-to-person transmission . In line with this , most cases occurred during the rainy season ( 82% ) , especially in outbreak sites with high incidence , with heavy floods possibly deteriorating water and sanitation system and triggering water borne transmission . Further studies using molecular biology methods , innovative approach to evaluate risk factors for cholera infection ( eg . including community controls ) ; small scale spatial epidemiological analysis and other studies such as description of the secondary infection at household and neighbor level should further elucidate the modes of cholera transmission . In parallel , majority of suspected cases reported that they had not attended a mass gathering or market in the seven days before symptom onset . This would underline the over proportional importance of continuous exposure through the water source at the place of residence rather than at isolated mass gathering events . Looking at all factors associated with confirmed cholera , we can see that risk factors are not that different for suspected and confirmed cases . This would reflect the common risk factors for cholera and other water-borne diarrheal diseases . Although some association between HIV status and confirmed cholera was shown ( adjusted OR , 95% CI: 4 . 5 [1 . 6–12 . 2] ) , this result should be interpreted with caution , because the majority of suspected cases ( 70% ) and confirmed cases ( 54% ) had unknown HIV status . Also , most HIV tests that were carried out and produced positive results came from one site ( Beira ) . Additionally , we could not differentiate any HIV infection from HIV infection with immunosuppression , and HIV infection may simply be a marker for persons with less access to clean water and sanitation . A previous study in Beira indicate that persons with HIV infection had an increased risk of cholera compared to those without HIV infection , although this risk did not reach statistical significance ( p = 0 . 08 ) , and no information on immunosuppression status of enrolled patients was provided in this study [16] . The different cholera characteristics between zones highlight the need to tailor intervention strategies to the specific local setting and at-risk populations and are useful for evidence-based decision making . The data presented here were used to direct interventions to prevent/treat cholera such as timely and solid cholera surveillance system , improved environmental management in particular continued access to safe water and proper sanitation , and the adequate use of cholera vaccines as a complementary immediate measure . The high-risk zones where cholera outbreaks repeatedly occur ( e . g . , Nampula city in Nampula province , Mocuba district in Zambezia , and Pemba city in Cabo Delgado province ) are so-called “cholera hotspots” and may benefit from preventive or reactive cholera immunization campaigns in combination with other cholera control activities ( such as WASH activities ) , as recommended by the World Health Organization ( WHO ) [17] . The persistence of cholera over decades and the wide-spread risk of drinking contaminated water in those areas will require a decisive comprehensive effort from all stakeholders to improve the situation . No plans for such interventions are yet known and they will likely take years . In contrast OCV can provide rapid protection . A previous OCV campaign had been conducted in Beira city in 2003–2004 and showed a high vaccine effectiveness of one or more doses ( 78% , 95% CI: 39–92 ) , even in a setting with high HIV prevalence [6] . More recently , in October 2016 , another OCV campaign was conducted in Mozambique in Nampula city , targeting high-risk neighborhoods; monitoring and evaluation of this campaign is currently on-going . Although our study provides valuable information about cholera surveillance in Mozambique , there are some limitations . Only 52 . 2% of suspected cases were tested and 23 . 5% were microbiologically confirmed . This limited capacity to confirm cases by culture combined with limited sensitivity of culture lowered the overall sensitivity of our surveillance [9] . Our diagnostic procedures relied exclusively upon culture results , which can be influenced by various factors including collection , transport and storage conditions , training of human resources or previous antibiotic exposure . Also , culture has a limited sensitivity which can translate in a low negative predictive value . It would have been preferable to use PCR testing , however , given resource limitations , we were unable to do so and thus our burden estimations need to be interpreted accordingly . Conversely , acute diarrhea cases might also have been reported as cholera while being caused by other pathogens . There was a high proportion of certain variables missing , which made it difficult to analyze the risk factors associated with confirmed cholera . In addition , data gaps limited our ability to conduct a robust examination of the association of cholera with malaria or HIV , including any differences in associations that may be due to immunosuppression status . The selection criteria for our surveillance included functional surveillance system and functional health structures with access to appropriate case management . Therefore the mortality related estimates cannot be generalized for the entire country which would likely underestimate mortality and CFRs . Moreover , our study did not account for the cholera cases and deaths in the community when the patients did not visit the health facilities . In this analysis we presented cholera cases in Africhol surveillance sites and some cholera outbreaks from 2011–2015 . Compared with simultaneous official figures reported by the National Surveillance System , our surveillance system showed fewer cases reported in some of the districts , indicating that the Africhol surveillance was not exhaustive since it was limited to certain surveillance zones . However , the introduction of case-based surveillance methodology into the public-health system has allowed national staff to appreciate its value and expand this approach to other infectious diseases thereby reinforcing national surveillance capacity overall .
Our study provides the most comprehensive information on cholera in Mozambique available in recent years . This study does not aim to replace the national system but to assess the characteristics of populations at risk of cholera and risk factors in specific sites through an enhanced case-based surveillance . There is a need for continued surveillance , detailed data and stronger laboratory capacity to target prevention and control efforts , including locally adapted WASH interventions and preemptive use of OCV . In order to improve quality and access to safe water and sanitation , the Mozambican government has established investment funds and water supply assets which shall be used for public investment into the water supply system and the management of several companies across Mozambique . The use of burden data in smaller-scale geographic units will help target higher risk neighborhoods ( bairros ) within the identified districts , and this work is ongoing .
|
Cholera is a major public health problem in many countries in sub-Saharan Africa . In Mozambique , annual outbreaks occur but the place and time may vary . Africhol was implemented in Mozambique in 2011 to generate more detailed information on disease burden and characteristics to support appropriate interventions for cholera control and prevention in the country . The study was conducted in six different zones , where patients with cholera symptoms seeking care at a health facility were asked questions on socio-demographic characteristics , their symptoms , and behaviors that may increase cholera risk . Stool samples were also taken to test for the presence of cholera infection ( Vibrio cholerae ) . Among the 1 , 863 patients , more than half were tested for cholera , and among those tested , less than one in four was infected with the pathogen . About 1% of patients died from cholera . Our study helps to understand the burden of cholera in different areas of the country , and the characteristics of the people infected . It is important to continue the surveillance of this disease to choose the most appropriate control and preventive interventions , and to apply them in precisely the right place .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion",
"Conclusion"
] |
[
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"water",
"resources",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"tropical",
"diseases",
"geographical",
"locations",
"microbiology",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"bacterial",
"diseases",
"age",
"groups",
"diarrhea",
"rna",
"viruses",
"signs",
"and",
"symptoms",
"gastroenterology",
"and",
"hepatology",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"africa",
"infectious",
"diseases",
"cholera",
"natural",
"resources",
"medical",
"microbiology",
"epidemiology",
"hiv",
"microbial",
"pathogens",
"people",
"and",
"places",
"mozambique",
"infectious",
"disease",
"surveillance",
"diagnostic",
"medicine",
"viral",
"pathogens",
"disease",
"surveillance",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"lentivirus",
"organisms"
] |
2017
|
Multi-site cholera surveillance within the African Cholera Surveillance Network shows endemicity in Mozambique, 2011–2015
|
The evolution of multiple antibiotic resistance is an increasing global problem . Resistance mutations are known to impair fitness , and the evolution of resistance to multiple drugs depends both on their costs individually and on how they interact—epistasis . Information on the level of epistasis between antibiotic resistance mutations is of key importance to understanding epistasis amongst deleterious alleles , a key theoretical question , and to improving public health measures . Here we show that in an antibiotic-free environment the cost of multiple resistance is smaller than expected , a signature of pervasive positive epistasis among alleles that confer resistance to antibiotics . Competition assays reveal that the cost of resistance to a given antibiotic is dependent on the presence of resistance alleles for other antibiotics . Surprisingly we find that a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds , that some double resistances entail no measurable cost , and that some allelic combinations are hotspots for rapid compensation . These results provide additional insight as to why multi-resistant bacteria are so prevalent and reveal an extra layer of complexity on epistatic patterns previously unrecognized , since it is hidden in genome-wide studies of genetic interactions using gene knockouts .
Epistasis occurs when the phenotypic effect of a mutation in a locus depends on which mutations are present at other loci . When the phenotype of interest is fitness , the existence of such genetic interactions can constrain the course of evolution . The strength and form of epistasis is relevant for the evolution of sex , buffering of genetic variation , speciation and the topography of fitness landscapes . While epistasis between gene deletions [1] , [2] has been the focus of recent research , interactions between randomly selected alleles , which are of the greatest evolutionary interest has not [3] . Since mutations that confer antibiotic resistance are known to affect bacterial fitness , levels of epistasis amongst such mutations may determine how multiple resistance evolves . Such knowledge can be used to understand and predict what type of resistance mutations are likely to be segregating in microbial populations [4] . To understand and predict the evolution of multiple resistance it is of key importance to know how the fitness of sensitive and resistance bacteria is affected in different environments , particularly both in the presence and in the absence of drugs . Recent studies have shown that interactions exist amongst pairs of antibiotics , i . e . resistance to one drug affects the action of another drug [5] , [6] . In particular the combination of pairs of drugs has been studied and the combinations have been characterized as additive , synergistic , antagonistic or suppressive . Importantly it has been found that in certain drug combinations ( suppressive ) one of the antibiotics may render the treatment more effective against its resistant mutant than against the wild type [6] . However , we are lacking data on genetic interactions amongst single nucleotide mutations conferring antibiotic resistance in a drug free environment , i . e . the cost of multiple resistance . With the occurring increase in frequency of multiple resistant bacteria and the public health problems associated with it , knowledge on the type and strength of epistasis is of most importance in understanding the evolution of multiple resistance and , ultimately , the planning of new strategies for human intervention . One can think that a way to halt the spread of resistance to a given antibiotic is to stop the use of that antibiotic . In the absence of the antibiotic for which resistance has been acquired , antibiotic-resistance mutants have a fitness cost when compared to sensitive bacteria [7] , [8] . However , when infection is not resolved , a common strategy is to continue treatment with a different antibiotic to which the infecting bacteria are still susceptible . Unfortunately this strategy has led to rapid increase in multiple-drug resistance and not to the loss of resistance to the first treatment , as it would be desired [9] . This raises the possibility that multi-resistant mutations are not independent . If so , when resistance first develops the following question should be asked: if a pathogenic strain is resistant to antibiotic X , which antibiotic should be administered as a second treatment ? Clearly , the strategy will depend not only on knowledge about the fitness costs of single resistance mutations but also on the level of genetic interactions – epistasis - between the alleles that underlie those phenotypes . When epistasis exists , it can be positive ( antagonistic or alleviating ) or negative ( synergistic or aggravating ) . If strong negative epistasis amongst drug resistance alleles is found , then the cost of multiple resistances is high and one can expect multi-drug resistant microbes to be counter selected and disappear very rapidly in the absence of either drug . On the contrary , a much more worrying scenario is the existence of positive epistasis , for it implies that the expected time for elimination of multiple resistance , even if antibiotic pressure is inexistent , will be much longer and multiple resistant bacteria are expected to accumulate in the population . Here we quantify the degree of genetic interactions on cellular fitness in an antibiotic free environment for point mutations which confer resistance to commonly used antibiotics of three different classes . We have focused on resistances to: ( i ) the quinolone nalidixic acid , which inhibits DNA replication by binding to DNA gyrase; ( ii ) rifampicin , which belongs to the rifamycins class of antibiotics that bind to the β-subunit of RNA polymerase thereby inhibiting transcription; and ( iii ) streptomycin , an aminoglycoside that binds to the ribosome and inhibits elongation of protein synthesis [10] . We find that epistasis is allele specific and that the vast majority of allelic combinations exhibit positive epistasis . The costs of double resistance are therefore smaller than what one would expect if they were independent . Interestingly we found several cases of sign epistasis , which implies that mutations conferring resistance to a new antibiotic are compensatory , i . e . alleviate the cost of resistance present on another locus .
To study the degree of epistatic interactions amongst alleles that confer antibiotic resistance we started by selecting a series of Escherichia coli spontaneous mutants resistant to commonly used antibiotics of 3 different classes: nalidixic acid , rifampicin , and streptomycin ( Methods ) . From a panel of 120 sequenced clones that carry a single nucleotide change , we obtained 19 different classes of clones ( Table S1 ) with spontaneous mutations in gyrA , rpoB and rpsL , which are the correspondent common target genes of resistance to nalidixic acid , rifampicin , and streptomycin , respectively . Resistant bacteria with mutations in the same amino acids as those collected here are segregating in microbial populations [4] , [11] . We should notice that our procedure for isolating clones carrying antibiotic resistance requires that viable colonies of resistant bacteria can be formed and detected . Any mutations that can arise and cause very high fitness costs cannot therefore be accounted for in this study . Given that highly deleterious mutations are unlikely to segregate in natural populations [4] , genetic interactions amongst such mutations are likely to be of less clinical importance , unless these can be very easily compensated for . We determined the cost of resistance of each of the clones in the absence of antibiotics . This was performed by measuring relative competitive fitness of the resistant strains against a marked sensitive strain through a competition assay in liquid LB medium without antibiotics [12] ( Methods ) . Figure 1A shows the distribution of fitness costs of each of the mutants . The genotype of each mutation as well as its fitness costs are provided in Table S1 . On average the cost of antibiotic resistance was 9% . A Kolmogorov-Smirnov test does not reject the exponential distribution ( P = 0 . 85 ) , a commonly used model to describe the distribution of deleterious mutations in several organisms [13] . Mutations that confer resistance to nalidixic acid had a lower average fitness cost ( 3% ) than streptomycin or rifampicin ( 13% and 9% , respectively ) . Similar levels of fitness cost were found previously in resistant mutants of Mycobacterium tuberculosis [4] , [14] and E . coli [15] , [16] . To study the fitness cost of double resistance and measure epistasis we constructed by P1 transduction all possible pairwise combinations ( 103 ) between different resistance alleles . These correspond to 5×11 streptomycin/rifampin , 5×3 streptomycin/nalidixic acid and 11×3 rifampin/nalidixic acid possible combinations . We measured the fitness of the obtained double resistant by competition assays , determined its cost and compared it with the cost that would be predicted if there was no epistasis . Figure 1B shows that the average cost of double resistance is less than twice the average cost of a single resistance ( compare with Figure 1A ) , indicating positive epistasis . Fitness of double resistance was used to calculate pairwise epistasis . Pairwise epistasis , ε , between locus A and B can be measured as follows [17] . If WAB is the fitness of the wildtype , WAb , WaB are the fitnesses of each of the single mutants and Wab that of the double mutant then:When the value of the costs is small then this measure becomes very similar to the difference between the cost of double resistance and the sum of the costs of each resistance . Strikingly , the majority of mutations show positive epistasis . 68% of the points are above the line in Figure 2A and from the clones that show significant epistasis 15% show negative epistasis and 42% show positive epistasis . The later ones correspond to clones where the cost is less than the sum of the costs of each resistance ( see also Figure 2C for the specific combinations of alleles showing significant positive epistasis ) . It has been suggested from theoretical modeling of RNA secondary structures and from studies in digital organisms – computer programs that mutate and evolve- [18] that epistasis and the fitness effect of mutations may be correlated , such that mutations with larger effects are more epistatic . In our data we find significant correlations between the strength of epistasis ( deviation from zero in absolute value ) and the costs of the mutations ( Pearson's correlation r = 0 . 61 , P<0 . 001 ) , which support this theoretical prediction . We also find a marginally significant correlation between the value of epistasis , ε , and the cost of the mutations ( Pearson's correlation r = 0 . 19 , P = 0 . 06 ) . Figure 2B shows the distribution of the ε values , the median is significantly positive ( median = 0 . 025 , Bootstrap 95% CI [0 . 016; 0 . 032] ) , and its value corresponds to about 1/3 of the average cost of each single mutation . From the allelic combinations for which there is significant epistasis ( 53% ) , only 27% give rise to negative epistasis , whereas 73% of these combinations result in positive epistasis . Epistatic interactions were observed more frequently between mutations in gyrA and in rpsL , with high frequency of positive epistasis but also extreme cases of negative epistasis ( synthetic sub-lethal , Figure 2C ) . Combination of gyrA and rpoB mutations produced the lowest frequency of negative epistasis ( 6% ) , which suggests that the sequential prescription of antibiotics leading to these resistances may easily result in multi-resistance development . Focusing on the mutations we notice that the interactions are not gene but allele specific . For example R529H and H526L mutations in rpoB notably showed very high negative epistasis frequency , whereas four other mutations ( D516V , H526N , I572F and S512F ) in the same gene showed no negative epistasis regardless of the combination , and D516V and I572F mutations revealed high frequency of positive epistasis . The latter are potential candidates to segregate in natural populations . The rpoB H526D mutation is a specific example where its epistasis is highly dependent on the particular allele of the second mutation: rpoB H526D interacts positively with rpsL K43N , but negatively with rpsL K43T . Interestingly , the rpoB H526D mutation has been found in multi-drug resistant M . tuberculosis strains and also in this organism its cost was shown to depend on the genetic background [4] . This suggests two important predictions: that we should find resistance alleles with strong positive interaction segregating at higher frequencies ( such as rpoB H526D/rpsL K43N ) in natural populations and that these genetic interactions should also apply to microorganisms other than E . coli . Recently it has been shown that an important type of epistasis , which is known as sign epistasis [19] , may constrain the evolution of resistance to high penicillin concentrations [20] . Sign epistasis happens when the sign of the fitness effect of a mutation ( deleterious or beneficial ) is itself epistatic , i . e . sign epistasis exists when a mutation is deleterious on some genetic backgrounds but beneficial on others . This form of epistasis , if common , may give rise to multiple peaks on the fitness landscape . In the context of antibiotic resistance , the implication is simple: if sign epistasis is pervasive then it will be much more difficult to move from a scenario of multiple drug resistance to a scenario where all the bacteria are sensitive , even if antibiotic selection pressure is stopped . Experimental evolution studies have shown that , within hundreds of generations , mutations which compensate for the cost of antibiotic resistance ( compensatory ) are more likely to occur [16] , [21] , [22] than revertants [14] . This suggests that sign epistasis might have an important role for the evolution of antibiotic resistance . Within the allelic combinations studied , 12% of our clones showed unexpected sign epistasis between drug resistance alleles . This corresponds to double mutants that have a fitness bigger than the fitness of at least one of the single mutants ( Figure 3 ) , and here it means that the mutation conferring resistance to a new antibiotic is beneficial ( compensatory ) when in a genetic background that contains a mutation conferring resistance to a different antibiotic . This is the worst possible scenario for the host and the best possible for the microbe . Given that a particular mutation was just selected by application of an antibiotic , evolution by natural selection makes it likely that the fixation of a mutation conferring resistance to another antibiotic will occur , even if selective pressure is not applied . Specifically , we find that the same mutation conferring streptomycin resistance ( rpsL K88E , Figure 3 ) can be compensated by different mutations that confer rifampicin resistance showing that rifampicin treatment should be avoided in patients infected with rpsL K88E streptomycin resistant mutants . Given these results , knowledge of both the clinical history of patient antibiotic use as well as the specific genotypes associated with a given resistance is recommended for predicting the optimal clinical outcomes . Another worrying class of clones that we found corresponds to combination of double resistant mutations that entail no significant cost . These are 6 out of the 103: rpsL K43R/gyrA D87G , rpsL K43R/gyrA D87Y , rpoB H526N/gyrA D87G , rpoB H526N/gyrA D87Y , rpoB D516V/gyrA D87Y , rpsL K43R/rpoB H526N . These mutants have no disadvantage against the wild-type . Although this constitutes a small percentage , it is nevertheless of extreme importance since for these double mutants little , if any , compensation is required for restoring the competitive ability of the wild-type . Given the known association between the cost of resistance mutations measured in the laboratory and the frequency at which they are found in clinical settings has been demonstrated , at least in M . tuberculosis [4] , we predict that those combinations of double mutations are the ones which are more likely to be found . Future studies are planned to test this prediction , although it should be noted that the target for resistance may vary between species and environmental conditions [23] . In the opposite extreme we found five clones for which the transduction efficiency was very low ( combinations shown in black , Figure 2C ) which might correspond to combinations of mutations that must entail high fitness cost . To make predictions about which resistance alleles are likely to be segregating it is important to study the frequency at which they spontaneously arise . To query which of the P1 transducted mutants are likely to naturally occur and if these also show pervasive epistasis we collected hundreds of spontaneous double resistance mutants . From 289 clones that were sequenced , we obtained 76 different genotypes , whose frequencies are given in Figure 4 . We performed a χ2-square test to investigate the effects of genetic background on the spectrum of mutations that spontaneously arise . We observe that there are significant differences between the types of new resistance mutations that appear in certain resistant backgrounds and the wild-type sensitive background ( Table S2 ) . We measured the fitness of each of the different spontaneous double resistant clones and compared it with the fitness in the corresponding clones constructed by P1 transduction . 67 out of 72 spontaneous clones were not different from the P1 constructed mutants ( Figure S2 ) , but surprisingly , five double resistant clones ( rpoB S531F/gyrA D87Y; rpsL K43T/rpoB H526Y; rpsL K88R/rpoB H526D; rpsL K43R/rpoB S531F; rpoB I572F/rpsL K88R ) had a significantly higher fitness ( Wilcoxon test , P<0 . 01 ) . These five spontaneous mutants have a higher fitness because they must have acquired an extra mutation during their isolation which is compensatory . To show that this is in fact the case we measured the fitness of independent clones carrying the same resistance mutations . For three of these haplotypes ( rpsL K43T/rpoB H526Y , rpsL K88R/rpoB H526D , rpsL K43R/rpoB S531F ) we measured the fitness of spontaneous clones which were obtained applying the reversed antibiotic selection procedure . ( i . e . the clones were now isolated by selecting first for rifampicin and secondly for streptomycin ) . A Wilcoxon-test revealed that the fitness values of these new independent clones were not different from the corresponding P1 clones , showing that the original spontaneous clones carry a compensatory mutation . Given the reduced number of generations in the procedure of generating spontaneous double resistant mutants , observing a compensatory mutation is only probable if such mutation has a very strong effect ( sc ) . This is because only with a large sc it will not be stochastically lost ( probability of fixation ∼2sc ) , and can fix in such short time ( time to fixation ∼1/sc ) [24] . Indeed for the five mutants mentioned above , the estimated fitness effect sc is very large , on average 0 . 09 ( with the corresponding effects of each compensatory mutation 0 . 07; 0 . 13; 0 . 11; 0 . 07; 0 . 06 ) . Adaptive mutations of such strong effect emerging and fixing so rapidly in bacterial populations under such small effective population size , is surprising given previous estimates of effects of beneficial mutations ( on average 0 . 01 ) [25] . A strong compensatory mutation must also have occurred in the spontaneous clones carrying rpsL K43T/gyrA D87G , rpsL K43N/gyrA D87G , rpsL K43T/gyrA D87Y and rpsL K43N/gyrA D87Y mutations , which were determined as synthetic sub-lethals by P1 transduction . We calculated the ε values for the 67 spontaneous clones where we do not have evidences for extra compensatory mutations and obtained the same trend as with the double mutants constructed by P1 transduction ( 58% showed significant epistasis from which 74% showed positive epistasis and 7% that had no significant cost ( Figure S3 ) ) . These results indicate that positive epistasis is also pervasive in spontaneous mutants supporting the relevance of our results . Additionally , there is evidence from epidemiological studies that in M . tuberculosis environmental resistant isolates more than 96% of the strains resistant to rifampicin have at least one mutation in rpoB , 52 to 59% of the streptomycin resistant strains have mutations in rpsL and 74 to 94% of the strains resistant to ofloxacin or levofloxacin ( quinolones like nalidixic acid used here ) have mutations in gyrA [26] . Also , the same type of mutations have been isolated in E . coli [27] and in Salmonella enterica where 42% of the isolates showed substitutions in gyrA at position S83 and 35% at position D87 [28] , that we show here to exhibit epistasis . Thus we expect the traits observed to be relevant for the evolution of multi-drug resistance acquired during treatment of infectious agents .
Given the importance of epistasis in a variety of biological features such as sex and recombination , buffering of genetic variation , speciation , and the evolution of genetic architecture [29] , recent studies have focused on measuring genome-wide levels of epistasis . However , typically only deletions or knockout mutations were studied [1] , [2] , [30] , [31] . Here , albeit focusing in a small number of genes , we measured epistasis on fitness at the scale of the allele given that these may be the most common type of mutations segregating in natural populations and because it also allows us to study mutations in essential genes . Our results not only show the significance of these interactions for the evolution of antibiotic resistance but also reveal an extra layer of complexity on epistatic patterns previously unpredicted , since it is hidden in genome-wide studies of genetic interactions using gene knockouts . The data obtained here revealed an average level of positive epistasis , which differs strongly from the results of the degree of epistasis among slightly deleterious mutations caused by random transposon insertions in E . coli , where on average no epistasis was found [30] . Positive epistasis has also been found in HIV-1 isolates [32] . Since the data in that study was obtained by sampling mutants from natural populations , comparisons between the results obtained and those found here should be taken carefully . We note nevertheless that some bias towards positive epistasis that may be present in the HIV study is not present in our study , since we constructed all possible pairwise combinations of double mutants . It remains to be explored whether the type of mutations ( single nucleotide changes , deletions or tranpositions ) can affect the pattern of genetic interaction which can be observed . We predict that it can since we show that the type of interactions is not gene but allele specific . Positive epistasis was also detected when studying interactions between rifampin and streptomycin resistant mutants in Pseudomonas aeruginosa [33] . The pattern in E . coli presented here and the results found in P . aeruginosa ( even though having a different genetic basis ) [33] indicate that the presence of positive epistasis amongst antibiotic resistance mutations is not species specific . Furthermore epistatic interactions involving fluoroquinolone resistance mutations in gyrA have also been found in Streptococcus pneumoniae [34] . Although we have studied different classes of antibiotics that affect different cell targets , our finding of pervasive epistasis can be reflecting the fact that these target genes are part of the fundamental flow from DNA to RNA to protein , and thus can be considered to be working in the same pathway . Because these affect highly conserved cell processes they should be relevant in many different organisms . An interaction between some of these genes has been described for specific traits , namely propagation of bacteriophage T7 ( interaction between rpsL and rpoB ) and mitomycin C resistance in E . coli ( between rpoB and gyrA ) [35] , [36] . Our data , both of spontaneous and P1 transducted double resistant clones , also indicates the presence of sign epistasis in the cost of multi-drug resistance involving rifampin , streptomycin and nalidixic acid , that is , a small fraction of double resistant clones showed a higher fitness than at least one of the corresponding single resistant mutants . Sign epistasis implies that the fixation of one mutation ( for example by strong selection pressure of a given antibiotic ) may alter the adaptive path in both number and type of subsequent beneficial mutations . Sign epistasis was also previously found in the context of resistance to the antibiotic cefotaxime [20] . In this system , of the 120 possible mutational paths from the low resistance to high resistance , only 18 can actually occur due to the occurrence of sign epistasis . Although there are not many examples in the literature [19] , this one clearly shows the power of sign epistasis in constraining protein adaptation . Another interesting example in the context of this work is bacterial adaptation to the cost of resistance through the acquisition of new compensatory mutations . In such an adaptive process it was observed that adaptive mutations which reduce the cost of resistance to streptomycin in E . coli [37] and Salmonella [12] are deleterious in the streptomycin-sensitive background and therefore constitute an example of sign epistasis . In this work we have determined the fitness effect of mutations in the absence of antibiotics . Future studies should focus on epistatic interactions when bacteria grow in the presence of antibiotics , a condition already shown to be relevant to the evolution of resistance [38] , [39] . Our results highlight the importance of determining the costs of single and multiple resistances and , accordingly ordering allele combinations by the degree of epistasis they exhibit . Given that at the present time , infections are likely to be caused by microbes that carry resistance to at least one drug , the strategy expected to give the best outcome is one in which the next drug is the one leading simultaneously to the resistant mutant with the biggest cost and strongest negative epistasis . Another approach to prevent the evolution of multidrug resistance would be to use drug combinations ( at certain concentrations ) that select for sensitive bacteria . This is a plausible scenario since it has been shown that when competing in the presence of the two drugs , sensitive bacteria outgrow resistant [6] . Our finding of pervasive positive epistasis suggests one possible explanation for the difficulty of eradicating multi-drug resistance in organisms like M . tuberculosis , for which current treatments involve combinations of the same drugs as studied here .
The strains used were Escherichia coli K12 MG1655 and Escherichia coli K12 MG1655 Δara . All clones with antibiotic resistant mutations were derived from the ancestral strain Escherichia coli K12 MG1655 . Escherichia coli K12 MG1655 Δara , was used as reference for the competition fitness assay . The two strains are distinguishable by phenotypic difference due to a deletion in the arabinose operon: ara+ and Δara give rise to white and red colonies , respectively , in tetrazolium arabinose ( TA ) indicator agar [12] . The clones were grown at 37°C on plates containing Luria-Bertani ( LB ) supplemented with agar and the respective antibiotics . The antibiotic concentrations were 100 µg/ml for rifampicin , 40 µg/ml for nalidixic acid and 100 µg/ml for streptomycin . To estimate fitness costs competitions were performed during 24 hours in 50 ml screw-cap tubes containing 10 ml of LB medium at 37°C , with aeration ( orbital shaker at 230 RPM ) . To estimate the frequency of each strain , in the beginning and by the end of the competition , Tetrazolium Agar ( TA ) medium containing 1% peptone , 0 . 1% yeast extract , 0 . 5% sodium chloride , 1 . 7% agar , 1% arabinose and 0 . 005% Tetrazolium chloride was used . All sets of dilutions were done in MgSO4 at a concentration of 0 . 01 M . The measure of two-locus epistasis requires the construction of double mutants from single mutants , in order to obtain clones with the same mutations alone and in combination . The antibiotics chosen to isolate the mutants were rifampicin , nalidixic acid and streptomycin . Sets of 40 single clones resistant to each antibiotic were obtained by growing independent cultures of Escherichia coli K12 MG1655 , plating in Luria-Bertani ( LB ) agar medium supplemented with each antibiotic and randomly selecting the clones after 24 hours of incubation at 37°C . Each clone was streak plated , and a single colony was grown in a screw-cap tube with 10 ml of LB medium supplemented with the respective antibiotic and stored in 15% glycerol at −80°C . Generalized transduction of the resistance mutations with bacteriophage P1 was performed as described previously [40] . These mutations were obtained by isolating spontaneous resistant clones and then using these as donor or recipient strains for the construction of the double mutants . Whereas in the great majority of clones the transduction efficiency was high , in 5 combinations of clones it was extremely low . These clones were termed synthetic sub-lethals and are indicated on Figure 2C . For two combinations of double resistance mutations , three independent clones were assayed for fitness . No significant differences were observed ( Kruskal-Wallis test ) between these clones . Three single spontaneous clones , resistant to each antibiotic , were used to put back the resistance on the wild-type sensitive background through P1 transduction . We then measured in 5 fold replicate competitions the fitnesses of each spontaneous mutant and the corresponding single P1 transducted clone . Pairwise comparisons , by Wilcoxon test , revealed no significant differences between the single resistance clones constructed in different ways . We exposed the single resistant clones to a second antibiotic to select for spontaneous mutants resistant to two antibiotics . These were obtained by culturing each clone carrying resistance independently and plating in Petri-dishes with the two antibiotics . Clones with the double resistance were picked randomly . All spontaneous resistant clones were tested for a mutator phenotype and those few with evidence of high mutation rate were disregarded . The main target genes for resistance to rifampicin , nalidixic acid and streptomycin are rpoB , gyrA and rpsL , respectively . To know the mutations that confer the obtained resistances , each target was amplified and then sequenced . The primers used to amplify the portion of the rpoB gene encoding the main set of mutations conferring resistance to rifampicin were: 5′-CGTCGTATCCGTTCCGTTGG-3′ and 5′-TTCACCCGGATAACATCTCGTC-3′; for gyrA gene , which encodes for the main set of mutations conferring resistance to nalidixic acid , 5′-TACACCGGTCCACATTGAGG-3′ and 5′-TTAATGATTGCCGCCGTCGG-3′; for rpsL gene , that encodes for the mutations conferring streptomycin resistance , 5′-ATGATGGCGGGATCGTTG-3′ and 5′-CTTCCAGTTCAGATTTACC-3′ . The same primers were used for sequencing straight from the PCR product . To measure fitness cost of the resistance mutations a competition assay was done . The resistant mutants were competed against a reference strain , Escherichia coli K12 MG1655 Δara in an antibiotic free environment , in an approximate proportion of 1∶1 . To do so , we grew both resistant and reference strains in LB liquid medium for 24 hours at 37°C with aeration . Accurate values of each strain initial ratio were estimated by plating a dilution of the mixture in TA Agar plates . Competitions were performed in 50 ml screw-cap tubes containing 10 ml of LB liquid medium by a period of 24 hours at 37°C with aeration . By the end of this competition process , appropriate dilutions were platted onto TA agar plates to obtain the final ratios of resistants and reference strains . The fitness cost of each mutant strain- i . e the selection coefficient - was estimated as the per generation difference in Malthusian parameters for the resistant strain and the marker strain [12] , discounted by the cost of the Δara marker . The fitness cost was estimated as an average of four and five independent competition assays for P1 and spontaneous resistant clones respectively . No correlation was observed between the cost of the resistance mutations and the frequency at which they arose ( Figure S1 ) . Pairwise epistasis , ε , can be measured assuming a multiplicative model in which case: ε = WABWab−WAbWaB , where Wij is the fitness of the clone carrying alleles i and j and capital letters represent the wild-type sensitive alleles . Error ( σε ) of the value of ε is then estimated by the method of error propagation:Whenever the value of ε was within the error we considered that alleles a and b did not show any significant epistasis ( we indicate such combinations as white boxes labeled no epistasis in Figure 2C ) . From the distribution of values of ε , provided in Figure 2B , we calculated the median value of ε and its 95% CI by bootstrap where we took 1000 samples . We tested normality of the distribution by a Shapiro-Wilk normality test ( P = 0 . 024 ) , and a Wilcoxon test for the location in zero resulted in a P value of P = 0 . 0006 . To query about the presence of sign epistasis in the data we made pairwise comparisons between the fitness of each double resistance clone and its corresponding single resistance clones using a Wilcoxon test to assess if the fitness of the double resistant was higher than the fitness of any of the single resistance clones . The P values are indicated in Figure 3 for those combinations that provided significant results , at 5% confidence level , after Bonferroni correction ( n = 2 comparisons ) . For some of the double resistant clones created by P1 transduction that indicated the presence of sign epistasis- see Figure 3- we also obtained spontaneous double resistant mutants , that equally indicated evidence for sign epistasis . Epistasis is sometimes calculated assuming an additive model [41]: ε = cab− ( ca+cb ) , such that it measures the deviation of the cost of carrying double resistance from the sum of the costs of each resistance . Since the values of the majority of the cost are small , applying the multiplicative or the additive model leads to the same conclusions , i . e . those combinations of alleles that lead to positive ( negative ) epistasis under the multiplicative model , also lead to positive ( negative ) epistasis under the additive model . A Kolmogorov-Smirnov comparing the distributions of ε under multiplicative model with ε under the additive model results on P = 0 . 9 . The median value in the distribution of epistasis calculated under the additive model is 0 . 025 with bootstrap 95% CI [0 . 015; 0 . 036] and a Wilcoxon test for location strongly supports the presence of positive epistasis: P = 0 . 00002 . This shows that the same pattern occurs applying either the multiplicative or the additive models . To perform the statistical analysis we used the free software R: http://www . r-project . org .
|
Understanding the nature of genetic interactions , known as epistasis , is crucial in biology . The strength and type of epistasis is relevant for the evolution of sex , buffering of genetic variation , speciation , and the topography of fitness landscapes . While epistasis between gene deletions has been the recent focus of research , interactions between randomly selected alleles , which are of the greatest evolutionary interest , have not . We have studied the strength and type of epistasis amongst alleles that confer antibiotic resistance and have found that: in an antibiotic-free environment , the cost of multiple resistance is smaller than expected—a signature of pervasive positive epistasis amongst alleles that confer resistance to antibiotics; epistatic interactions are allele specific; a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds; some double resistances entail no measurable cost; and some allelic combinations are hotspots for rapid compensation . Overall , our findings provide added reasoning as to why multi-resistance is so difficult to eradicate . Importantly , our results of allelic-specific epistasis reveal an extra layer of complexity on epistatic patterns previously unrecognized .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"public",
"health",
"and",
"epidemiology",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2009
|
Positive Epistasis Drives the Acquisition of Multidrug Resistance
|
Pseudomonas aeruginosa ( PA ) is an opportunistic pathogen that causes the relapse of illness in immunocompromised patients , leading to prolonged hospitalization , increased medical expense , and death . In this report , we show that PA invades natural killer ( NK ) cells and induces phagocytosis-induced cell death ( PICD ) of lymphocytes . In vivo tumor metastasis was augmented by PA infection , with a significant reduction in NK cell number . Adoptive transfer of NK cells mitigated PA-induced metastasis . Internalization of PA into NK cells was observed by transmission electron microscopy . In addition , PA invaded NK cells via phosphoinositide 3-kinase ( PI3K ) activation , and the phagocytic event led to caspase 9-dependent apoptosis of NK cells . PA-mediated NK cell apoptosis was dependent on activation of mitogen-activated protein ( MAP ) kinase and the generation of reactive oxygen species ( ROS ) . These data suggest that the phagocytosis of PA by NK cells is a critical event that affects the relapse of diseases in immunocompromised patients , such as those with cancer , and provides important insights into the interactions between PA and NK cells .
Infectious complications are one of the major causes of morbidity and mortality in immunocompromised patients , despite recent advances in therapeutic approaches and supportive care . Among the infectious agents , the increasing incidence of PA is a worldwide problem , particularly in patients with leukemia and in hematopoietic stem cell transplantation recipients [1] , [2] , [3] . PA is a multi-drug resistant , Gram-negative , opportunistic pathogen and is associated with significant morbidity and mortality [4] , [5] . PA constitutes the major cause of prolonged hospitalization , severe illness , death , and increased cost for immunocompromised patients . A high mortality rate occurs in patients with underlying disease , such as cystic fibrosis and cancer [6] , [7] . PA pathogenesis involves the production of a variety of toxic products , including alkaline protease ( AP ) , elastase [8] , and several Type III system-dependent exotoxins that include Exo A , Exo T , and Exo U [9] , [10] . AP and elastase have previously been implicated in the inhibition of NK cell activity [8] , and the exotoxins have been reported to induce apoptosis of phagocytes , such as dendritic cells [11] , macrophages [12] , and neutrophils [13] . Apoptosis and shedding of the infected , apoptotic cells may be beneficial to the survival of the host organism [14] . However , apoptosis of lymphocytes by bacterial infection has detrimental effects on host survival [15] . NK cells are lymphocytes that mature from hematopoietic stem cells ( HSC ) in the bone marrow ( BM ) [16] . Upon activation , they can eliminate leukemic cells , as well as pathogen-infected or transformed cells , either directly or indirectly through the release of cytokines and chemokines [17] , [18] . Previous studies indicate that upon infection with PA , NK cells can produce interferon-γ that may assist in clearing the bacteria [19] . However , a negative role of NK cells in the regulation of PA infection has also been reported [20] . Furthermore , NKG2D and substance P have been shown to be important in host defense against PA infection [19] , [21] , supporting the involvement of NK cells in resistance to such infections . However , little is known about the exact mechanisms or interactions between NK cells and PA during infection . In this report , we show for the first time that PA invades and eliminates NK cells in vivo and in vitro by induction of apoptosis via ROS generation . The reduction in NK cell number by PA invasion led to the aggravation of metastasis in a tumor-bearing animal model . Thus , the capability of PA to induce apoptosis of NK cells may be an important factor in the relapse of illness , as well as in the initiation of infection , bacterial survival , and escape from the host immune response .
PA infection has been implicated in prolonged hospitalization and shortened lives of cancer patients [7] , [22] . Thus , we analyzed the effects of PA infection on tumor metastasis in an animal model using a PAK strain . When B16-F10 melanoma cells were i . v . - injected into C57BL/6 mice after PAK infection , the number of metastasized tumor colonies in the lungs was significantly increased compared with the PBS-injected control ( Fig . 1A , B ) , suggesting that metastatic tumor growth may be aggravated by PAK infection in cancer patients . We next examined the populations of various immune cells in the spleen after PAK infection . A significant reduction in the number of NK cells in the spleen , in addition to a decrease in the number of macrophages , was observed following PAK infection . However , the number of total splenocytes and other immune cells , such as T or B cells , was not significantly altered ( Table 1 ) , suggesting that NK cells might be specifically involved in PAK-induced metastasis . In fact , the reduction in the number of NK cells after PAK infection not only occurred in the spleen , but also in other tissues , such as the liver and lung ( Fig . 1C ) . Next , we investigated whether the reduction in NK cells was specifically due to PAK infection by analyzing NK populations after i . v . injection of various doses of PAK . The results showed that the reduction in the number of splenic NK cells was PAK-dose dependent , with a loss of up to approximately 80% of the NK cells in 24 h at the dose of 2×107 PAK , in terms of population and absolute number ( Fig . 1D ) . These results further suggest that PAK infection significantly and specifically reduces the NK cell population , although the number of macrophages were also altered , as previously reported [23] . Because NK cells play a critical role in tumor clearance and defective NK cell activity results in tumor metastasis [17] , we examined the role of NK cells in PAK-induced metastasis in a mouse model by performing adoptive transfer of NK cells after PAK infection . As expected , adoptive transfer rescued the diminished tumor clearance activity and restored it to control levels ( Fig . 1E , F ) . Taken together , these findings suggest that PAK infection eliminates NK cells and subsequently results in increased tumor metastasis . Next , to investigate whether a reduction of NK cells by PA infection is directly involved in NK cell function , we examined the activity of NK cells after PA infection in vivo and in vitro . Depletion of NK cells has been known to reduce resistance to herpes simplex virus 1 ( HSV-1 ) infection [24] . Thus , we infected C57BL/6 mice with or without PAK , subsequently injected HSV-1 i . p . or i . v . 2 days later , and measured the survival rate of the mice . In both cases , Kaplan-Meier survival plots showed that the survival rates of the mice were prominently decreased when the mice were infected with PAK prior to HSV-1 infection , compared with mice infected with PAK or HSV-1 alone ( Fig . 2A ) . The decline in resistance against HSV-1 observed in mice infected with PAK may be due to the reduction in NK cell number caused by PAK infection . Next , to investigate the effects of PAK infection on NK cytolytic activity , we performed in vitro 51Cr-release assays using PAK-infected cells . At 12 h post-infection , the population of NK cells in the spleen decreased to approximately 40% of the control ( Fig . 2B ) . Accordingly , the cytotoxic assay revealed an approximately 40–50% reduction in the activity of PAK-infected splenocytes compared with control or heat-killed PAK-treated splenocytes ( Fig . 2C ) . Moreover , NK92 cell lines also showed significantly reduced cytotoxicity when the cells were infected with PAK in vitro ( Fig . 2D ) . Taken together , these results demonstrate that PAK infection reduces the number and functions of NK cells in vivo and in vitro . To determine whether the reduction in the number of NK cells after PAK infection was due to decreased infiltration or to direct elimination , we infected NK92 cells with PAK in vitro and analyzed the survival of the NK cells via microscopy and flow cytometry . As seen in Fig . 3A , infection with PAK significantly increased the number of propidium iodide ( PI ) -positive NK cells , whereas heat-inactivated PAK did not affect the survival of NK cells . The lactate dehydrogenase ( LDH ) release assay also showed that PAK directly kills NK cells in a concentration-dependent manner compared with control or inactivated PAK ( Fig . 3B ) . In addition , flow cytometric analysis revealed that PAK infection induced both early ( PI−/Annexin-V+ ) and late ( PI+/Annexin-V+ ) apoptosis of NK cells in a concentration-dependent manner ( Fig . 3C ) . Taken together , these results suggest that PAK infection directly kills NK cells via apoptosis . Two caspase activation pathways are centrally involved in executing apoptotic events: the receptor-initiated , caspase-8-dependent pathway and the mitochondria-initiated , caspase-9-dependent pathway [25] . Therefore , we investigated changes in mitochondrial membrane potential after PAK infection of NK92 cells by staining them with JC1 ( Molecular Probes , Netherlands ) . PAK infection affected mitochondrial integrity in a concentration-dependent manner , whereas heat-inactivated bacteria did not affect the mitochondrial membrane potential ( Fig . 3D ) . Accordingly , caspase-9 and caspase-3 were activated in a concentration-dependent manner by bacterial infection , whereas caspase-8 was not significantly affected ( Fig . 3E ) . These results suggest that PAK-induced NK cell apoptosis does not occur through a receptor-mediated pathway , but rather follows mitochondria-initiated , caspase-9-dependent mechanisms . PAK uses TTS systems to secrete several exotoxins , including Exo A , Exo T , Exo S , and Exo U , all of which are known to induce cellular apoptosis [10] , [26] , [27] , [28] . Thus , we tested whether PAK-induced NK cell apoptosis was dependent on the TTS system by infecting NK cells with corresponding mutant strains of PAK . When ExoS− or ExoT− bacterial strain were i . v . -injected into C57BL/6 mice , the percentage ( Fig . 4A ) and absolute number ( Fig . 4B ) of NK cells ( NK1 . 1+/CD3− ) were prominently reduced in vivo , comparable to the effects of wild type ( WT ) strain infection . Moreover , the ExsA− strain , in which the TTS system is disrupted [29] , did not significantly decrease the NK cell number compared to the WT strain . This suggests that PAK-induced NK cell apoptosis was independent of the TTS system . Accordingly , induction of NK cell apoptosis by each exotoxin-deleted mutant strain was similar to that of the WT strain in vitro ( Fig . 4C ) , and the mutant strains did not induce apoptosis of HeLa cells , as previously reported ( Fig . S1 ) [26] . Furthermore , the mutant strains were still able to augment tumor metastasis in an in vivo animal model , similar to the WT strain ( Fig . 4D ) . These results suggest that PAK-triggered NK cell apoptosis is TTS system-independent , unlike PAK-induced apoptosis of non-immune cells , such as epithelial and endothelial cells . Secreted proteins from PAK , such as AP and elastase , may contribute to bacterial cytotoxicity in macrophage or NK cells [8] , [23] . Therefore , we investigated the involvement of secreted toxins in NK cell apoptosis by measuring whether the bacterial culture medium was cytotoxic to NK cells . Flow cytometric analysis showed that culture medium containing secreted bacterial toxins did not affect cell viability ( Fig . 4E ) , suggesting that PAK-induced NK cell apoptosis is independent of the secretion of bacterial toxins . Pyocyanin , a toxic metabolite of a certain strain of PA , has been shown to induce apoptosis in neutrophils [13] , [30] . Thus , we examined whether pyocyanin is involved in NK cell apoptosis . As expected , flow cytometric analysis revealed that PA14 , a pyocyanin-producing strain of PA , induced NK cell apoptosis , although the apoptosis was not as high as that produced by PAK ( Fig . S2A ) . A pyocyanin-deficient PA strain , PA14-Phz1/2 [31] did not show any cytotoxic effect against NK cells . In addition , when NK cells were treated with pyocyanin in vitro , pyocyanin induced cytotoxicity against NK cells in a dose-dependent manner ( Fig . S2B ) . These results suggest that pyocyanin may contribute to the apoptosis of PA-induced NK cell apoptosis depending on PA strains . However , the King Agar A assay [32] revealed that PAK , the strain used in this study , produced little amounts of pyocyanin ( Fig . S2C ) , as previously reported [33] . Taken together , these results suggest that although pyocyanin seems to be able to induce NK cell apoptosis , it is not involved in the PAK-induced NK cell apoptosis observed in this study because the PAK strain may not produce sufficient pyocyanin to be toxic . Autophagy is a non-apoptotic programmed cell death mechanism that involves a lysosomal pathway and has been reported to play a role in host defense against bacterial infection [34] , [35] . Although the above results showed that PAK-induced NK cell death occurs via caspase-9-dependent apoptosis , we investigated the possible involvement of autophagy in PAK-induced NK cell death by examining the endogenous expression of LC3 , an autophagic marker . Confocal microscopic analysis revealed that there was no change in LC3 expression in PAK-infected NK cells , while rapamycin-treated NK cells exhibited the punctuated structure of LC3 , colocalized with lysosomal marker Lamp-1 , which is indicative of autophagosome formation ( Fig . S3 ) . These results demonstrate that internalization of PAK did not induce autophagy in NK cells and that PAK-induced NK cell death is a distinct process from autophagy . Because the above results showed that PAK-induced NK cell apoptosis is not dependent on bacterial toxins , we hypothesized that NK cell apoptosis following PAK infection might be due to the active invasion of intact bacteria into NK cells . Thus , we constructed GFP-expressing PAK ( PAK-GFP ) and used confocal microscopy to investigate its localization ( Fig . 5A ) . When NK cells were co-cultured with PAK-GFP ( bottom ) , PAK-GFP bacteria were internalized by the NK cells ( Green ) . Moreover , typical characteristics of apoptosis , including disruption of membrane integrity ( Red ) and nuclear condensation ( Blue ) , were observed following infection . Control NK cells retained normal membrane integrity and large nuclei ( top ) . These results suggest that PAK can cause phagocytosis-induced apoptosis of NK cells . Furthermore , transmission electron microscopy ( TEM ) revealed bacterial internalization and disruption of the entry site at the NK cell surface ( Fig . 5B ) . In addition , the bacteria inside the cell were surrounded by vacuole-like structures , suggesting that PAK enters NK cells by phagocytosis . Taken together , these results show that PAK can invade NK cells and stimulate phagocytosis-induced apoptosis of non-phagocytic NK cells . Because PI3K has been implicated in PAK internalization of non-phagocytic cells [36] , we investigated the involvement of PI3K in PAK entry into NK cells . Western blot analysis demonstrated that bacterial infection activated Akt in NK cells in a time-dependent manner ( Fig . 6A ) , suggesting the participation of PI3K in this process . Additionally , treatment with PI3K inhibitors significantly decreased bacterial entry into NK cells ( Fig . 6B ) and blocked internalization of the bacteria in a concentration-dependent manner ( Fig . 6C ) . Treatment with a PI3K inhibitor also prevented PAK-induced apoptosis of NK cells ( Fig . 6D ) , supporting the involvement of PI3K in PAK entry into NK cells . MAP kinases are critical factors for the survival or apoptosis of cells . In general , p38 and JNK are involved in cell death mechanisms , whereas Erk1/2 is critical for cell survival [37] . We therefore investigated the involvement of MAP kinases in bacterial invasion and apoptosis of NK cells . Western blot analysis showed that JNK and p38 were activated in the later stage after infection , whereas Erk1/2 was gradually deactivated ( Fig . 6E ) . This suggests that PAK infection induced death signals while attenuating survival signals in NK cells . Interestingly , the activation of JNK and p38 occurred when Erk1/2 disappeared: p38 was gradually activated during infection , whereas JNK suddenly appeared at 6 h post-infection and PI3K was activated earlier ( Fig . 6A , E ) . These results suggest that MAP kinases may affect the survival of NK cells , whereas PI3K might be involved in bacterial invasion . Additionally , treatment with inhibitors of p38 and JNK decreased the bacterially-induced apoptosis of NK cells , whereas the Erk1/2 inhibitor did not affect the survival of NK cells infected with PAK ( Fig . 6F ) . ROS has been identified as a critical factor in phagocytosis-induced apoptosis [38] . MAP kinases such as JNK are involved in ROS generation , which ultimately leads to apoptosis [12] . Therefore we measured ROS generation during PAK-induced NK cell apoptosis . A significant increase in ROS levels was observed after PAK infection of NK cells , whereas heat-inactivated PAK did not affect ROS generation ( Fig . 7A ) . In addition , pretreatment of NK cells with inhibitors of various MAP kinases did not block the ROS generation induced by PAK invasion ( Fig . 7B ) . However , treatment of H2O2 gradually induced MAP kinase activation in NK cells ( Fig . 7C ) . These results suggest that ROS is an upstream signal of MAPK activation in PAK-induced NK cell apoptosis . Moreover , DPI , a NADPH oxidase inhibitor , blocked NK cell apoptosis in a concentration-dependent manner ( Fig . 7D ) , suggesting that ROS generation is critical for NK cell apoptosis induced by phagocytosis of PAK .
Bacterial infection is a critical complication that may lead to prolonged hospitalization of cancer patients [6] , [7] , [22] . Bacteremia , the presence of viable bacteria circulating in the bloodstream , results in a reduction in the number of lymphocyte subsets [39] , [40] , [41] , which in turn may result in tumor propagation in these patients [42] . A high percentage of patients with PAK bacteremia were observed to have cancer as an underlying disease ( ∼50% ) [7] . Several immune cell types , including dendritic cells , macrophages , neutrophils , and certain T cells , are involved in clearing PAK infection [20] , [43] , [44] , [45] . However , PAK has its own strategies for eluding the immune system of the host , including the elimination of immune cells [15] . For example , PAK kills principal effector immune cells , such as macrophages and neutrophils , by inducing phagocytosis-induced apoptosis [28] , [46] . Although NK cells are the primary cell type responsible for innate immunity , the role of NK cells in the clearance of PAK is controversial . For example , it has been reported that NK cells may negatively regulate PAK clearance via the production of cytokines , which in turn can affect the activities of neutrophils directly involved in splenic bacterial clearance [20] . On the other hand , NKG2D-expressing cells , including NK , T , and NKT cells , have also been implicated in the clearance of PAK [21] , [47] . In addition , a recent study showed that IFN-γ production by NK cells is involved in the elimination of PAK [19] . In this regard , we speculate that interactions must occur between PAK and NK cells , the results of which can be detrimental or beneficial to the host . In the present study , we found that PAK directly invades NK cells and kills them by inducing apoptosis . The fact that adoptive transfer of NK cells attenuated metastasis after PAK infection implies that the reduction of NK cells by PAK may be a major reason for relapse in cancer patients . PAK-induced apoptosis of host cells such as macrophages , neutrophils , and epithelial cells has been studied extensively [12] , [13] , [14] . In general , the death of these cells occurs through the TTS system and involves ExoS , ExoT , and Exo U [26] , [27] , [28] . The TTS system delivers cytotoxins directly from the bacterium to the host through a contact-dependent mechanism that eventually kills the host cell . In our study , various PAK mutants that lacked TTS toxins exhibited the capacity to kill NK cells . This suggests that PAK-induced apoptosis may not utilize the TTS system . Instead , PAK may elicit phagocytosis-induced cell death ( PICD ) of NK cells via caspase and MAP kinase activation , as well as via ROS generation , as observed for professional phagocytes [38] , [48] , [49] . A number of bacteria , including Salmonella , Shigella , Mycobacterium , and PAK , induce PICD of leukocytes [50] , [51] , and PICD of phagocytic leukocytes has emerged as an important mechanism in the pathogenesis of bacterial infections [28] , [38] , [52] . Notably , the outcome of PICD may be detrimental or beneficial to the host's immune response . For example , pathogen-induced apoptosis of macrophages may adversely affect the immune response , whereas that of neutrophils may be critical for clearing infections [38] , [46] . This topic requires further investigation . NK cells are the lymphocytes that play an important role in immune surveillance against tumors [53] . Intravenous injection of B16-F10 melanoma cells is a common model for hematogenous metastases , and NK cells have been shown to reduce tumor lung metastasis in the murine cancer models . Therefore , NK cells are regarded as one of the main effector cells that are responsible for the early response against tumors [54] . The fact that the observed reduction of NK cells following PAK infection exacerbated the metastasis of cancer cells strongly suggests that decreased NK cell number may be a major reason for relapse in cancer patients . Interestingly , bacteria were rarely present in the spleen or the lung at 48 h post infection ( Fig . S4 ) , suggesting that they may be cleared by the systemic immune system . Thus , augmentation of tumor metastasis in PAK-infected mice implies that the initial induction of NK cell apoptosis by PAK may be sufficient to cause impairment of host defense against bacterial infection in cancer patients . Blood NK cells have been reported to play critical roles in preventing tumor cell localization to the lung [54] . In summary , we have shown that PAK infection induced metastasis of cancer cells in a tumor-bearing animal model and that adoptive transfer of NK cells restored the tumor clearance ability of the host . We further showed that PAK actively invaded NK cells via PI3K activation and that phagocytosis of PAK by NK cells induced mitochondria-mediated apoptosis via activation of caspase-9 , JNK , p38 , and ROS generation , ultimately leading to the elimination of NK cells .
6- to 8-week-old C57BL/6 mice were purchased from The Jackson Laboratories ( Bar Harbor , ME ) and maintained under specific pathogen-free conditions with standard temperature and lighting . Mice were given food and water ad libitum . All studies were approved by the institutional review board ( KRIBB Institutional animal care and use committee/KRIBB-IACUC , approval number: KRIBB-AEC-9027 ) , and all procedures were performed in accordance with institutional guidelines for animal care . All cells were purchased from the American Type Culture Collection ( ATCC ) . NK92 ( CRL-2407TM ) cells were cultured in α-Minimum Essential Medium ( MEM ) ( GIBCO , 12561 ) supplemented with 20% fetal bovine serum ( FBS ) ( Hyclone , SH30396 . 03 ) and 10 ng/ml of IL-2 ( PEPROTECH Asia , 200–02 ) . HeLa S3 ( CCL-2 . 2TM ) and B16-F10 ( CRL-6475TM ) cells were cultured in Dulbecco's Modified Eagle's Medium ( DMEM ) ( GIBCO , 11995 ) containing 10% FBS . PAK , PA14 , and PA-phz1/2 were cultured in BactoTM Tryptic Soy Broth ( TSB ) ( BD Biosciences , 6292241 ) , and TTS toxin-deficient mutants of PAK , Exo A− and Exo S− , were cultured in TSB containing 200 µg/ml of streptomycin and spectromycin . Exo T− cells were cultured in TSB containing 200 µg/ml of gentamicin . After incubation for 16–18 h at 37°C , bacteria were harvested by centrifugation at 6000 rpm ( 2613×g ) for 15 min . The bacteria were washed 3 times with phosphate buffered saline ( PBS ) and used to infect cells in vivo via i . v . injection or in vitro . Bacterial number were calculated by measuring the absorbance at 600 nm and absorbance of 0 . 5 AU represented 3×108 cfu/ml . A plasmid with p519ngfp ( ATCC 87453 ) was introduced into PAK by electroporation using the Gene Pulser II ( Bio-Rad , Hercules , CA , USA ) set at 2 . 5 kV , 25 µF , and 200 Ω in 0 . 2-cm cuvettes , according to the manufacturer's instructions . The resulting clones were streaked onto TSB plates with kanamycin ( 500 µg/ml ) , and colonies were selected using direct fluorescence . Colonies were further amplified in kanamycin-supplemented medium . Invasion ability and cytotoxicity of PAK-GFP were verified by flow cytometry ( Fig . S5 ) . Splenocytes were isolated from each mouse 24 h after tail vein injection of PBS or PAK and the population of lymphocytes was measured by flow cytometry . The antibodies used in these experiments including NK1 . 1-PE ( 553165 ) , CD3e-FITC ( 553062 ) , CD11b-PE ( 557397 ) , Ly-6G/Ly6C-FITC ( 553126 ) , CD45R/B220-PE ( 553090 ) , and CD19-FITC ( 553785 ) , were purchased from BD Biosciences . NK92 cells were infected with PAK at various MOI ( multiplicity of infection ) and supernatants were harvested after 16–18 h to detect LDH release . NK92 cell death was determined by measuring lactate dehydrogenase activity with the CytoTox 96 assay kit ( Promega , Madison , WI ) . Pyocyanin ( 10009594 ) was purchased from Cayman Chemical ( Michigan , USA ) . For the flow cytometric assay , the Annexin V-FITC Apoptosis Detection Kit I ( 556547 ) and Annexin V-PE ( 556422 ) were purchased from BD Biosciences . For western blot analyses of caspases , antibodies against caspase-8 ( Calbiochem ) , caspase-9 ( Cell Signaling , Danvers , MA ) , and cleaved caspase-3 ( Asp175 ) ( Cell Signaling ) were employed . FAS ( B-10 ) ( Santa Cruz Biotechnology ) , ( S ) - ( + ) -camptothecin ( C9911 ) and etoposide ( E1383 ) from Sigma–Aldrich ( St . Louis , MO ) were used as positive controls for the induction of apoptosis . To determine mitochondrial membrane potential , infected cells were washed twice with 1×PBS and incubated with 1 µg/ml of the JC-1 stain ( Molecular Probes-Invitrogen , T3168 ) for 30 min at 37°C . Cells were washed in PBS and subjected to flow cytometric analysis . The red fluorescence of JC-1 aggregates represents the intact mitochondrial membrane potential , whereas the green fluorescence of JC-1 monomers is indicative of mitochondrial depolarization . ROS production was analyzed by labeling cells with 20 µM 2′ , 7′-dichlorodihydrofluorescein diacetate ( DCFDA ) ( Invitrogen , C400 ) for 30 min at 37°C in the dark . Samples were examined by fluorescence-activated cell sorter ( FACS ) analysis , and the results were analyzed using CellQuest software ( Becton Dickinson , San Jose , CA ) . Confocal fluorescence images were obtained using a Zeiss LSM510NLO confocal scan head mounted on a Zeiss Axiovert 200 M inverted microscope with a 63× objective . Sequential excitation at 488 nm and 543 nm were produced by argon and helium-neon gas lasers , respectively . The emission filters BP500–550 and LP560 were used to collect green and red in channels one and two , respectively . After sequential excitation , green and red fluorescence images of the same cell were acquired using Laser Sharp software . Images were analyzed using Zeiss software . For the autophagy assay , cells were fixed and washed with the BD Cytofix/CytopermTM Fixation/Permeabilization Kit ( BD Biosciences , 554714 ) and blocked in 2% BSA/PBS for 30 min . After incubation with anti-LC3 antibody ( MBL , PM036 ) and anti-LAMP1 antibody ( SANTA CRUZ , sc-18821 ) for 3 h at 4°C , cells were washed three times and incubated in fluorescein-anti-rabbit IgG ( VECTOR , FI-1000 ) , and PE-anti-Mouse IgG1 ( BD Bioscience , 550083 ) antibodies for 1 hr at 4°C to visualize LC3 and LAMP1 , respectively . Cells were washed three times counterstained with DAPI ( VECTOR , H-1500 ) , and imaged with a confocal fluorescence microscope . TEM was carried out to further visualize the intracellular internalization of PAK . NK92 cells were infected with PAK at a MOI of 500 for 8 h , and the cells were washed twice with cold PBS , and fixed overnight in 2 . 5% glutaraldehyde . On the following day , cells were rinsed three times with PBS and post-fixed in 1% osmium tetroxide ( OSO4 ) for 1 h . The cells were dehydrated using increasing concentrations of ethanol ( 30% , 50% , 75% , 100% , 100% , 10 minutes each ) and embedded in Polybed 812 epoxy resin ( Polysciences ) . Sections ( 60 nm thick ) were cut and stained with 4% aqueous uranyl acetate for 15 min , followed by lead citrate for 7 min . Samples were viewed by electron microscopy . For analysis of mitogen-activated protein ( MAP ) kinase signaling pathways , phospho-specific antibodies against ERK1/2 , JNK/SAPK and p38 were used to detect expression in whole cell lysates by western blotting . All antibodies were purchased from Cell Signaling , inhibitors of MAP kinases were purchased from Calbiochem . NK92 cells were infected at a MOI of 100 at 37°C for 3 h . Cells were washed 3 times with PBS and incubated with experimental medium containing gentamicin ( 200 µg/ml ) for 1 h at 37°C to kill extracellular bacteria . The cells were washed again with PBS and lysed in 0 . 25% Triton X-100 for 10 min at room temperature . The released intracellular bacteria were enumerated by plating on Tryptic Soy Agar plates . PBS ( 50 µl ) or various doses of PAK were injected into C57BL/6 mice via i . v . injection . At 2 days post-infection ( DPI ) , B16-F10 cells in the exponential growth phase were harvested and injected i . v . into the mice . For the adoptive transfer experiments , 1×106 NK cells in 50 µl PBS were injected into the mice at 3 DPI . At 12 DPI , the mice were sacrificed and the number of tumor cells in the lungs were enumerated . Splenocytes and NK92 cells were activated with IL-2 ( 20 ng/ml ) for 12 hrs and infected with live or heat-inactivated ( 80°C , 30 min ) PAK . After 12 h , cells were treated with gentamicin ( 200 µg/ml ) for 1 h to remove extracellular bacteria . Following this treatment , the cytotoxicities of splenocytes and NK92 cells were assessed using 51Cr labeled YAC-1 and K562 cells as target cells , respectively , at various effector/target ratios . After incubation , the plates were centrifuged , and 100 µl of the resulting supernatant was removed . Radioactivity was measured using a γ scintillation counter . Cell activity was calculated as the percent cytotoxicity , according to the following equation: Spontaneous release of 51Cr was determined by incubation of target cells in the absence of effector cells: maximum release was determined by treatment with 1% Triton X-100 . C57BL/6 mice were i . v . -inoculated with 3×107 PAK . Two days after infection , 1×107 PFU herpes simplex virus type 1 ( HSV-1 ) or PBS were injected i . v . or i . p . Each group was composed of 6 mice: the survival of the mice was scored 14 days after HSV-1 injection . Spleen and lung were aseptically removed post-infection and homogenized in 3 ml of cold PBS . Homogenates were plated on tryptic soy agar plates and incubated at 37°C for 24 h . Tissue homogenates were obtained from a minimum of 3 mice . All experiments were repeated at least three times . Results are presented as means±standard deviation . A student's t test was used to compare means and p<0 . 05 was considered as significant .
|
Phagocytic leukocytes , including neutrophils and macrophages , are critical for innate immunity against invading bacteria . Binding and internalization of bacteria by these immune cells stimulates a variety of anti-microbial activities . Although the immune cells are specialized for elimination of bacteria , cellular apoptosis by bacterial phagocytosis has emerged as an important mechanism of pathogenesis . NK cells are non-phagocytic lymphocytes that are responsible for innate immunity via elimination of virus or bacteria-infected cells , as well as transformed cells . We found that PA invades NK cells and that this phagocytic event results in the generation of ROS within the NK cells , leading to apoptosis . The elimination of NK cells , at least in part , may be responsible for the relapse in PA-infected cancer patients . Based on these findings , studies on the interactions between bacterial determinants and host receptors should provide further insight into the mechanisms of bacterial pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] |
2009
|
Pseudomonas aeruginosa Eliminates Natural Killer Cells via Phagocytosis-Induced Apoptosis
|
The parasitic flatworm Schistosoma mansoni is a blood fluke that causes schistosomiasis . Current schistosomiasis control strategies are mainly based on chemotherapy , but many researchers believe that the best long-term strategy to control disease is a combination of drug treatment and immunization with an anti-schistosome vaccine . Numerous antigens that are expressed at the interface between the parasite and the mammalian host have been assessed . Among the most promising molecules are the proteins present in the tegument and digestive tract of the parasite . In this study , we evaluated the potential of Sm10 . 3 , a member of the micro-exon gene 4 ( MEG-4 ) family , for use as part of a recombinant vaccine . We confirmed by real-time PCR that Sm10 . 3 was expressed at all stages of the parasite life cycle . The localization of Sm10 . 3 on the surface and lumen of the esophageal and intestinal tract in adult worms and lung-stage schistosomula was confirmed by confocal microscopy . We also show preliminary evidence that rSm10 . 3 induces erythrocyte agglutination in vitro . Immunization of mice with rSm10 . 3 induced a mixed Th1/Th2-type response , as IFN-γ , TNF-α , and low levels of IL-5 were detected in the supernatant of cultured splenocytes . The protective effect conferred by vaccination with rSm10 . 3 was demonstrated by 25 . 5–32% reduction in the worm burden , 32 . 9–43 . 6% reduction in the number of eggs per gram of hepatic tissue , a 23 . 8% reduction in the number of granulomas , an 11 . 8% reduction in the area of the granulomas and a 39 . 8% reduction in granuloma fibrosis . Our data suggest that Sm10 . 3 is a potential candidate for use in developing a multi-antigen vaccine to control schistosomiasis and provide the first evidence for a possible role for Sm10 . 3 in the blood feeding process .
Schistosomiasis occurs primarily in developing countries and is the most important human helminth infection in terms of global mortality . This parasitic disease affects more than 200 million people worldwide , causing more than 250 , 000 deaths per year [1] . Furthermore , schistosomiasis is responsible for the loss of up to 4 . 5 million DALYs ( disability adjusted life years ) annually [2] . Current schistosomiasis control strategies are mainly based on chemotherapy but , despite decades of mass treatment , the number of infected people has not decreased considerably in endemic areas [3]–[5] . The extent of endemic areas , constant reinfection of individuals and poor sanitary conditions in developing countries make drug treatment alone inefficient [6] . It is thought that the best long-term strategy for controlling schistosomiasis is through immunization with an anti-schistosome vaccine combined with drug treatment [7] . A vaccine that induces even a partial reduction in worm burdens could considerably reduce pathology and limit parasite transmission [8] . Currently , the most promising schistosome vaccine candidates are proteins located on the surface of the worms [9] , such as the tegument proteins TSP-2 [10] and Sm29 [11] . The tegument is a dynamic host-interactive surface involved in nutrition , immune evasion/modulation , excretion , osmoregulation , sensory reception , and signal transduction [12] , [13] . Other surface-exposed proteins with high potential as vaccine targets are located in the digestive tract of lung-stage schistosomula and adult worms [14]–[18] . In this study , we evaluated the potential of Sm10 . 3 , a member of the micro-exon gene 4 ( MEG-4 ) family , to serve as a component of a recombinant subunit vaccine . The Sm10 . 3 antigen was first described in 1988 [19] , but the Sm10 . 3 and MEG family genes were not completely characterized until the recent publication of the S . mansoni genome [20] . There are multiple copies of some MEGs in the S . mansoni genome , arranged as tandem , symmetrically organized exons with lengths that are a multiples of three bases ( from 6 and 36 base pairs ) [20] , [15] . It is thought that this arrangement may lead to protein variation through alternative splicing . Moreover , most of the MEGs are up-regulated during the stages in the parasite life cycle that involve establishment in the mammalian host [15] . In this study , we determined that Sm10 . 3 is located on the surface of the digestive tract of S . mansoni adult female worms and lung-stage schistosomula . We detected higher levels of Sm10 . 3 mRNA in the schistosomula stage of the parasite life cycle . We also show preliminary evidence that Sm10 . 3 plays a role in erythrocyte agglutination . Furthermore , we report that vaccination with rSm10 . 3 induces a mixed Th1/Th2-type immune response in mice , which correlates with a reduction in the worm burden and liver pathology .
All animal experiments were conducted in accordance with the Brazilian Federal Law number 11 . 794 , which regulates the scientific use of animals , and IACUC guidelines . All protocols were approved by the Committee for Ethics in Animal Experimentation ( CETEA ) at Universidade Federal de Minas Gerais UFMG under permit 179/2010 . Female C57BL/6 mice aged 6–8 weeks were purchased from the Federal University of Minas Gerais ( UFMG ) animal facility . S . mansoni ( LE strain ) cercariae were maintained in Biomphalaria glabrata snails at CPqRR ( Centro de Pesquisa René-Rachou-Fiocruz ) and prepared by exposing infected snails to light for 1 h to induce shedding . Cercarial numbers and viability were determined prior to infection using a light microscope . The plasmid pJ414 containing the sequence for rSm10 . 3 ( pJ414::Sm10 . 3 ) was manufactured by DNA 2 . 0 ( https://www . dna20 . com ) using DNA2 . 0 optimization algorithms for expression in Escherichia coli . This plasmid was transformed into E . coli Rosetta-gami ( Merck KGaA , Darmstadt , Germany ) competent cells . Transformants harboring the designed plasmid were screened on LB agar plates containing ampicillin ( 50 µg/ml ) and cloranphenicol ( 34 µg/ml ) and the selected transformant was designated as rSm10 . 3-Rosetta . One liter of rSm10 . 3-Rosetta was cultured in a three-liter erlenmeyer on a rotary shaker at 200 rpm at 37°C to an optical density at 600 nm of approximately 0 . 5–0 . 8 and gene expression was induced by using 1 mM isopropylthiogalactoside ( IPTG ) . After 5 h of induction , the bacterial cells were harvested by centrifugation at 4 , 000× g for 20 min . Using gently vortexing or pipetting , the pellet was resuspended in 50 ml of 10 mM Na2HPO4 , 10 mM NaH2PO4 , 0 . 5 M NaCl and 20 mM imidazole . Subsequently , the cells were submitted to three cycles of sonication lasting 30 s each and centrifuged at 5400× g for 20 min . The rSm10 . 3 was recovered solubilized in the supernatant and purified by affinity chromatography on a Ni-Sepharose column ( Hitrap chelating 5 mL ) using an AKTA explorer chromatography system ( GE Healthcare , São Paulo , Brazil ) . After protein binding to the Ni-Sepharose column , washes with 50 mM imidazole were performed and the protein was eluted with 500 mM imidazole . Fractions containing the protein were determined through Bradford's method ( Coomassie Protein Assay Kit , Pierce ) and also SDS/PAGE-12% and dialyzed against PBS pH 7 . 0 . The dialysis was carried out at 4°C using a Spectra/Por2 membrane ( MWCO 6 to 8 kDa; Spectrum Medical Industries , Inc . , Laguna Hills , CA ) . The recombinant protein was quantified using the Bradford's method and used as antigen for vaccination and immunological experiments . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) unless otherwise specified . Total RNA was isolated from adult parasites , eggs , miracidia , cercariae or schistosomula using published procedures [21] . Total RNA was extracted with Trizol ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer's instructions . RNA samples were treated with DNAse , purified and concentrated using the RNeasy Micro Kit ( QIAGEN , Valencia , CA , USA ) according to the manufacturer's instructions . A 1 . 5 µg portion of each sample was reverse transcribed using the SuperScriptH III First-Strand Synthesis SuperMix ( Invitrogen , Carlsbad , CA , USA ) . Specific primer pairs ( 5′-CTT AAT CAA TAA GCC AAA GG-3′ and 5′-TAT TGA TTT GTC GTA ATA GT-3′ ) were designed using the Primer Express program ( Applied Biosystems , Foster City , CA , USA ) and default parameters and arbitrarily named primers 1 and 2 , respectively . Real-time RT-PCR reactions were conducted in triplicate in a 20 µL volume containing 10 µL of Sybr Green PCR Master Mix ( Applied Biosystems , Foster City , CA , USA ) , 160 nmol of each primer ( primers 1 and 2 ) and 0 . 30 µL of the cDNA generated by reverse transcription . Real-time RT-PCR was performed using the 7300 Real-Time PCR System ( Applied Biosystems , Foster City , CA , USA ) and the following cycling parameters: 60°C for 10 min , 95°C for 10 min and 40 cycles of 95°C for 15 sec and 60°C for 1 min . A dissociation curve was generated using the following conditions: 95°C for 15 sec , 60°C for 1 min , 95°C for 15 sec and 60°C for 15 sec . Real-time data were normalized to the expression level of NADH dehydrogenase . p-values were determined by Student's t-test , using one-tailed distribution and heteroscedastic variance . Purified rSm10 . 3 was analyzed on 15% polyacrilamide SDS-PAGE gels prepared and run as previously described [22] . The gel was then transferred to a nitrocellulose membrane [23] . The membrane was blocked with TBS-T ( 0 . 5 M NaCl−0 . 02 M Tris ( pH 7 . 5 ) , 0 . 05% Tween 20 ) containing 5% dry milk for 16 h at 4°C . The membrane was then incubated with a mouse monoclonal antibody to the 6×His-tag ( GE Healthcare , Pittsburgh , PA , USA ) diluted 1∶2 , 000 or a mouse polyclonal antibody to rSm10 . 3 diluted 1∶1 , 000 for 1 h at room temperature . After three washes with TBS-T , the membrane was incubated in 1∶2 , 000 goat anti-mouse IgG conjugated to alkaline phosphatase ( AP ) , then treated with a developing buffer containing nitroblue tetrazolium ( NBT ) and 5-bromo-4-chloro-3-indolyl-1-phosphate ( BCIP ) . After the membrane was imaged , it was washed with distilled water and dried by sandwiching between two sheets of filter paper for storage . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) unless otherwise specified . Six to eight week-old female C57BL/6 mice were divided into two groups of ten mice each . Mice were injected subcutaneously at the nape of the neck with 25 µg of rSm10 . 3 protein or PBS ( for the control group ) on days 0 , 15 and 30 . The protein mixture was formulated using Complete Freund's Adjuvant ( CFA ) for the first immunization and Incomplete Freund's Adjuvant ( IFA ) for the last two immunizations ( Sigma-Aldrich , CO , St . Louis , MO , USA ) . For the microscopy studies , adult worms were recovered from perfused mice , and lung-stage schistosomula were prepared in vitro as described by Harrop & Wilson [24] . Parasites were fixed in Omnifix II ( Ancon Genetics , St Petersburg , FL , USA ) for sectioning . For the sectioning assays , 7 µm slices of Paraffin-embedded adult male or female parasites were deparaffinized using xylol and hydrated with an ethanol series , [25] . For experiments using in vitro cultured lung-stage schistosomula , a whole-mount protocol was chosen , lung stage schistosomula were treated with permeabilizing solution ( 0 . 1% Triton X-100 , 1% BSA and 0 . 1% sodium azide in PBS pH 7 . 2 ) overnight at 4°C [25] . Following , permeabilized schistosomula and parasite sections were blocked with 1% BSA ( bovine serum albumin ) in PBST ( phosphate buffered saline , pH 7 . 2 with 0 . 05% Tween-20 ) for 1 h and incubated with anti-rSm10 . 3 serum diluted 1∶20 in blocking buffer . Serum from non-immunized mice was used as a negative control . The samples were washed three times with PBST and incubated with an anti-mouse IgG antibody conjugated to FITC ( Molecular Probes , Carlsbad , CA , USA ) diluted 1∶100 in blocking buffer containing rhodamine phalloidin ( Molecular Probes , Carlsbad , CA , USA ) to stain the actin microfilaments . The samples were washed four times and mounted with ProLong Gold anti-fading mounting medium containing DAPI ( Molecular Probes , Carlsbad , CA , USA ) . Schistosomula were imaged on a Nikon A1R confocal microscope ( 60× NA1 . 4-CFI-Plan-Apo oil objective ) , and adult worms were imaged on a Nikon Eclipse Ti microscope from the Microscopy Center of the Biological Sciences Institute ( CEMEL ) at the Federal University of Minas Gerais ( UFMG ) . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) unless otherwise specified . Mice blood for the hemagglutination assays were collected and processed as described previously [26] . Blood was withdrawn from mice with a syringe , added to tubes containing EDTA ( at a final concentration of 12 mM ) and centrifuged for 15 min at 3000× g at room temperature . After centrifugation , the plasma was transferred to a fresh tube . In a second tube , erythrocytes were washed three times and suspended in PBS ( pH 7 . 2 ) to a hematocrit of 20 or 40% . The erythrocyte suspensions were used for the agglutination assays on glass slides , in the hemagglutination endpoint dilution assays or in the hemagglutination kinetics assays . Agglutination assays on glass slides was performed as previously described [26] , the erythrocyte suspensions were prepared as above to a final hematocrit of 20% diluted in PBS . Next , 9 µl of each erythrocyte suspension was combined with 1 µl of rSm10 . 3 containing 5 µg of purified protein , 1 µl of PBS or 1 µl of PBS containing 5 µg of rSm29 ( an unrelated S . mansoni antigen ) as negative controls . Recombinant protein rSm29 [11] was expressed with a 6×-histidine tag and purified in a similar way to rSm10 . 3 , as described in previous sections . The 10 µl mixture was loaded onto glass slides , covered with coverslips and immediately visualized using 10× and 40× objective lenses on a microscope equipped with a JVC TK-1270/RBG micro camera . Hemagglutination endpoint dilution assays were performed as previously described [27] , [28] . A serial two-fold dilution of 50 µl of rSm10 . 3 , rSm29 and Concanavalin A solutions ( protein concentrations ranged from 500 µg/mL to 0 . 97 µg/mL ) in microtiter U-plates was mixed with 50 µl of a 2% suspension of mice erythrocytes in PBS at 37°C , resulting in a final erythrocytes suspension volume of 100 µl and a hematocrit of 1% . In the blank wells 50 µl of a 2% suspension of mice erythrocytes were mixed with 50 µl of PBS . Concanavalin A was used as positive control of the hemagglutination process [29]–[31] and rSm29 ( an unrelated S . mansoni antigen ) as negative control . The results were read after approximately 1 h when the blank had fully sedimented . The endpoint was defined as the highest dilution showing complete hemagglutination . The hemagglutination titer , defined as the reciprocal of the highest dilution exhibiting hemagglutination , was defined as one hemagglutination unit . Specific activity is the number of hemagglutination units per mg of protein per milliliter [32] . Mice polyclonal antibodies raised against rSm10 . 3 were used as inhibitor in hemagglutination inhibition assays according to a protocol previously described [33] , with modifications . Twenty five microliters of rSm10 . 3 solutions with three different protein concentrations ( 1000 µg/mL , 500 µg/mL and 250 µg/mL ) were mixed with a serial two-fold dilution of 25 µl of anti- rSm10 . 3 mice serum , ranging from 1∶1 to 1∶32 dilution and incubated at 37°C for 30 min . Following , 50 µl of a 2% suspension of mice erythrocytes in PBS was added to the wells and incubated for 1 h at 37°C . The kinetics of the hemagglutination process were monitored by analyzing variations in turbidity [26] . Briefly , 100 µl of the erythrocyte suspension ( in PBS ) at a hematocrit of 5% was added to 96-well plates , and the agglutination was triggered by adding 0 . 5 to 5 µg of rSm10 . 3 diluted in 100 µl of PBS , 100 µl of PBS containing 5 µg of Concanavalin A , as positive control of the hemagglutination process and 100 µl of PBS or 100 µl of PBS containing 5 µg of rSm29 as negative controls , resulting in a final volume of 200 µl and a hematocrit of 2 . 5% . Plates were incubated at 37°C and spectrophotometric readings were taken at 655 nm every 13 s , with 8 s of shaking between each reading in a VersaMax Tunable Microplate reader ( Molecular Devices , Sunnyvale , CA , USA ) . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) . Fifteen days after the final immunization , the mice were challenged with 100 cercariae ( LE strain ) by percutaneous exposure of the abdominal skin for 1 h . Forty-five days after the challenge , the adult worms were perfused from the portal veins , as described previously [34] . Two independent experiments were performed to determine protection levels . The degree of protection was calculated by comparing the number of worms recovered from each vaccinated group to the respective control group , using the following formula:where PL indicates the protection level , WRCG indicates the number of worms recovered from the control group , and WREG indicates the number of worms recovered from the experimental group . Following immunization , sera were collected from ten mice in each experimental group at two week intervals . The levels of specific anti-Sm10 . 3 antibodies were measured by indirect ELISA . Maxisorp 96-well microtiter plates ( Nunc , Roskilde , Denmark ) were coated with 5 µg/mL rSm10 . 3 in carbonate-bicarbonate buffer ( pH 9 . 6 ) for 16 hat 4°C , then blocked for 2 h at room temperature with 200 µL/well PBST ( phosphate buffer saline , pH 7 . 2 with 0 . 05% Tween-20 ) plus 10% FBS ( fetal bovine serum ) . One hundred microliters of serum diluted 1∶100 in PBST was added to each well , and the plates were then incubated for 1 h at room temperature . Plate-bound antibodies were detected using peroxidase-conjugated anti-mouse IgG , IgG1 and IgG2a ( Southern Biotechnology , CA , USA ) diluted 1∶10000 , 1∶5000 and 1∶2000 in PBST , respectively . The color reaction was induced by adding 100 µL of 200 pmol OPD ( o-phenylenediamine ) in citrate buffer ( pH 5 . 0 ) plus 0 . 04% H2O2 to each well for 10 min . The color reaction was stopped by adding 50 µL of 5% sulfuric acid to each well . The plates were read at 495 nm in an ELISA plate reader ( BioRad , Hercules , CA , USA ) . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) unless otherwise specified . The cytokine experiments were performed using cultured splenocytes from individual mice immunized with rSm10 . 3 or PBS ( n = 4 for each group ) . The splenocytes were isolated from the macerated spleens of individual mice one week after the third immunization and washed twice with sterile PBS . After washing , the cells counts were adjusted to 1×106 cells per well in RPMI 1640 medium ( Gibco ) supplemented with 10% FBS , 100 U/mL of penicillin G sodium and 100 µg/mL of streptomycin sulfate . The splenocytes were maintained in culture with medium alone or stimulated with rSm10 . 3 protein ( 15 µg/mL ) , concanavalin A ( ConA ) ( 5 µg/mL ) , or LPS ( 1 µg/mL ) , as previously described [34] . The 96-well plates ( Nunc , Roskilde , Denmark ) were maintained in a 37°C incubator with a 5% CO2 atmosphere . The culture supernatants were collected after 24 h to measure IL-5 levels , after 48 h to measure TNF-α levels and after 72 h to measure IFN-γ and IL-10 levels . The cytokine measurement assays were performed using the DuoSet ELISA kit ( R&D Systems , Minneapolis , MN ) according to the manufacturer's instructions . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) unless otherwise specified . Following perfusion to recover the schistosomes , liver samples were collected from 8 animals each from the control and experimental groups to evaluate the effect of immunization on granuloma formation . The liver samples , which were taken from the central part of the left lateral lobe , were fixed with 10% buffered formaldehyde in PBS . Histological sections were performed using microtome at 6 µm and stained on a slide with picrosirius-haematoxylin-eosin ( PSHE ) . The count of granulomas was performed at a microscope with 10× objective lens . Each liver section was scanned for calculating its whole area ( mm2 ) using the ImageJ software ( http://rsbweb . nih . gov/ij/index . html ) . For measurement of the total area of granulomas , a microscope with 10× objective lens was used; images were obtained through a JVC TK-1270/RBG microcamera attached to the microscope . Twenty granulomas with a single-well-defined egg were randomly selected , in each liver section and the granuloma area was measured using the ImageJ software . Granuloma fibrosis was analyzed using the software analysis getIT ( Olympus Soft Imaging getIT ) and images to illustrate the fibrosis area were edited using Adobe Photoshop software . All reagents were purchased from Sigma-Aldrich , CO ( St . Louis , MO , USA ) . The results from the two experimental groups were compared by Student's t-test using the software package GraphPad Prism ( La Jolla , CA ) . Bonferroni adjustments were included for multiple comparisons . The p-values obtained by this method were considered significant if they were <0 . 05 . Sm10 . 3 ( M22346 . 1 ) , Sm29 ( AF029222 . 1 ) , Tsp2 ( AF521091 . 1 ) .
The expression of the Sm10 . 3 gene was detected by real-time PCR at different stages in the S . mansoni life cycle . The only stage during which Sm10 . 3 mRNA was not detected was the miracidium stage . The highest level of Sm10 . 3 mRNA expression was observed in lung-stage schistosomula . Sm10 . 3 expression was also detected in eggs , adult worms and cercariae , but at lower levels than in the schistosomula ( Fig . 1 ) . Cloning and heterologous expression of the Sm10 . 3 gene was performed as described in the material and methods section . Recombinant Sm10 . 3 ( rSm10 . 3 ) was purified from bacteria , and the first seven fractions that were eluted from an affinity chromatography column were combined and dialyzed in PBS pH 7 . 2 and then analyzed by SDS-PAGE followed by Coomassie blue staining ( Fig . 2A ) . The strong band visible at approximately 26 kDa , the expected mass of the purified rSm10 . 3 , indicates the success of the purification protocol ( Fig . 2A ) . To further evaluate the specificity of the purification procedure , the purified rSm10 . 3 was analyzed by western blot using a mouse monoclonal anti-His tag antibody ( Fig . 2B ) . The western blot analysis confirmed that the protein around 26 kDa had a histidine tag and this is the molecular weight expected for rSm10 . 3 ( Fig . 2A , B ) . The localization of Sm10 . 3 was determined in S . mansoni lung-stage schistosomula ( Fig . 3C and D ) , female adult parasites ( Fig . 3G and H ) and male adult parasites ( Fig . 3K and L ) using specific mouse polyclonal antibodies to rSm10 . 3 and fluorescence microscopy . Rhodamine phalloidin ( red ) was used as an actin marker to label the cytoskeletal tegument components and muscle layers ( Fig . 3B , D , F , H , J and L ) . The cell nuclei were stained with DAPI ( blue ) ( Fig . 3 ) . The native Sm10 . 3 protein ( green ) was located exclusively in the internal tissues of lung-stage schistosomula ( Fig . 3C and D ) , as well as on the surface of the esophageal and intestinal epithelia of adult parasites ( Fig . 3G and H ) . No Sm10 . 3-specific signal was detected in sera from naïve mice ( pre-serum ) in either the adult parasites ( Fig . 3E , F , I and J ) or the lung-stage schistosomula ( Fig . 3A and B ) . To evaluate the role of Sm10 . 3 in the digestive epithelia of adult worms and its possible contribution to the erythrocyte feeding process , we analyzed the effect of rSm10 . 3 on mouse erythrocytes suspended in PBS . The kinetics of the hemagglutination process were monitored analyzing variations in turbidity by an adapted protocol that was previously described [26] . This methodology allows the measurement of hemagglutination due to the formation of erythrocyte clumps and a subsequent reduction in absorbance . Different amounts of rSm10 . 3 were added to erythrocyte suspensions at a haematocrit of 5% and followed over time by reading the absorbance at 655 nm with a spectrophotometer . The recombinant protein rSm29 [11] was used as a negative control and the lectin concanavalin A as a positive control of the erythrocytes hemagglutination process , as previously demonstrated [29]–[31] . The addition of a similar amount of rSm29 did not induce any changes in the absorbance . However , the addition of 5 µg/mL or 10 µg/mL of rSm10 . 3 resulted in a 5% reduction in absorbance , and higher amounts of rSm10 . 3 ( 50 µg/mL ) reduced the absorbance by 10–15% as compared to rSm29 , while 50 µg/mL of concanavalin A decreased the absorbance by 28–32% after 400 s compared to rSm29 ( Fig . 4I ) . The hemagglutination process induced by rSm10 . 3 can also be observed microscopically , as shown by the formation of erythrocytes clumps upon the addition of recombinant Sm10 . 3 ( Fig . 4E and F ) . Later , the hemagglutinating effect of rSm10 . 3 in mice erythrocytes was clearly evidenced by hemagglutination endpoint dilution assays . Protein concentrations of rSm29 up to 250 µg/mL were not able to induce hemagglutination ( Fig . 4G ) , while rSm10 . 3 caused erythrocytes agglutination with the lowest protein concentration of 31 . 2 µg/mL ( Fig . 4G ) . The minimal protein concentration of concanavalin A required to induce hemagglutination was 3 . 9 µg/mL , approximately eight times lower than the amount observed for rSm10 . 3 ( Fig . 4G ) . These results confer to concanavalin A a total hemagglutinating activity of 1 . 56×102 units ( U ) and a specific hemagglutinating activity of 40000 U/mg/mL ( Table S1 ) , while these values for rSm10 . 3 were 13 times lower and 100 times lower , respectively , 0 . 12×102 units and 400 U/mg/mL ( Table S1 ) . By means of hemagglutination inhibition assays using anti-rSm10 . 3 it was demonstrated the specific and protein concentration dependent pattern of the rSm10 . 3 hemagglutinating activity ( Fig . 4H ) . There was no hemagglutination inhibition with 250 µg/mL of rSm10 . 3 , even at the highest anti-rSm10 . 3 concentration ( 1∶1 dilution ) . However , with lower rSm10 . 3 concentrations its hemagglutinating activity was inhibited by anti-rSm10 . 3 ( Fig . 4H ) . When using 125 µg/mL of rSm10 . 3 there was inhibition at anti-rSm10 . 3 dilutions ranging from 1∶1 to 1∶8 , while with 62 . 5 µg/mL of rSm10 . 3 the inhibition was evident from 1∶1 to 1∶16 dilutions ( Fig . 4H ) . Sera from ten animals from each vaccination group were tested by ELISA to evaluate the levels of specific IgG , IgG1 and IgG2a antibodies to rSm10 . 3 . Significant titers of anti-rSm10 . 3 IgG antibodies were detected at all time points tested after the first immunization ( Fig . 5A ) . The levels of specific IgG1 antibodies increased at 30 days after the first immunization ( Fig . S1A ) , while the levels of specific IgG2a antibodies continued to increase up to day 75 ( Fig . S1B ) . Furthermore , the IgG1/IgG2a ratio was reduced at days 45 , 60 , 75 and 90 ( Fig . 5B ) , which correlates with the elevation in anti-rSm10 . 3 IgG2a production ( Fig . S1B ) . To test the potential usefulness of rSm10 . 3 as part of an anti-schistosome vaccine , we asked whether this recombinant antigen could induce protection in a murine model of S . mansoni infection . Two independent vaccination trials were conducted and C57BL/6 mice were immunized three times with rSm10 . 3 formulated with Freund's adjuvant and then challenged with 100 S . mansoni cercariae . The control group received adjuvant only in phosphate-buffered saline . Mice vaccinated with rSm10 . 3 showed a 32 . 0% reduction in the adult worm burden in the first trial ( Fig . 5C ) and 25 . 5% reduction in the second trial ( Fig . 5D ) . Regarding the number of eggs in mice livers , it was observed 43 . 6% and 32 . 9% reduction in the number of eggs per gram of liver tissue in the first and second trials , respectively ( Fig . 5E ) . As shown in Figure 6A–D , histopathological analysis of the hepatic tissue , from animals of the first vaccination trial , demonstrated that rSm10 . 3 immunization reduced the extent of fibrosis compared to control animals . These analysis showed a 23 . 8% reduction in liver granuloma counts ( Fig . 6E ) , an 11 . 8% reduction in granuloma area ( Fig . 6F ) , and a 39 . 8% reduction in granuloma fibrosis ( Fig . 6G ) , as compared to control mice . To evaluate the cytokine profile of mice immunized with rSm10 . 3 , splenocytes were isolated from spleens of vaccinated and control animals after the third immunization . Statistically significant levels of IFN-γ , the signature cytokine of a Th1-type immune response , and TNF-α , a pro-inflammatory cytokine , were detected in the supernatant of cultured splenocytes from immunized animals compared to the control group ( Fig . 7A and B , respectively ) . IL-5 , a characteristic Th2-type cytokine , was also detected in the supernatant of cultured splenocytes from rSm10 . 3-immunized mice at statistically significant levels compared to the PBS control ( Fig . 7-D ) . Furthermore , high levels of the modulatory cytokine IL-10 were also observed in vaccinated animals ( Fig . 7C ) . Concanavalin A ( ConA ) and LPS were used as positive controls to confirm that the splenocytes were responsive to stimuli . As shown in Figure 7 , ConA induced the production of IFN-γ , IL-5 and IL-10 , while LPS induced the production of TNF-α .
Schistosomiasis is one of the most important neglected tropical diseases . Effective control is unlikely in the absence of improved sanitation and the development of a vaccine . Proteins located at the host/parasite interface , particularly molecules that are secreted or surface-exposed on the tegument and the intestinal epithelia , are the most promising targets for developing an anti-schistosomiasis vaccine [18] . We therefore evaluated the potential of the Sm10 . 3 antigen as a vaccine candidate , as it was previously reported to be localized on the esophageal gland in schistosomula and adult worms [15] , as well as on the gut primordium of the nonfeeding cercaria [15] . The deduced amino acid sequence of Sm10 . 3 contains a signal peptide , and the protein was predicted to be secreted or localized to the exterior surface of the cell . The gene products of several other MEG family members contain signal peptides for secretion and are secreted from different schistosomal glands and epithelia [20] , [15] . We confirmed previous reports [20] , [15] that Sm10 . 3 is mainly expressed in the schistosomulum stage , as well as in other stages that involve contact with the mammalian host , such as eggs , cercariae and adult worms . MEG genes are difficult to clone , primarily due to extensive alternative splicing that generates variant transcripts of different sizes through exon skipping and the arbitrary combination of exons [19] , [20] , [15] . This variation in MEG gene products may represent a strategy used by members of the Schistosoma genus to confuse the host immune system , similar to the mechanisms of surface protein variation in Trypanosoma brucei and Plasmodium falciparum [20] , [15] , [18] . In this study , we produced the recombinant Sm10 . 3 protein from a synthetic gene that allowed us to express a protein corresponding to the largest transcript from the Sm10 . 3 gene to optimize codon usage and avoid errors in the amino acid sequence . Our fluorescence microscopy data confirm the in silico prediction that Sm10 . 3 is secreted or located on the cell surface , demonstrating that the native Sm10 . 3 protein localizes to the epithelia and lumen of the intestinal tract in adult parasites , as well as to the internal tissues of lung-stage schistosomula . A previous microarray study demonstrated an increase in Sm10 . 3 expression in lung-stage schistosomula [15] . In the same study , Sm10 . 3 localization was examined using an antibody against a synthetic peptide ( immunocytochemistry ) and RNA hybridization ( WISH ) . The authors found Sm10 . 3 proteins in the esophageal gland and the esophageal lumen of adult worms , but were unable to define the localization of Sm10 . 3 in the larval stage [15] . A more recent study from this group demonstrated that the Sm10 . 3 antigen is highly O-glycosylated , which means that native Sm10 . 3 a very sticky macromolecule that is highly likely to adhere to surfaces [35] . The authors suggest that this adherence could be responsible for the immunodetection of Sm10 . 3 not only in the esophageal gland but also throughout the entire esophagus ( distal to secretion sites ) [35] , [15] . We did not detect any Sm10 . 3 accumulation in the esophageal glands , but we did find Sm10 . 3 on the esophageal epithelia , in the esophageal lumen and in the gut epithelia . It was observed immunoreactivity in the gut epithelia and sometimes in the gut lumen in all samples that were imaged . However , it was not possible to obtain image sections showing the entire gut length , which prevent us to state that Sm10 . 3 is present in the whole gut . These apparent differences in Sm10 . 3 localization could be due to the different antibodies that were used in the studies . Alternatively , variant Sm10 . 3 transcripts could be differentially regulated depending on the experimental conditions . It has been previously shown that environmental stimuli can affect gene expression in S . mansoni , as the presence or absence of erythrocytes altered the transcription levels of genes expressed in the tegument and related to feeding [14] . To further investigate the role of Sm10 . 3 protein in the esophagus and gut of adult worms , we analyzed the effect of rSm10 . 3 protein on erythrocytes and found that rSm10 . 3 induced hemagglutination in vitro . This effect could be related to erythrocyte digestion and nutrition in adult worms , and may represent a role for Sm10 . 3 in adult worms . However , it is necessary to evaluate in vivo the impact of this Sm10 . 3-induced hemagglutination on the blood feeding process . In addition to the possible role of Sm10 . 3 protein in blood feeding , it is also likely that Sm10 . 3 contributes to protein variation , a role that has been previously proposed for the MEG family members [20] , [15] , [18] . The digestive tracts of schistosomes in the blood feeding stages are accessible to macromolecules such as albumin and immunoglobulins [36] , [37] , which implies that there may be direct contact between the digestive epithelia and the host immune system . We also assessed the interaction of Sm10 . 3 with the host immune system and its potential as a vaccine candidate . rSm10 . 3 induced high levels of anti-rSm10 . 3 IgG production in the sera of immunized mice after the second immunization . A decrease in the ratio between the IgG subtypes ( IgG1/IgG2a ) was observed 45 days after the first immunization . Furthermore , cytokine analysis of the supernatants of cultured splenocytes stimulated with rSm10 . 3 suggests a mixed Th1/Th2-type immune response . Studies using the irradiated cercariae model , which induces high levels of protection in mice , suggest that effective protection can be based on a mixed Th1/Th2 response , a polarized Th1 response or even a polarized Th2 response [38] . In previous studies performed by our group , the majority of S . mansoni antigens tested as recombinant protein vaccines that conferred partial protection against cercariae challenges induced a Th1-type immune response [11] , [34] , [39] , [40] or a mixed Th1/Th2 response [41] , [42] , [43] . IFN-γ is involved in protective immunity against schistosomiasis , as specific anti-IFN-γ antibodies completely abolish the protection conferred by vaccination with irradiated cercariae [44] . Similar results were obtained in a study using IFN-γ knockout mice [45] . The partial protection conferred by vaccination with rSm10 . 3 resulted in 25 . 5% to 32% reduction in worm burden , and the overall pathology was reduced , as shown by 32 . 9% to 43 . 6% reduction in the number of eggs per gram of hepatic tissue , a 23 . 8% reduction in the number of granulomas , an 11 . 8% reduction in the area of the granulomas and a 39 . 8% reduction in granuloma fibrosis . It is possible that the reduced liver pathology is related to the elevated levels of IL-10 detected in immunized mice , which may regulate Th2 responses and/or prevent the development of a polarized Th1 response , consequently reducing inflammation and liver injury [46] , [47] . In conclusion , our results confirm that Sm10 . 3 is mainly expressed during the stages of the Schistosoma mansoni life cycle that involve contact with the mammalian host . We show that the Sm10 . 3 protein is located in the intestinal tract of adult worms , providing the first evidence for a possible role for Sm10 . 3 in the blood feeding process . Finally , our data suggest that Sm10 . 3 is a potential candidate for use in developing a multi-antigen vaccine to control schistosomiasis .
|
Schistosomiasis mainly occurs in developing countries and is the most important human helminth infection in terms of global mortality . This parasitic disease affects more than 200 million people worldwide and causes more than 250 , 000 deaths per year . Current schistosomiasis control strategies are mainly based on chemotherapy , but many researchers believe that the best long-term strategy for controlling schistosomiasis is a combination of drug treatment and immunization with an anti-schistosome vaccine . Consequently , significant effort has been dedicated to developing and characterizing an anti-schistosome vaccine . Over the last five years , considerable data have been generated regarding the genomics , transcriptomics and proteomics of Schistosoma mansoni . In the present study , we characterize the Sm10 . 3 protein and evaluate its potential to protect against S . mansoni infection in a murine model . We demonstrate that Sm10 . 3 is primarily expressed during the stages of the parasite life cycle that involve infection and disease development in the human host . Sm10 . 3 is located on the surface of the digestive epithelia of adult female worms , an important host/parasite interface . Moreover , the vaccination of mice with rSm10 . 3 confers partial protection against S . mansoni . Taken together , our data suggest that Sm10 . 3 may be a useful component of a multi-antigen vaccine against schistosomiasis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunology",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"parasitology"
] |
2014
|
Sm10.3, a Member of the Micro-Exon Gene 4 (MEG-4) Family, Induces Erythrocyte Agglutination In Vitro and Partially Protects Vaccinated Mice against Schistosoma mansoni Infection
|
Mass drug administration ( MDA ) with antibiotics is a key component of the SAFE strategy for trachoma control . Guidelines recommend that where MDA is warranted the whole population be targeted with 80% considered the minimum acceptable coverage . In other countries , MDA is usually conducted by salaried Ministry of Health personnel ( MOH ) . In Plateau State , Nigeria , the existing network of volunteer Community Directed Distributors ( CDD ) was used for the first trachoma MDA . We conducted a population-based cluster random survey ( CRS ) of MDA participation to determine the true coverage and compared this to coverage reported from CDD registers . We surveyed 1 , 791 people from 352 randomly selected households in 24 clusters in three districts in Plateau State in January 2011 , following the implementation of MDA . Households were enumerated and all individuals present were asked about MDA participation . Household heads were questioned about household-level characteristics and predictors of participation . Individual responses were compared with the CDD registers . MDA coverage was estimated as 60 . 3% ( 95% CI 47 . 9–73 . 8% ) by the survey compared with 75 . 8% from administrative program reports . CDD registration books for comparison with responses were available in 19 of the 24 clusters; there was a match for 658/682 ( 96% ) of verifiable responses . CDD registers did not list 481 ( 41 . 3% ) of the individuals surveyed . Gender and age were not associated with individual participation . Overall MDA coverage was lower than the minimum 80% target . The observed discrepancy between the administrative coverage estimate from program reports and the CRS was largely due to identification of communities missed by the MDA and not reported in the registers . CRS for evaluation of MDA provides a useful additional monitoring tool to CDD registers . These data support modification of distributor training and MDA delivery to increase coverage in subsequent rounds of MDA .
Trachoma , caused by infection with the bacterium Chlamydia trachomatis , is the leading cause of infectious blindness worldwide [1] , [2] . Over time , repeated infections develop scar tissue on the inside of the eyelid , pulling the eyelashes towards the eye where they abrade the cornea , resulting in corneal opacity , low vision and blindness . Where trachoma is a public health problem , the World Health Organization ( WHO ) recommends the implementation of the full SAFE Strategy ( Surgery , Antibiotic Therapy , Facial Cleanliness and Environmental Change ) . The “A” arm of the SAFE strategy calls for antibiotic treatment with a target of administering antibiotics to at least 80% of the population , using oral azithromycin or topical ( ophthalmic ) tetracycline ointment which treats individual cases and reduces the reservoir of ocular chlamydia in the community . If the prevalence of clinical trachoma ( grade trachomatous inflammation , follicular , known as “TF” ) exceeds 10% among children one to nine years of age , mass distribution of antibiotics is warranted at the district level ( defined as an administrative unit of approximately 150 , 000–250 , 000 persons ) . Once initiated , district-wide antibiotic distribution is implemented annually until the program reduces the prevalence of clinical signs of trachoma among children to below 10% [3] . Prevalence surveys conducted in Nigeria suggest that trachoma is of public health importance in parts of northern and central Nigeria [4]–[7] . Seven of the 30 LGAs surveyed had a prevalence of TF among children ages one to nine years of age greater than 10% , qualifying for district-wide mass drug administration ( MDA ) of antibiotics for trachoma control in the context of the full SAFE strategy . In June 2010 , Nigeria received its first donation of azithromycin from Pfizer , Inc . via the International Trachoma Initiative for the implementation of district-wide MDA in these seven LGAs . The MDA treatment protocol for trachoma is described elsewhere [8] . The Plateau and Nasarawa state ministries of health implemented the first MDA for trachoma control in the seven eligible LGAs from October to November 2010 . It was delivered through the existing community-directed treatment network established in the 1990s to implement MDA for onchocerciasis , and more recently lymphatic filariasis , and schistosomiasis [9] . Under this delivery model , two to four volunteer community drug distributors ( CDDs ) are selected from the community by local leadership to implement MDA activities . Smaller communities are often served by a CDD from a neighboring village . All communities in the seven trachoma-endemic LGAs were eligible to participate in the MDA and the entire LGA population was targeted for treatment . CDDs participate in annual two-day trainings on the drug distribution protocol to prepare them to register community members , manage drug supply and administer treatment . Prior to MDA , CDDs are responsible for mobilizing community participation via health education sessions . Over the course of a two-week MDA , CDDs administer drugs at central sites in their community or conduct treatments at the household level . CDDs are not compensated directly by the program , but may receive other opportunities to earn per diem because of their experience or receive cash or gift-in-kind contribution from their community . Each individual who resides in the community is named in the CDD register . When the community member receives treatment during MDA , his/her participation is recorded by the CDD . Upon completion of the MDA , the number of people treated by each CDD is reported to the LGA and state health services . The administrative coverage of MDA is calculated by aggregating the treatment reports from all CDDs at the LGA-level and dividing by the total population of the LGA as estimated by the MDA registered population . In other trachoma control programs , management of the drug is the responsibility of the health personnel and stocks of azithromycin are carefully monitored within the health system and not handed over to volunteers or left in the villages overnight . Therefore , to validate coverage estimates calculated from CDD reports , we conducted a survey to estimate the prevalence of participation in trachoma MDA . The primary objective of the study was to estimate the proportion of individuals who participated in the MDA . Secondary outcomes included the proportion of trachoma-related health education among heads of household and the proportion of participation verified by the CDD register .
In order to detect a prevalence of participation in MDA of at least 50% with 80% power , and an alpha level of 0 . 05 , including a design effect of 4 . 0 [5] and a 20% non-response rate , a sample size of 1 , 843 persons was required . Assuming an average household size of five persons and 24 clusters , 15 households per cluster were planned to be surveyed . This survey was implemented using a two-stage cluster random survey design . Clusters were defined using the census administration unit known as an Enumeration Area ( EA ) , for which borders are well-delineated and populations are approximately the same size , eliminating the need for probability proportional to size sample selection . In the first stage , the total 2 , 838 mutually exclusive EAs in the three LGAs were listed in order of geographic proximity and 24 clusters were selected systematically by calculating a sampling interval ( n ) , randomly selecting the first EA from the first n EAs and the 23 subsequent nth EAs . In the second stage , sampling of households was performed using the map and segment method to ensure equal , non-zero probability of selection within each cluster [10] . Using cluster maps , the cluster was then divided into segments of five households each , and three segments were randomly selected . All households in the selected segments were eligible to participate in the survey . A household was defined as a group of people who ate from the same cooking pot . If two or more households were enumerated in the same dwelling , the individual heads of households were interviewed separately . The survey team returned to vacant households at least twice on the day of interview . Heads of households who were out , made themselves repeatedly absent , or declined to participate were not replaced . In each household , informed consent was obtained from the head of household ( or available household member aged 15 years and older ) before the interviews began . A standard pre-coded questionnaire was administered to each head of household to collect basic household demographic data and knowledge of trachoma disease , the SAFE strategy and the household-level interaction with the CDD during MDA . The survey teams also reported the physical presence of a household pit latrine and whether there was evidence of use ( feces in the pit ) as a proxy indicator of the household's participation in hygiene and sanitation programs . After the head of household interview was completed , the members of the entire household ( including those absent ) were enumerated on a census form . Each member was asked to report their age , type of antibiotic taken ( if any ) , and reason for not participating , if applicable . Children ages 6–15 years of age were asked if they had ever received multiple drug treatment for onchocerciasis , lymphatic filariasis , schistosomiasis , or soil-transmitted helminthes after being shown an example of the pills administered for each disease . In the event that a household member was absent , the head of household responded as a proxy . Survey participants were shown examples of azithromycin bottles and tablets and empty pediatric oral suspension bottles to enhance recall . Once all household interviews in the cluster were completed , the survey team reviewed the CDD register to verify the responses of the participants . For example , if a participant said that s/he did not receive treatment but the CDD logbook indicated that s/he had , the response would be recorded as “not verified” . If the participant was not listed in the register then the response was coded as “not recorded” . Data collection team members were recruited from Plateau State and participated in training on the study protocol , household selection , and the administration of the questionnaire . The questionnaire was translated into Hausa and back-translated into English to verify the accuracy of the translation . Before the fieldwork began , the questionnaire was pilot-tested among households in a village not selected for the study . Survey data were double-entered and validated in Microsoft Access 2003 . The analysis was conducted using SAS version 9 . 2 ( The SAS Institute Cary , NC ) . The primary outcome of the study was the proportion of total respondents who were present during the survey that reported having taken antibiotics during the MDA . The household-level questionnaire was analyzed using the SURVEYFREQ procedure to account for the correlation among the data due to the study design and to allow weighted analysis to adjust for differences in probability of household selection between EAs . All frequencies presented in this paper are weighted unless otherwise specified . Individual level responses from the census form were also analyzed using cluster-level weights . The proportion of individuals who reported participation was calculated using only the individuals present at the time of interview to reduce the introduction of systematic error due to recall and response bias on behalf of other household members . A kappa coefficient was calculated using the proportion of agreement between household responses and the CDD register . In order to determine if there was an association between age and gender among individual responses and the proportion of participation in the MDA , linear regression using a generalized estimating equation ( GEE ) with a binomial distribution and identity link was performed to calculate the prevalence difference of participation among individuals whose head of household reported prior knowledge of trachoma MDA , knowledge of the purpose of trachoma MDA and treatment received at home . A sensitivity analysis was performed to compare the probabilities derived from a logistic regression model and the linear regression to check the model fit . Confounding by head of household age , gender , and head of household advance knowledge of the MDA was assessed . The prevalence difference of participation was also calculated for the subset of the households where the head of household reported having received antibiotics for trachoma from a CDD . Administrative treatment data and population estimates from the MDA were provided by the Plateau State Trachoma Control Program in order to compare the administrative coverage against the results of the survey . Administrative coverage was calculated as the number of people treated with MDA divided by the total population of the LGA . This study was reviewed and approved by the Emory University IRB under protocol 00009342 . The Plateau State Ministry of Health approved the survey as an ongoing program monitoring activity . Upon arrival in the community , standardized consent statements were read in Hausa or the local language to the village head to request permission to enter the community . Verbal informed consent was obtained from the head of household prior to conducting interviews in the household . Each adult household member selected for participation was read a standardized consent form and verbal consent obtained prior to administering the survey , Verbal informed consent was obtained from parents or guardians of children under the age of 18 and verbal assent was obtained from children ages 6 and above prior to conducting interviews . Oral informed consent/assent was approved by the Emory IRB due to low literacy rates in the population and because no samples or specimens were taken during the survey , causing no additional risk to the participants than those experienced in daily life . Oral consent/assent was documented on individual survey forms prior to the commencement of data collection . No incentives were offered or provided to any participant . ”
The survey team visited 24 EAs , where a total of 392 households were eligible for interview . Verbal informed consent was obtained from 352 heads of households , for a head of household-level response rate of 90% . Figure 2 illustrates the composition of the sample . The weighted proportion of household-level respondents who were male was 61 . 4% ( 95% CI: 52 . 5–70 . 3% ) and the mean age of the head of household respondent was 41 . 7 years ( SD: 1 . 1 ) . The mean household size was 5 . 9 persons ( SD: 0 . 2 ) . A pit latrine was observed in 30 . 4% of households ( 95% CI: 17 . 8–42 . 9% ) . The results of household-level responses on participation in trachoma MDA are summarized in Table 1 . Knowledge of trachoma was assessed by asking the head of household to name the sequelae of the disease if he/she claimed to know of trachoma . Among those who said they knew of trachoma , 94 . 1% ( 95% CI: 91 . 1–97 . 1% ) demonstrated knowledge of the disease by their survey response and 87 . 9% ( 95% CI: 82 . 2–93 . 5% ) demonstrated knowledge of some aspect of the SAFE strategy at the time of the survey . A total of 2 , 103 people were enumerated in the household census . Among those enumerated , 1 , 791 people were present at the time of the survey . The mean proportion of members present for the interview in each household was 89 . 3% ( 95% CI: 84 . 2–94 . 4% ) . Among those present , 60 . 3% reported having received azithromycin or tetracycline eye ointment during MDA ( 95% CI: 47 . 9–73 . 8% ) . Participation in MDA among children ages 1–9 years ( the target age group of the trachoma control program ) was 58 . 8% ( 95% CI: 42 . 9–74 . 6% ) . Approximately 54 . 1% of children ages 6–15 years ( of a total 492 children ) reported ever participating in a prior non-trachoma MDA ( 95% CI: 35 . 1–73 . 1% ) , with 25 . 7% ( 95% CI: 8 . 3–43 . 1% ) of those respondents able to identify the disease for which they were treated . Table 2 presents the weighted frequencies of participation in the MDA for trachoma control for those present at the interview . The prevalence difference estimates derived from the model are presented in Table 3 . Overall , there is a large difference in participation among individuals if the head of household was informed in advance and knew about the purpose of the trachoma MDA . Comparison of participant responses to the CDD register was only feasible in 19 out of the 24 EAs due to either the absence of the CDD or the register at the time of the survey; all household members for whom verification data were available were included in this sub-analysis . The number of participant records that were verified is described in Figure 3 . In the 19 EAs where the CDD register was available , a total of 1 , 163 people were present for the census . From that sub-group of the study population , a total of 658 participant responses were verified out of 682 responses that were compared against the CDD register: 542 responses were verified as having participated , 116 were verified as having not participated and 24 responses did not match the record in the registration book . Of the responses that could be verified , agreement with the register was 96% ( 632/658 , unweighted ) , with a kappa coefficient of 0 . 88 ( 95% CI: 0 . 84–0 . 93 ) . Among the entire study population present at the time of interview , 41 . 4% ( 481/1 , 163 , unweighted ) were not listed in the CDD register book ( where a register book was examined for that cluster during the survey ) . To estimate the proportion of participation in the MDA among the clusters where the CDD registers were employed , we restricted the analysis to responses that were verified by the CDD register book . In these clusters , 79 . 9% ( weighted for cluster ) of the respondents participated in the MDA ( 95% CI: 76 . 9–82 . 9% ) . The Plateau State Trachoma Control Program reported administrative coverage of MDA as 75 . 8% overall , being: 112 , 066 persons treated in Langtang North ( 80% of the 140 , 643 total population ) ; 185 , 454 persons treated in Shendam ( 89% of the 208 , 017 total population ) ; and 89 , 166 persons treated in Wase ( 55% of the 161 , 714 total population ) . This compares to 60 . 3% overall coverage reported by participants randomly selected from all LGAs in the cluster random .
The results of this coverage survey and the administrative data from the MOH show that the population coverage of MDA did not meet the WHO-recommended minimum target of 80% coverage . The overall proportion of the three LGA-level coverage estimates from the MOH administrative data ( 75 . 8% ) overstates the coverage obtained in these areas compared to the survey point estimate of 60 . 3% . Participation among children ages 1–9 years ( 58 . 8% ) was similar to the overall survey estimate . Participation among clusters where treatment was correctly recorded in the registers ( 79 . 9% ) was similar to the administrative data , suggesting that coverage was greater were the CDD followed MDA protocol . The proportion of children ages 6–15 years who recalled participating in non-trachoma MDA was approximately 54 . 1% , which is consistent with the coverage estimated for trachoma MDA . This survey compared CDD registers with participant survey responses as a novel method to verify responses post distribution and quantify recall bias . Although verification of responses was not possible in all clusters , the high concordance of responses ( 96% ) with the treatment registers suggests that participant recall was accurate . Participants were also shown examples of the antibiotic treatments to enhance recall and avoid confusion with non-trachoma MDA treatments ( which differ markedly in shape , size and color of tablets and are administered separately ) . Although agreement between respondents and CDD registers was high , the proportion of responses that were not verified suggests that use of CDD registers could be strengthened through additional CDD training . Almost a third of head of household respondents reported receiving antibiotics from someone other than a CDD ( refer to Table 1 ) , suggesting that the community may not be universally aware of the identity of the CDD or the CDD works with other community members to administer treatment . Since MDA in Nigeria is community-directed , these data support the recommendation that future CDD training activities address the role of community volunteers in pre-MDA mobilization . Reported knowledge of trachoma was high among head of household respondents , yet only 56 . 9% of respondents knew what azithromycin and tetracycline treated . This suggests that although awareness of the SAFE strategy is prevalent , knowledge of MDA for trachoma should be reinforced by the CDD during mobilization activities . Knowledge of the diseases for which the other MDA drugs are distributed was also comparably low among children ages 6–15 years , further reinforcing the need for additional health education either before or during MDA for all control programs in Plateau state . Although these findings are sufficient to generate recommendations for future implementation of MDA , the results of the survey should be interpreted in light of the following limitations . The three LGAs were sampled as one evaluation unit , which does not estimate population coverage for the individual-level LGAs . As illustrated by the administrative data , there are likely to be minor variations in the coverage estimates and household-level characteristics at the LGA-level . We calculated frequencies without correcting for household-level correlation; however , a sensitivity analysis comparing the results from PROC SURVEYFREQ and a GEE controlling for household showed that the results from these approaches were similar . The administrative unit for the clusters , the census EA , did not always match exactly the catchment areas for individual CDDs , which may have introduced bias in terms of verification of responses with CDD registers . Although the head of household-level response rate was almost 90% , missing data from absent individuals reduces the sample size and precision in our estimates . Notably , the missing CDD registers limit the generalizability of the kappa statistic . The implementation of a cluster random survey to estimate the population coverage of MDA enables trachoma control programs to identify opportunities to improve the implementation of MDA and community participation . Without periodic coverage surveys , programs would be reliant on administrative coverage estimates to monitor treatment performance . The discrepancy between reported administrative coverage and the survey results in this evaluation suggest that reliance on administrative data alone is not sufficient for monitoring of MDA performance . In other settings , cluster random surveys to estimate the population coverage of mass distribution interventions have shown that administrative records are not always reliable [11]–[13] . The low coverage achieved by this MDA may be attributed to the use of village lists for planning and implementation of the program from other disease control programs , whereas in clusters where the CDDs followed program monitoring guidelines using treatment registers , the survey results are more consistent with administrative data . To improve the performance of future antibiotic distributions , these data suggest that there is a need to review community-level population estimates to ensure that coverage calculated from CDD registers is accurate . Furthermore , if the program population data underestimate the true population , administrative coverage data will be inflated , and the supply of future antibiotics may not be sufficient to treat the entire population . Administrative data is often biased due to a reliance on population estimates for the denominator , which can be incorrect between census years . Administrative data is also vulnerable to bias from lost forms or inaccurate records . Conducting a cross-sectional survey to measure the prevalence of participation in MDA provides an opportunity for trachoma control programs to validate the administrative reports and measure covariates to identify factors associated with increased participation . Accurate population coverage estimates are essential to measuring the overall effectiveness of the SAFE strategy on reducing trachoma as part of routine program evaluations . These data demonstrate that household surveys , when compared with administrative coverage data , are a useful tool to monitor MDA performance .
|
The World Health Organization recommends that mass drug administration for trachoma control reach a minimum of 80% of the target population . Previous evaluations of MDA coverage have demonstrated that administrative reports can bias coverage estimates . A survey of participation in mass drug administration for trachoma control was implemented in three districts in Plateau State , Nigeria in 2011 to validate coverage calculated from treatment registers . A total of 352 households were surveyed from 24 randomly selected communities . Heads of household were interviewed to identify household-level characteristics and predictors of participation . Individual household members were enumerated and those present at the time of interview were asked to report individual participation in the MDA . Responses were verified against the community-drug distributor registration log . Approximately 60% of the sample reported receiving either tetracycline eye ointment or azithromycin for trachoma control . Administrative data on treatment estimated coverage at 76% for the three LGAs . The discrepancy between the coverage estimate from administrative data ( calculated by the program ) and the survey data suggest that cluster random surveys of MDA provide a useful monitoring tool to validate administrative data on treatment coverage . These data support modification of distributor training and MDA delivery to increase coverage in subsequent rounds of MDA .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"global",
"health",
"neglected",
"tropical",
"diseases",
"trachoma",
"public",
"health"
] |
2013
|
Monitoring of Mass Distribution Interventions for Trachoma in Plateau State, Nigeria
|
Environmental stresses increase genetic variation in bacteria , plants , and human cancer cells . The linkage between various environments and mutational outcomes has not been systematically investigated , however . Here , we established the influence of nutritional stresses commonly found in the biosphere ( carbon , phosphate , nitrogen , oxygen , or iron limitation ) on both the rate and spectrum of mutations in Escherichia coli . We found that each limitation was associated with a remarkably distinct mutational profile . Overall mutation rates were not always elevated , and nitrogen , iron , and oxygen limitation resulted in major spectral changes but no net increase in rate . Our results thus suggest that stress-induced mutagenesis is a diverse series of stress input–mutation output linkages that is distinct in every condition . Environment-specific spectra resulted in the differential emergence of traits needing particular mutations in these settings . Mutations requiring transpositions were highest under iron and oxygen limitation , whereas base-pair substitutions and indels were highest under phosphate limitation . The unexpected diversity of input–output effects explains some important phenomena in the mutational biases of evolving genomes . The prevalence of bacterial insertion sequence transpositions in the mammalian gut or in anaerobically stored cultures is due to environmentally determined mutation availability . Likewise , the much-discussed genomic bias towards transition base substitutions in evolving genomes can now be explained as an environment-specific output . Altogether , our conclusion is that environments influence genetic variation as well as selection .
The notion of stress-induced mutagenesis ( SIM ) [1 , 2] has changed our perspective on the flexibility of mutation rates in organisms . The earliest evidence for SIM was that starvation can increase the supply of mutations , presumably increasing the capacity for adaptive changes and evolvability [1 , 3 , 4] . SIM is a collection of mechanisms observed in bacterial , yeast , and human cells , in which mutagenesis pathways are activated in response to adverse conditions , such as starvation or antibiotic stresses [5 , 6] . The detailed systems biology of mutation supply and its link to environmental states is still poorly defined , however . The most basic deficiency in understanding SIM is that neither the inputs nor the outputs of SIM are systematically defined . An analysis of what is meant by “stress-induced” ( the input variation ) and what is meant by “mutagenesis” ( the output of subsets or all mutations ) is essential for it to be clear whether SIM consistently involves the same environment-specific changes to DNA repair and mutation rates and the same mutational spectra with different stresses . An accurate modelling of evolution and detailed analysis of genomic signatures requires this level of information , and our study aims to provide clarity on this point . Mutation availability is of obvious significance in the emergence of antibiotic resistance in bacteria or cancer in humans , as well as the stability of organisms in biotechnology and evolution in general . Both experimental and theoretical indications suggest that increasing the supply of mutations allows populations to overcome adaptive hurdles , such as those presented by antibiotic treatment [7–10] . Antibiotic-induced mutagenesis increases mutation rates and also changes the pattern of mutations [7 , 9] . To understand the full significance of SIM , the breadth of inputs into SIM need to be defined beyond stresses like starvation or antibiotics that are known to impact mutational processes [11] . In total , the majority of environmental and physicochemical stress effects on SIM are poorly understood . It is even uncertain whether the increased mutation rates in aging , starving bacterial colonies ( the main initial evidence for stress-induced mutagenesis [1 , 3 , 4] ) can be extrapolated to physicochemical stresses , generally . As indicated in Fig 1 , there is no current information on the 4 output questions posed in boxes A . through D . on whether mutational processes , total mutation rates , individual mutation rates , and DNA repair respond similarly to distinct stressful environments common in the biosphere or even under standard laboratory culture conditions . What we know about DNA repair regulation in SIM is also limited to starvation and antibiotic effects . The identified effects are on mismatch repair down-regulation by starvation and antibiotics , as well as on the up-regulation of error-prone DNA polymerase by starvation [9 , 12–14] . The magnitude of the increased mutation rates and the magnitude of changes in mutational spectra is a particularly important question with SIM . As shown in Fig 1 , the total mutation rate ( μTOTAL ) [15] includes the 6 possible base-pair substitutions ( BPSs ) , several single base-pair indels ( SI , e . g . , +1 , −1 base-pair [bp] insertion or deletion of different base pairs ) , deletion and insertion indels >1bp ( LI ) , and transpositions ( with 10 different insertion sequence [IS] elements in E . coli K-12 , for example ) . Approximations of the individual mutation rates , as well as amplifications , have been made [16] , but these rely on data from various strains , selection environments , and laboratories . In this study , we devise a systematic analysis of whether the classes of mutation in Fig 1 fluctuate across environments and whether a composite mutation rate can really predict the outcome of evolution in different conditions . Testing the environmental patterns of mutational rates and spectra is difficult because of the same experimental complexities that have bedevilled estimates of SIM [17] . The initial evidence for elevated mutation rates in bacteria under stress is based on estimating rifampicin resistance [1] or lac mutation reversion [3 , 4] in aging or starved colonies . These experiments have 2 problems . The first is that the environment in colonies on agar plates is complex , and the stress shifts over time [18] . The lac reversion assay is effectively carbon starvation [19] , but the effects of other limitations have not been investigated on lac reversion . Secondly , the Lac and Rif assays , involving growth on lactose and rifampicin resistance phenotypes respectively , have been claimed to be compromised by the contribution of fitness and selection to the appearance of mutants on agar plates [16 , 17] . This problem is particularly important for Rif resistance [20] , but the lac results have been reproduced in the absence of selection [21] . To avoid the problems of environmental fluctuation and selection , an analysis of the role of various stresses in bacterial mutagenesis required 2 essential ingredients . The first is the ability to fix environments to reproducibly compare both mutation rates and spectra . This challenge was solved by using bacteria that grow in controlled environments , in continuous ( chemostat ) cultures , at identical growth rates [22] . We controlled 5 resources essential for all forms of life , i . e . , utilizable sources of carbon ( C ) , phosphorus ( P ) , nitrogen ( N ) , iron ( Fe ) , and oxygen ( O ) . The availability of these is irregular in most ecosystems [23 , 24] , including the human body , and bacteria like E . coli respond to these limitations through distinct patterns of gene expression [25] . Steady state limitation of C , O , N , Fe , or P was achieved by limiting the amount of glucose , oxygen , ammonium , iron , and phosphate salts in chemostats . Each of the nutrient-stressed environments fixed the specific growth rate to a constant 0 . 1 h−1 , a 7-fold reduction compared to 0 . 7 h−1 with excess nutrients . At this reduced growth rate , bacteria are highly stressed and exhibit high concentrations of alarmones like guanosine tetraphosphate ( ppGpp ) and transcriptional stress controllers like RpoS [22] . A second requirement was an experimental system in which both the rate and spectrum of mutational types can be assayed without selection bias . Understanding genetic variation needs a comprehensive set of mutational types to be evaluated , beyond analyses that often focus just on BPSs [26 , 27] . Approaches like mutation accumulation ( MA ) experiments [28] are not environmentally controlled and somewhat biased because deleterious mutations in essential sequences are underestimated [29 , 30] . Some individual target gene analyses suffer from the above-mentioned fitness effects [17 , 27] . An example is the use of rifampicin-resistant rpoB mutations to analyse mutational spectra [31]; such studies are skewed by the large fitness differences and selectability of various alleles [32] . However , the use of a locus at a fixed chromosomal position eliminates the problem of variation in mutation rates between different chromosomal sites [33] , especially when compared across environments . The method we adopt is to follow mutations in the cycA locus of E . coli resulting from cycloserine resistance ( CycR ) [30 , 34] , which does not suffer the above problems . CycR is conferred by a wide spectrum of loss-of-function mutations , including all 6 possible types of BPSs , different types of SIs and LIs , and IS transpositions across the entire length of cycA . Mutations in cycA conferring CycR show negligible fitness effects ( [30 , 34] , so the environmental influences on mutations could be characterised without selection bias . The only disadvantage of this in-gene analysis is that stress-associated amplifications and larger chromosomal rearrangements associated with SIM [35] are not measured in this CycA study . By using the above strategies , we demonstrate the remarkable plasticity of mutations in different conditions . We further show that the variation of mutation availability in different environments has detectable impacts on adaptation . The emergence of particular traits dependent on specific classes of mutations were especially affected by major changes in mutation spectrum . Environments thus provide alternative sets of keys to unlock various evolutionary pathways , thus impacting the role of genetic variation in evolution .
Five distinct nutrient limitations controlled in chemostats ( of C , P , N , O , or Fe ) and a nutrient-rich environment were used for investigating the link between nutritional stress and mutagenesis . In appraising mutations resulting in CycR in the 6 environments , the first notable finding in Fig 2A is that μTOTAL ( involving all types of mutations in cycA conferring CycR ) is nonuniform across these conditions . The net mutation rate in nutrient-rich , rapid-growth conditions is low ( μTOTAL = 0 . 73 ± 0 . 22 SD x 10−7 per locus per generation ) , similar to the value ( 0 . 65 x 10−7 per locus per generation ) reported with batch cultures [34] . More surprisingly , the nutrient-rich mutation rate cannot be statistically distinguished from that under Fe , O , and N limitation ( 2-tailed t test , P > 0 . 05 ) . It is remarkable that the 7-fold decreased growth rate under limiting Fe , O , and N does not result in a net SIM , i . e . , elevated mutation rates due to “starvation” [1 , 3 , 4] . C and P limitation do induce SIM , and mutagenicity was highest with P limitation; C limitation was also higher than other conditions ( μTOTAL = 5 . 6 ± 0 . 65 SD and 3 . 2 ± 0 . 29 SD x 10−7 per locus per generation , respectively; 2-tailed t test , P < 0 . 001 in both cases ) . The observed 9- and 4-fold increases in P and C limitation respectively are notable because a 4-fold change in mutation rates can change evolutionary outcomes [36] . The μTOTAL spread we find in CycR matches the range of mutation rates measured in several other ways with E . coli ( see Table 1 for a detailed comparison ) . Differences in nutrition and environments may indeed explain the 10-fold span reported in these other environmentally less-controlled studies . In relation to the SIM discussions [1 , 3 , 4] , our data suggests that plate-starved colonies increased mutation rates because of C or P limitation or both . Most importantly though , our observations suggest that not all nutritional stresses increase net rates; the nature of the starvation is more important than the generally decreased growth rate . The observed higher mutation rate in C and P limitations was not due to increased fitness and enrichment of CycR mutants . As shown in Fig 3A , all 4 major classes of mutation-conferring CycR cause negligible fitness effects in all 6 environments , so these mutations were unlikely to be significantly enriched or eliminated in our experiments . The mutation rates were followed by sampling over only 72 h ( see Methods ) , further limiting fitness effects on mutational frequencies and avoiding the potential distortion of population structures by later sweeps and hitchhiking in chemostats [40] . To investigate whether nutritional SIM is associated with a uniform mutational spectrum in a series of distinct stresses , we sequenced the entire length of cycA in 1399 CycR mutants arising after 72 h in the 6 conditions ( see supplementary S1 Table for all mutants ) . Sibs were excluded by including only 1 example of each mutation from each culture . All the mutations were single mutations in cycA . The 4 major classes of mutation ( BPS , SI , LI , and IS ) are distributed throughout the cycA gene , and no particular BPS or SI hotpots were evident in the linear map depicting all detected mutations in cycA in the 6 nutritional states ( Fig 3B ) . Many LIs were more localised due to repeat sequences in cycA shown in Fig 3C , but their frequency could still be environmentally compared . We then estimated mutation rates independently for the BPS , SI , LI , and IS classes in all 6 conditions . The mutation rates for each type of mutation were estimated from their frequency in PCR-sequenced CycR colonies , and the overall mutation rates for the cycA gene in each environment . The individual rates for each class in each environment are shown in Fig 3D . When rates for all classes were stacked , it became clear that μTOTAL based on sequencing ( Fig 2B ) is consistent with μTOTAL based on CycR ( Fig 2A ) . The contribution of each mutational class to the μTOTAL is , however , highly nutrition-specific ( Fig 2C ) . Prominent differences include the IS mutation rates ( μIS ) , which are 8- and 6-fold higher in Fe and O limitations , respectively , than the μIS = 1 . 5 ± 0 . 51 SD x 10−8 per locus per generation in nutrient-rich conditions ( Fig 3D , 2-tail , P < 0 . 01 ) . Remarkably , the μIS differences occur between conditions that show indistinguishable μTOTAL rates ( Fig 2A , 2-tailed t test P > 0 . 1 ) . Another major difference was the μBPS = 20 . 4 ± 5 . 5 SD x 10−8 per locus per generation in P limitation , which was 10-fold higher than in the nutrient-rich condition ( 2-tail t test P = 0 . 0003 ) . An even greater divergence was seen with single base-pair indel mutation rates ( μSI ) ; μSI in P limitation ( 13 . 4 ± 5 . 58 SD x 10−8 per locus per generation ) was more than 150-fold higher compared to μSI in O limitation ( 2-tail t test , P = 0 . 002 ) . The LI rate ( μLI ) also varied markedly in the 6 conditions ( Fig 3D ) . As shown in Fig 4 , nutritional effects on spectra are even more evident within each BPS , SI , LI , and IS class . Notably , 3 limitations ( of O , N and Fe ) that cause limited increases in BPS rates in Fig 4 contained a >10-fold variation in rates of all 6 individual BPSs ( Fig 4A ) . P limitation showed the highest cumulative BPS mutation rate ( μBPS ) , and this was mainly due to GC→TA transversions and GC→AT transitions . Mutation rates for these substitutions were 10- to 13-fold higher than in nutrient-rich conditions ( 2-tailed t test , P < 0 . 001 in both cases ) . C limitation also resulted in a high BPS rate but mainly due to a GC→AT transition . We even found base changes that became extremely rare in particular environments; for example , GC→CG and AT→TA changes were below our detection limit in all 6 parallel cultures under O and C limitation , respectively . An entirely unanticipated consequence of the shifts in BPS patterns is a uniquely different transition to transversion ( Ti/Tv ) ratio in 1 environment ( Fig 4A ) . The Ti/Tv ratio is 1 . 9 in C limitation only but is closer to the random value of 0 . 5 with other limitations ( Fig 4A ) . The strikingly high ratio in the C-limited environment resembles that commonly found between diverging genomes [41] . We found that 6 different IS elements conferred CycR ( IS1 , IS2 , IS3 , IS4 , IS5 , and IS150 ) in cycA , but the elevated μIS under O , C , and Fe limitations was mainly due to IS150 movement ( Fig 4B and 4E ) . A remarkable consequence is that IS movements actually constitute the majority of mutations in O and Fe limitation ( Fig 2B , Fig 2C ) . Another responsive IS movement was with IS2 , which increased in O limitation compared to the unlimited condition ( 2-tail t test , P < 0 . 01 ) . Our overall transposition rate ( extrapolated by converting our μIS per locus rate to per genome rate; μIS = 4 . 9 ± 1 . 64 SD x 10−5 per genome per generation in the nutrient-rich environment ) is 3-fold higher than the estimate of 1 . 5 x 10−5 per genome per generation in a mutation accumulation study [42] . The difference may be due to the deleterious effect of IS transpositions in MA experiments [29] or locus-specific bias in cycA . Aside from IS movements , other types of transposition events are subject to stress regulators [43] , but we have no measure of these in the cycA experiments . The single base-pair indel rates ( μSI ) in P limitation were 40- to 150-fold elevated compared to nutrient-rich and O-limited environments due to 15- to 56-fold higher rates of all 4 types of SI ( Fig 4C , 2-tail t test , P < 0 . 001 ) . Likewise , significantly higher rates of 3 types of SI in C limitation ( 2-tail t test , P < 0 . 05 ) and 2 types of SI in N limitation ( 2-tail t-test , P < 0 . 05 ) differ from the less-extensive change in the 4 SI types among the nutrient-rich , O , and Fe limitation states ( 2-tail t test , P > 0 . 05 ) . The 4 LI events in Fig 4D were also nonuniform . A deletion of 12 bp at base positions 96–108 of the cycA gene ( LDR1 ) was present in some mutants , while a deletion of 18 bp ( LDR2 ) and an insertion of 18 bp ( LIR2 ) in the 918–948 region of cycA ( Fig 3C ) was present in others . The total LIR2 rate was surprisingly prominent in the nutrient-rich condition ( Fig 4E ) , which made LIs a prominent component of the mutation spectrum in nutrient-unlimited growth ( Fig 2B and 2C ) . In contrast , deletion formation was especially elevated under P limitation . These extensive spectrum findings in Fig 4 provide a new insight that nutritional states have a more general impact on stress-specific changes to DNA repair or mutagenesis than on overall μTOTAL mutation rates . The relationships between individual mutation rates and environments are summarized in a mutational landscape ( Fig 5A ) . This highlights the considerable mutational pattern differences between environments . There is nevertheless a dichotomy in rate patterns between the nutrient-unlimited ( Un ) , Fe- , and O-limited cultures and the more mutagenic P- and C-limited environments . This dichotomy is reinforced by the unweighted pair-group method with arithmetic mean ( UPGMA ) clustering in Fig 5B between sets of limitations , suggesting some environments result in more related mutagenic effects . Explaining these relationships is difficult , however . Physiologically , O and Fe limitation may show similarities if a reduction of Fe-initiated O radical damage is a cause of the pattern changes . On the other hand , the physiological relationship of O and Fe to N limitation is less obvious . The close clustering of the C and P limitations is also difficult to explain through their divergent patterns of gene expression [25] . One clue to the complexity of the spectra is the unexpectedly intricate pattern of DNA repair system expression in each nutritional state ( Fig 6 ) . We quantitated a component of the ubiquitous mismatch repair system ( MutS [9] ) , the SOS system ( DinB involved in error-prone repair [19 , 45] ) , and MutY expression ( involved in O-damage repair in E . coli [46] ) . The mismatch repair ( MMR ) and SOS systems and expression of mutY are not coregulated by nutritional factors , but all vary ( Fig 6 ) . There is no clear relation between repair levels and that of a global controller , RpoS ( Fig 6E ) , known to affect both MutS and DinB [9 , 19] . Elevated RpoS in all limitations relative to unlimited conditions shown in Fig 6 should increase double-strand breaks according to [47] , but we do not find correspondingly increased net mutation rates in some of the environments . Other inputs besides RpoS must control mutation processes . Another repair component , UvrD , was recently linked to stress [48] and is also likely to be subject to nutritional variation . The regulatory plasticity of repair systems may thus contribute to the basis of the complex shifts in mutation patterns seen in Fig 4 . An indication of this is with the most mutagenic environment ( P ) , which has the highest error-prone polymerase , equal lowest MutS , and reduced MutY repair . Of course , other influences on mutational spectra may contribute; the chromosomal structure and its superhelicity , subdomains , and DNA-binding proteins also changes in various environments [49] . Further analysis is needed to explain how environments impact on mutational spectra . There are suggestions that chromosomal position can influence rates of mutation [33] and other data suggests local context rather than location is important [51] . Our data in Figs 2–5 are based on the spectrum of CycR mutations in a single gene at position 95 . 44 min on the E . coli chromosome . Hence , it was desirable to test whether the spectral changes under the various stresses are similar at other chromosomal sites . We thus additionally followed , in the same 6 environments , the occurrence of 3 types of mutation at 3 other sites . These were in lacZ at 7 . 83 min ( for all 6 types of BPS ) , bgl at 65 . 57 min ( for IS movements ) , and araD at 1 . 42 min ( for a single base indel ) . The data obtained are shown in Figs 7 and 8 . The analysis of lac BPS changes used the 6 strains developed by Cupples and Miller , each of which require a particular base change to revert to a Lac+ phenotype [52] . The 6 strains were each grown in the same 6 environments , and Lac+ isolates were enumerated after 72 h of chemostat culture , as for cycA . The data are resented in Fig 7 . The reversion rates for each BPS were characteristic for each limitation , and AT→CG , GC→AT changes were strongly environment-dependent . For example , mutation rates for AT→CG and GC→AT are 9- and 3-fold higher in P limitation than in the nutrient-rich condition . This pattern was as observed for the cycA changes in Figs 3 and 4 . As shown in Fig 8A , the sum of all 6 BPS rate changes in each environment was also environment-dependent and remarkably similar to the cycA pattern in Fig 3D . The individual BPS changes in lacZ also resulted in a similar Ti/Tv pattern as shown for cycA in Fig 4A , with glucose limitation having the highest Ti/Tv ratio . Comparing the cycA and lacZ results , it appears the stress-dependent BPS spectrum changes were both gene- and assay-independent . A second test of the generality of environmental effects was on the differences in IS mutation rates in the 6 environments ( Fig 4C ) . We used the mutational acquisition of the ability to use β-glucosides like salicin in E . coli , because this specifically requires IS transposition ( several IS elements can be involved ) near the bgl genes [53] . The expectation was that we see elevated IS movement in Fe and O limitation but not in P limitation , as with cycA ( Fig 3D ) . Indeed , Sal+ mutations were commonest in O and Fe limitation and lowest in P limitation ( Fig 8B , 2-tailed P < 0 . 001 ) . There was a 10-fold range of bgl mutation frequencies in the 6 states . The Sal+ environment profiles match the IS spectra obtained from sequencing of cycA , except for C limitation ( Fig 3D ) . The third prediction was that large environmental effects on a +1 bp SI mutation in cycA ( Fig 4C ) should result in a commensurate rate of mutation at other loci . We tested the reversion to growth on arabinose in a mutant [54] with a 1 bp deletion in araD as an assay for the prevalence of a +1BP insertion . The reversion rates to Ara+ were measured in the 6 environments . As shown in Fig 8C , Ara+ reversion was also environment-sensitive . There was a >100-fold range of araD reversion rates . The strikingly clear result was that the frameshift change in araD was most prominent in P limitation but highly reduced during O limitation ( Fig 8C , 2-tailed P < 0 . 001 ) , altogether matching the SI pattern in Fig 3D . Sequencing of araD and PCR analysis for the bgl IS movement demonstrated that the mutational types were indeed a +1 frameshift and IS insertion , respectively . In order to check whether the Lac+ , Ara+ , and Sal+ differences in Fig 8 were due to fitness advantages of mutants in the chemostats , the competitive fitness of Lac+/- , Ara+/- , and Sal+/- mutations in each environment was tested to see if the mutations were differentially enriched in particular settings . The Lac+ , Ara+ and Sal+ mutations were each near-neutral and the fitness of mutants is not a major difference in the 6 environments ( Fig 8D and 8E ) . In the absence of fitness effects , mutation availability is thus the likely explanation for the uneven pattern of Lac+ , Ara+ , and Sal+ mutations across environments in Fig 8A–8C . The important consequence of these findings with the 3 sugars in the 6 environments is that the traits dependent on particular types of mutations are likely to emerge at different rates in different situations; this is also likely to apply to any adaptation that relies on specific types of mutation .
This analysis of nutritional states on mutation rates and spectra under stringently controlled conditions has produced results that challenge established assumptions on SIM and the randomness of mutations in genetic variation . The major surprise is the very distinct mutational mixes under 5 different forms of nutrient limitation at the same growth rate . This data suggests that mutational spectra are highly susceptible to the environment . The detected plasticity of all mutational types greatly extends mutation spectrum observations with individual stresses in various organisms [7 , 9 , 30 , 55] . Here we dealt with nutrient stress , but a systematic analysis of spectra with other physicochemical stresses such as pH and salinity at fixed growth rates are also called for to expand our understanding of how environments fix the scope of mutational genetic variation . A new study reinforces that the mix of mutations also changes with temperature [56] , but the spectral changes were not presented in detail . Environmental effects on genetic variation are thus likely to be a general phenomenon . Changes in total mutation rates with stress are long-argued but lacked a systematic analysis [1 , 3 , 4] . Our results indicate that nutritional stresses ( and perhaps other environmental stresses ) do not uniformly increase total mutation rates; only 2 of the 5 conditions tested significantly increased rates . One of these 2 conditions ( carbon limitation ) is likely to be a factor in the plate starvation assays used by others [1 , 3 , 4] , so provide conditions where higher mutation rates are measurable . Altogether , our results point to the importance of fixing environments in studying mutational processes because variations to Fe , P , or aeration levels can occur at high cell densities in rich media , and this could bias mutation profiles . In addition , the results with C and P limitation , as well as studies showing how regulatory mutations change mutational processes [19 , 50 , 57 , 58] argue that stress effects on mutations are real , although very sensitive to environments . Another surprise was that P limitation was the most mutagenic , giving a 9-fold higher net mutation rate and the lowest level of DNA repair . It should also be pointed out that our results exclude selection effects on the cycA , lacZ , bgl , and araD mutations analysed in these experiments , eliminating one of the major problems present in earlier studies [16 , 17] . The generality of the spectral changes in our cycA study is vindicated by the changes also seen in the lacZ , araD , and bgl genes ( Figs 7 and 8 ) . Biased base-pair changes , single-base SIs , and IS movements are a general feature of different forms of nutrient limitation in the 4 genes . Furthermore , a new study published since submission has determined mutational spectra under anaerobiosis [59] . This too found the IS150 difference to be the biggest single pattern change , as in Fig 4B . The small increase in total mutation rates ( 1 . 6-fold ) in anaerobic mutation accumulation experiments [59] is also consistent with our data in Fig 2A . So we are confident of the generality of our findings within E . coli that environments have a strong impact on mutational spectra . Our data provide an entirely new perspective of SIM . We find that all tested nutrient stresses impact on mutational spectra , and the availability of particular types of mutations . Likewise , all individual mutation types are stress-affected . The established description of SIM , as a process up-regulated by a stress response [5] , covers only a part of an unexpectedly complex environment–mutation landscape . Stress , resulting in major decreases in growth rates , does not always equally increase mutation rates . We find novel situations in which stress regulators down-regulate particular mutational types and up-regulate others , but net mutation rates remain unchanged . Actually , the only universal mutational effect of suboptimal environments is that mutational spectra are shifted in all of the situations we studied . It is interesting to speculate how the environmental influences may impact the evolution of bacteria , especially inside the mammalian habitat of E . coli . From our results , the implications for bacterial evolution are that acquisition of new traits needing IS transposition is much likelier in anaerobic ( colon ) or Fe-limited ( body secretions ) than P-limited situations . There is published evidence for this bias , because the majority of first-step bacterial adaptations in the anaerobic gut of mice are due to IS transpositions [60] . Another example of an anaerobic environment with unusually high rates of IS movement is in long-term stab cultures [61] , and a new study has shown that transposition rates are also altered by medium agar concentration [62] . Another example of mutational bias likely due to a P-limited environment is in lungs [63] . SIs are most likely to occur in P-limited environments in our study . Indeed , there is evidence that SIs provide the majority of parallel adaptations in Pseudomonas aeruginosa in 474 longitudinal lung isolates [64] . Genome-wide analysis of these isolates also showed a much lower BPS/indel ratio ( 3 . 4 ) than observed in bacteria generally ( 19 . 3; [65] ) . The BPS/SI ratio with P limitation in Fig 4 was correspondingly low ( 1 . 6 ) , whereas the highest ratio ( 32 ) was with O limitation . The C-limited state is prevalent in the competitive intestinal environment of E . coli , and it is frequent in many parts of the biosphere . It is tempting to predict that the BPS bias to transitions we find to be prevalent under C limitation provides a plausible cause of the transition bias arising in evolving E . coli and other genomes [66] . Still , natural environments are often not single-nutrient limited so we cannot predict how more complex environments ( e . g . , involving both C and O limitation ) affect mutation rates and spectra . Nevertheless , environmental causes may well contribute to the general transition bias in organisms [41] , as shown in Fig 4A and Fig 7 . We do not have a detailed mechanistic explanation for shifts in spectra but the extensive environmental-mutational plasticity we find is associated with the equally remarkable patterns of DNA repair . The sheer complexity of the changes means that we cannot identify particular gene-regulatory networks as being impacted by particular nutritional states . RpoS and other networks associated with DNA repair may contribute [19 , 50 , 57 , 58] , but there is no direct link between RpoS levels in Fig 6 and particular mutation patterns . It will need a comprehensive analysis of all repair functions to disentangle the spectrum differences . In relation to evolutionary theory , these findings profoundly change our understanding of genetic variation in evolution . This is because spectral shifts in mutation availability occur even when net mutation rates do not . Since not all mutational types are able to initiate all fitness paths [67] , the spectrum differences may well result in evolvability effects . The novel implication is that evolution provides alternative adaptive pathways , as proposed for evolutionary divergence [67] . Our inference is that models of evolution need to modify the historical concept of “mutation rate” as a parameter in evolution . Mutational heterogeneity as much as the net mutation rate may well influence evolvability; this notion is inconsistent with Neo-Darwinian assumptions that mutational effects have a minor role in evolution [68] . Especially relevant are situations in which the acquisition of new characters requires specific mutations rather than any random variation . For example , the spectra of IS and SI mutations in bgl and ara genes ( Fig 8 ) indicates that IS and SI mutations are very limited in some environments but not in others . Altogether , our results support the view that the availability of mutations can be a factor in evolution and may drive the acquisition of new characteristics through supplying limiting mutations , called constraint-breaking mutations in recent models [68 , 69] . Another fundamental question arising from the mutation rate and evolvability differences is whether the mutation biases are random with respect to adaptation . Based on our results , we do not see a clear link between increased individual mutation types and their benefits in those same environments , as has also been shown for starvation-induced mutagenesis [70] . For example , SIs are not obviously beneficial under P limitation in E . coli , the environment in which they are so prevalent . Although SIs may be mostly deleterious , the 10-fold increase in BPSs under P limitation may potentiate alternative beneficial changes . The transition bias in C limitation does not offer clear benefits , nor does the strongly elevated IS transposition under anaerobic conditions . Nevertheless , some of many notions of mutational “chance” or “randomness” in evolution [71] are impacted by our findings . For now , our conclusion is that environmental biases do not provide directed mutations . Finally , the implication of our results extends to all those important domains of biology and medicine where mutation availability and mutation bias have recognised roles . These include situations where particular mutations empower evolutionary divergence through different mutations [67 , 72] , the emergence of mutationally acquired antibiotic resistance in bacteria [73] , such as in the evolution of β-lactamase active sites [74] , or the spectrum of mutations causing cancer in humans [75] .
The E . coli K12 strain MC4100 [54] , which is CycS and salicin-negative ( Sal− ) and has an araD139 ( Ara- ) mutation , was used in this study . We also used a set of 6 E . coli strains CC101-106 [52] . Each CC strain is Lac− and contains a different point mutation in lacZ affecting residue 416 in β-galactosidase . The mutations necessary for Lac+ reversion are AT→GC ( CC106 ) , GC→AT ( CC102 ) , GC→TA ( CC104 ) , GC→CG ( CC103 ) , AT→TA ( CC105 ) , and AT→CG ( CC101 ) . “Unlimited” media used for exponential batch culture was the minimal medium previously described [76] , supplemented with 0 . 2% glucose . In all chemostat cultures , the dilution rate was set at 0 . 1 h−1 [76] . We used the following media for 80 ml chemostat cultures , resulting in 5 different nutritional states . In the glucose-limited ( C-limited ) medium , the minimal medium was supplemented with 0 . 02% glucose . For O-limited chemostats , the airflow through the sparger was blocked to create an oxygen-depleted environment and supplemented with 0 . 08% glucose as described previously [77] . An Fe-depleted environment was obtained by removing sodium citrate and Fe2SO4 from the medium as described in [76] and supplemented with 0 . 04% glucose . In addition , contaminating iron was removed from all glassware and other chemostat equipment by soaking for 24 h in 2% hydrochloric acid followed by rinsing with MilliQ water . The P-limited medium contained 0 . 2% glucose and was as previously described [78] . The N-limited medium was supplemented with 0 . 2% glucose and contained the same basal medium as the P-limited culture but with ( NH4 ) 2SO4 reduced to 0 . 03 g/l and 1 mM K2HPO4 . The higher glucose addition in some media was to ensure glucose excess in all conditions except under carbon limitation . Anaerobic , phosphate- , nitrogen- , and iron-limiting conditions result in less respiration and more fermentative metabolism , which consumes more glucose , hence the need for more glucose in the media to avoid a double limitation . Acquisition of resistance to cycloserine from cycloserine-sensitive populations of MC4100 ( CycS→CycR assay ) was used to select mutations in cycA . The cycloserine-sensitive E . coli was grown for 72 h in chemostats at a growth rate ( dilution rate ) of 0 . 1 h−1 in different nutrient-limited chemostats before plating culture samples on cycloserine plates for detection of CycR mutants . The cycloserine plates consisted of 0 . 2% wt/vol glucose , 40 μM cycloserine ( Sigma-Aldrich ) , and 1 . 5% wt/vol agar in minimal medium A . For the Lac+ reversion assay , bacteria containing mutant lacZ genes were grown in the same growth conditions as used for the detection of CycR for 72 h in chemostats . The Lac+ reversion assay was performed by mixing 1 ml of concentrated cultures containing 1 . 3–2 . 3 x 1010 cells with 8 ml of molten ( 45°C ) MMA-top agar ( 0 . 7% agar in MMA ) in a 50-ml tube . The contents were then overlaid onto 3 separate lactose MMA agar plates , which contain 1 . 5% wt/vol lactose as a sole carbon source . The total number of Lac+ colonies in each sample was counted after 48 h incubation at 37°C . We used the reversion of the arabinose-negative MC4100 ( with a frameshift mutation in araD ) to arabinose positive ( Ara− → Ara+ ) to detect a 1 bp indel in araD by plating cultures on MMA agar plates containing arabinose as the sole carbon source . For the IS transposition mutation rates , we used salicin minimal agar plates; growth on salicin requires a transposition of an IS element as described previously [53] . Arabinose and salicin plates were based on MMA and contained 1% wt/vol arabinose and 1% wt/vol salicin respectively . We used the Luria-Delbruck fluctuation test [79] for measuring mutation rates in nutrient-unlimited batch cultures but this method cannot be applied to continuous cultures . To determine mutation rates in chemostats , we adopted a method routinely used for continuous cultures [80] as described in detail below . For the Luria-Delbruck fluctuation tests [79] , a single colony of wild-type MC4100 was inoculated in 5 ml MMA medium , supplemented with 0 . 2% wt/vol glucose and allowed to propagate overnight at 37°C with shaking at 200 rpm . The overnight cultures were diluted in 5 ml fresh MMA medium , supplemented with 0 . 2% wt/vol glucose and allowed to grow to optical density at 600 nm to 0 . 6 . The exponentially growing cultures were then diluted 10 , 000-fold into 100 ml in MMA medium supplemented with 0 . 2% wt/vol glucose and divided equally into 20 separate McCartney bottles , each receiving 5 ml . The freshly diluted cultures were then incubated at 37°C overnight with shaking at 200 rpm . Culture samples were then plated separately on cycloserine , arabinose , and salicin plates . The plates were then incubated for 24 h at 37°C to detect CycR mutant colonies and 48 h for detection of Lac+ , Ara+ , and Bgl+ mutants , respectively . For total colony forming unit counts , aliquots of appropriately diluted cultures were plated on LB-agar plates . The mutation rates were then estimated from the number of resistant colonies per culture and total cell count by using the Ma-Sandri-Sarkar maximum likelihood ( MSS-MLL ) analysis [79] . For mutation rates in chemostats , a single colony of MC4100 was inoculated in 5 ml of the above 5 nutrient-limited minimal media for 6 h; 1 ml of this starter culture was used as inoculum for chemostats . Culture samples were taken at 2 time points , at time 0 h ( i . e . , when cultures reached a steady OD600 ( 0 . 2 to 0 . 3 , equivalent to 1 . 3–2 . 3 x 1010 bacteria in 80 ml depending on the chemostat composition ) and 1 sample at 72 h at the same maintained density . Aliquots were then plated or overlaid separately on the cycloserine , lactose , arabinose , and salicin plates described above as well as for total counts on LB agar . Mutation rates were calculated by using the following equation [80]: μ = [1 ( r2 –r1 ) ]/[Nλ ( t2—t1 ) ] , where , r1 and r2 are the number of mutants detected at times t1 and t2 respectively; N is total cell number , which remains constant in a chemostat but was determined in each sample; λ is dilution rate , which was set at 0 . 1 h−1 in these experiments . By adjusting population sizes to account for mutation rate differences , we aimed to obtain a mean of 100 colonies per plate for counting . The levels of RpoD , RpoS , DinB , and MutS proteins in chemostats and exponentially growing cultures of E . coli were determined by using the western blot protocol described previously [50] . The expression level of mutY was analysed in the 6 different nutritional conditions as described for the E . coli MC4100 derivative strain BW3500 containing a mutY-lacZ transcriptional fusion [46] . The mutation spectrum in cycA in CycR mutants obtained in the nutritional states was determined by sequencing the entire length of a PCR product of cycA in CycR mutants . A total of 1 , 399 CycR mutants isolated from 6 replicate cultures from each condition ( in total 249 from Fe-limited , 228 from Un-limited , 234 from O-limited , 240 from N-limited , 203 from C-limited , and 245 from P-limited cultures ) were randomly chosen for PCR sequencing as described in [30] . The identified mutations were categorized into BPSs , SIs , LIs , and transpositions involving one of several ( IS ) elements . Rates for each class and each individual type of mutation were estimated from their frequency amongst the sequenced CycR mutants and the overall mutation rate for the cycA gene based on the fluctuation tests and chemostat methods described above . We excluded only 1 culture from the analysis ( Fe-limited ) in which a jackpot event resulted in 75% of mutations of the same sequence . The fitness of CycR , Lac+ , Ara+ , and Bgl+ mutants relative ancestral MC4100 in the same 6 conditions in which they were isolated was analysed as described previously in [40] . Fitness of CycR , Lac+ , Ara+ , and Bgl+ in the nutrient-unlimited condition was obtained by comparing the exponential growth rate of mutants with that of wild-type E . coli . The tree showing the relationship among mutational spectra from the 6 different environments was based on the PAST software package [44] . The upper and lower limits with 95% confidence interval of mutation rates for CycS>CycR , Ara−>Ara+ , and Bgl->Bgl+ were determined by the FALCOR web tool [79] . Student t test was performed using Microsoft Excel . In all t tests , we used the 2-tailed test , assuming 2-sample unequal variance . SDs among replicates are shown in the text and were also calculated using Microsoft Excel . The SD was based on 20 replicate cultures for the Luria-Delbruck experiments and at least 6 replicate chemostats in each environment .
|
The importance of this study is in advancing our understanding of mutation supply—of the frequency that beneficial mutations arise in a population—in evolution and the contribution of stress-induced mutagenesis to this process . Evolutionary divergences , the emergence of antibiotic resistance in bacteria , and cancer in humans are all examples of processes powered by mutational DNA change . We show for the first time that stress conditions common in nature strongly influence the pattern of genetic variation and consequently the evolvability of traits dependent on particular mutations . More specifically , we show the nutrition-specific links between net mutation rate and type of stress and the significant plasticity of genetic variation in 6 environments . We reveal the nutrition-specific sensitivity of DNA repair and , most importantly , the demonstration that environmental spectrum differences impact evolution through constraints on mutations appearing at altered rates .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"mutagenesis",
"mutation",
"substitution",
"mutation",
"deletion",
"mutation",
"genomics",
"point",
"mutation",
"microbial",
"mutation",
"genome",
"evolution",
"insertion",
"mutation",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"evolution",
"microbiology",
"computational",
"biology",
"evolutionary",
"biology"
] |
2017
|
A shifting mutational landscape in 6 nutritional states: Stress-induced mutagenesis as a series of distinct stress input–mutation output relationships
|
HIV-1 is extremely specialized since , even amongst CD4+ T lymphocytes ( its major natural reservoir in peripheral blood ) , the virus productively infects only a small proportion of cells under an activated state . As the percentage of HIV-1-infected cells is very low , most studies have so far failed to capture the precise transcriptomic profile at the whole-genome scale of cells highly susceptible to virus infection . Using Affymetrix Exon array technology and a reporter virus allowing the magnetic isolation of HIV-1-infected cells , we describe the host cell factors most favorable for virus establishment and replication along with an overview of virus-induced changes in host gene expression occurring exclusively in target cells productively infected with HIV-1 . We also establish that within a population of activated CD4+ T cells , HIV-1 has no detectable effect on the transcriptome of uninfected bystander cells at early time points following infection . The data gathered in this study provides unique insights into the biology of HIV-1-infected CD4+ T cells and identifies genes thought to play a determinant role in the interplay between the virus and its host . Furthermore , it provides the first catalogue of alternative splicing events found in primary human CD4+ T cells productively infected with HIV-1 .
CD4+ T cells – the primary cellular target of HIV-1 – are progressively depleted over the course of infection . This long-term process culminates in the onset of AIDS , a condition in which the immune system is too weak to efficiently mount an effective defence against opportunistic pathogens . Yet , HIV-1 uses only 15 proteins to disable the natural immune defences and harness the host cell machinery to complete its replicative cycle . To do so , viral proteins interact with multiple cellular proteins , perturbing the normal flow of cellular processes . Moreover , the virus influence extends beyond the cells it infects . Indeed , the apoptosis rate of uninfected bystander CD4+ T cells is elevated in individuals carrying HIV-1 [1] . The dichotomy between uninfected bystander and HIV-1-infected CD4+ T cells is an important topic to study , as a deeper understanding of HIV-1 pathogenesis mechanisms might lead to new therapeutic approaches . Powerful technologies developed in recent years have provided high-throughput tools to study cellular dynamics . Among them , microarrays allow for the quantification of expression levels of thousands of genes at once . Since the inception of this technology , few studies have used microarrays to characterize the effect of HIV-1 on various cell types that compose the immune system . However , productive infection rates in primary human cells such as CD4+ T lymphocytes are very low . As microarray technology captures the average transcriptomic profile of a cell population , achieving a high level of purification of subpopulations of interest is crucial to accurately quantify any possible virus-mediated changes in the host transcriptome [2] . We recently developed a new reporter virus system that allows the efficient separation of HIV-1-infected cells from their uninfected bystander counterpart in vitro [3] . In the current work , we used this unique and versatile tool to define the HIV-1-induced modulation of host gene expression in one of its most worrisome cellular reservoir . We hereby provide the first time-course comparative and comprehensive study of the effect of HIV-1 in productively infected versus uninfected bystander primary human CD4+ T cells using Affymetrix Human Exon arrays . We pinpoint understudied genes which seemingly play important roles in the subset of CD4+ T cells preferentially infected by HIV-1 and provide an overview of splicing events found in this subpopulation .
We next performed a comparative microarray analysis of mock-infected , uninfected bystander ( HSA− ) and HIV-1-infected primary human CD4+ T cells ( HSA+ ) in an attempt to identify both virus-induced genes and intracellular environment most permissive to productive infection . To this end we magnetically separated virus-infected cells from three different donors on the basis of HSA expression from the whole cell population exposed to the R5-tropic reporter virus for 24 , 48 or 72 h ( Figure 1C ) . We next confirmed productive HIV-1 infection in isolated cell fractions using a spliced Tat-specific qRT-PCR based on the idea that such a sensitive technique allows the quantification of early expressed viral transcripts without detection of input viral RNA . Data shows a strong signal in the fractions containing HSA-expressing cells ( called HIV Pos ) , while uninfected bystander cell fractions ( called HIV Neg ) and mock-infected ( called Mock ) displayed an almost complete absence of spliced Tat , thus indicating a very high degree of cell purification ( Figure 1D ) . Thereafter , we extracted total RNA from the studied cell fractions and performed transcriptome profiling using Affymetrix Human Exon arrays , which interrogate over 5 million probes spanning all exons in the human genome . A careful analysis of differentially expressed genes ( DEGs ) using a false discovery rate of 1% and a cut-off of 1 . 7 fold showed no effect of HIV-1 on the transcriptome of uninfected bystander cell population compared with mock-infected cells at any studied time point or in aggregate ( Figure 2A , left panels ) . Comparison between mock-infected and HIV-1-infected cells revealed 287 , 236 and 176 DEGs at 24 , 48 and 72 h post-infection , respectively ( Figure 2A , middle panels ) while the aggregate comparison of all time points yielded 289 genes . Given that no DEG was identified in the uninfected bystander cell population , we then compared cells productively infected with HIV-1 to uninfected bystander cells to improve our statistical power , the rationale being that the separation procedure allows to isolate the small percentage of virus-infected cells by removing them from the uninfected bystander cell fraction ( constituting the majority of cells ) , creating a better signal differential than the one obtained comparing with the mock-infected fraction . By doing so , we obtained 502 , 366 and 323 DEGs ( at 24 , 48 and 72 h ) in HIV-1-infected cells while the aggregate comparison yielded 464 DEGs ( Figure 2A , right panels ) . Proportional Venn diagrams depict the relative distribution of DEGs and their evolution through time for these two comparisons ( Figure 2B ) . An overview of all modulated genes is presented in Figure 2C as a hierarchical cluster . It can be concluded that most DEGs identified in HIV-1-infected target cells ( Figure 2C ) are stable at least during the studied period , whereas expression of a small cluster of genes increases significantly over time . Complete lists of DEGs that were found to be modulated are depicted in Dataset S1 . We broadly defined the characteristics of the dataset using the metadata clustering engine DAVID , which identifies enriched biological themes within a list of genes using various biological annotation sources [7] . Using the combined list of 835 DEGs identified in all comparisons performed , we found the following statistically over-represented categories: immune system process , cytokine-receptor interaction , regulation of leukocyte activation , Map kinase phosphatases , FOS/JUN related genes , positive/negative regulation of apoptosis and p53 pathways ( Figure 3 ) . The first three identified categories overlap significantly and contain markers for Th1 ( i . e . IFN-γ , TNF-α , TNF-β , IL-1A , IL-3 , and TBX21/T-bet ) , Th2 ( i . e . IL-4 , IL-5 , IL-10 , and IL-13 ) and Th17 profiles ( i . e . IL-17A , IL-17F , IL-21 , IL-22 , IL-23R , and IRF4 ) , all of which are over-expressed in primary human CD4+ T cells productively infected with HIV-1 ( Figure 3 , top left ) . The expression patterns associated with these different profiles suggest that Th1 and Th17 cells are slightly more susceptible to infection with the studied R5-tropic reporter virus , as virus-infected cells express more IFN-γ and IL-17A than other cytokines ( Figure S1 ) . Other molecules associated with the effector phenotype and activated T cells in general are also differentially expressed ( i . e . IL4R , IL18R , MCSF , GMCSF , ICAM-1 , OX40 , OX40L , CD27 , and its counterpart CD70 , CD80 , CTLA4 , CD69 , CD40LG , IL2RA , FASLG , IL12RB2 , IL18R1 , IL18RAP , and IL-9 ) . Their expression pattern is consistent with highly activated TEM cells being preferentially infected by HIV-1 . Several Map kinase phosphatases involved in T-cell activation are also modulated in virus-infected cells ( Figure 3 , bottom right ) . Fos and Jun bind together to form the AP-1 transcription factor [8] , which is essential for the differentiation and proliferation of lymphocytes . Multiple AP-1 binding sites are found in the HIV-1 LTR [9] and it has been demonstrated that this transcription factor promotes viral transcription [10] . The DAVID analysis pinpointed a cluster of over-expressed Fos-related genes ( i . e . Fos , Jun and related genes BATF3 , ATF3 , FOSB , FOSL1 , and FOSL2 ) in virus-infected cells ( Figure 3 , bottom left ) . Interestingly , a promoter analysis of the genes modulated in HIV-1-infected cells reveals that 57% of DEGs identified in this study and 70% of core genes ( differentially expressed at 3 time points ) contain at least one binding site for AP-1 ( Figure S2 ) , confirming that this transcription factor is one of the core determinants of the cellular environment that is most favorable to productive HIV-1 infection in primary human CD4+ T cells . Among DEGs found precisely in HIV-1-expressing cells are genes involved in both positive and negative regulation of apoptosis ( Figure 3 , top right ) . Most of these genes appear to be an integral part of the genetic programme engaged in virus-infected cells and are stable through time . However , expression a subset of genes increases rapidly at 48 and 72 h post-infection , thus suggesting that such DEGs are a consequence rather than the determinant of viral gene expression . Further analysis shows that these genes are implicated in p53-dependent apoptosis – specifically CDKN1A/p21 , MDM2 , GADD45A , GADD45G , TNFRSF10B ( TRAIL ) , ATM , ZMAT3 , PMAIP1 ( NOXA ) , BCL2L11 , PERP , TP53I3 , RRM2B , and SESN3 ( Figure 3 , bottom right ) . Additionally , the p53 pathway is identified by the DAVID analysis as the most significantly over-represented ontology category among the 180 genes exclusive to the 48 h and 72 h time points ( p<0 . 0001; data not shown ) . The expression pattern of these genes over time is in agreement with the numerous reports of p53-related apoptosis in HIV-1-infected cells following DNA damage caused by the virus integration process [11] , [12] , [13] , [14] , [15] . However , patterns of differential expression of p53-regulating genes emerge as early as 24 h , before HIV-1-induced apoptosis occurs . For example , MDM2 , a factor responsible for the degradation of p53 [16] , is over-expressed in virus-infected cells . On the other hand , ATM , a gene involved in phosphorylation and inactivation of MDM2 [17] , is under-expressed during this same period . This suggests that cells with low potential for p53 activation are more susceptible to productive HIV-1 infection , perhaps due to their slower reaction time for triggering p53-dependent apoptosis following viral integration , giving the virus more time to replicate actively . The DAVID analysis is useful for broad categorization but is ultimately insufficient to fully extract biological significance from the dataset . A bibliography analysis was thus performed to visualize known relationships between genes present in the dataset . Bibliosphere ( Genomatix ) extracts relationships from co-citation of gene names ( including synonyms ) in abstracts from Medline . The resulting network illustrated in Figure 4A is consistent with the previous analysis , as the already identified over-represented categories cluster together . Moreover , the analysis uncovered numerous DEGs closely related to these clusters , but missed by the DAVID analysis . For example , it pinpointed CDC25A , CDC25C , and CDC14A near the p53 genes cluster . The pattern of expression of these genes is consistent with cells being most permissive to HIV-1 infection when entering in the M-G2 phase of the cell cycle [18] . The graph can also help identify small clusters of genes that would otherwise have gone unnoticed . Notably , furin and PCSK5 proteases , which are both known to participate in the cleavage of viral glycoprotein gp160 [19] , [20] , [21] , [22] , show opposite expression patterns . Indeed , furin is over-expressed in virus-infected cells , while PCSK5 is under-expressed at the 24 h time point . This implies that , although both proteases can cleave the viral envelope in vitro , furin is favored in acute HIV-1 infection studies . The graph file is available as Dataset S2 and can be explored dynamically by using graph visualization software Gephi ( http://gephi . org/ ) . It was recently suggested that poorly characterized genes deserve a more careful analysis [23] . We performed a careful literature analysis and found 178 unconnected genes for which no known relationship with other genes in the dataset is currently described and/or little information is available ( Figure 4A ) . We repeated the DAVID analysis on this subset of genes and found two over-represented protein domains , i . e . Krüppel associated box ( KRAB ) and GTPase regulator activity ( Figure 4B ) . The KRAB domain is mainly found in KRAB-ZNFs , a large family of mammalian transcription factors responsible for negative regulation of transcription through chromatin remodelling via their association with KAP1 [24] . They have been recently implicated in control of endogeneous retroelements [25] . Most of the modulated KRAB-ZFPs are under-expressed in virus-infected cells , which might indicate that their absence creates a permissive environment for HIV-1 , probably by playing a role in T-cell regulation . Two of the identified KRAB-ZFPs can modulate crucial components of HIV-1 and lymphocyte biology . Indeed , overexpression of ZNF383 inhibits the transcriptional activities of AP-1 [26] , while ZNF675 can suppress TRAF6-induced activation of NF-κB and c-Jun N-terminal kinase [27] . Interestingly , the two members that are over-expressed in virus-infected cells show mutations in the MLE motif of their KRAB domain ( i . e . ZNF79 and ZNF282 ) ( Figure S3 ) , a pattern associated with loss of repression potential for KRAB-ZFPs [28] . It should be noted that ZNF79 is also a known p53 target [29] . The second over-represented category contains GTPase regulators , mainly of the RHO/RAC family . These influence a variety of cellular processes such as endocytic trafficking , actin dynamics and cell growth by affecting the rate of GTP hydrolysis by GTPases [30] . Many of the identified members either participate in T-cell signaling responses or directly influence proteins known to be important for HIV-1 entry in human primary CD4+ T cells , such as HERC1 ( under-expressed in virus-infected cells ) that is known to regulate ARF6 [31] , [32] . Amongst the list of DEGs in HIV-1-infected cells , most genes showing high levels of differential expression are poorly studied . Since we isolated cells productively infected with HIV-1 from the total population ( primarily composed of uninfected bystander cells ) , under-expressed genes potentially impede HIV-1 at some step of its replicative cycle , while the reverse holds true for over-expressed genes . For example , TRIM22 , known for its role in inhibition of HIV-1 transcription [33] , is under-expressed in virus-infected cells . Of note TRIM22 RNA levels progressively return to normal , suggesting that HIV-1 can overcome its effects over time ( Table 1 ) . Transcription factors known to repress HIV-1 transcription such as YY1 [34] and BCL11B/CTIP2 [35] are also found to be under-expressed in virus-infected cells . MARCH8 , a regulator of vesicular transport of proteins between cellular compartments , was recently identified in a large scale siRNA screen as a top candidate for the inhibition of HIV-1 infection [36] and is found under-expressed in virus-infected cells in the present dataset . We hereby present a list of the most promising understudied genes in regard to their importance for HIV-1 pathobiology and T lymphocyte biology . For example the transcription factor ZBED2 is highly over-expressed in HIV-1-infected cells . Although little is known about its function , this gene has been reported to be over-expressed in differentiated T cells [37] , [38] . Its precise direct and indirect roles in the regulation of HIV-1 expression or lymphocyte differentiation remain to be more clearly defined . GJB2 and GJB6 are connexins that are members of the gap junction protein family involved in the formation of cell-cell channels [39] . It is possible that they play a role in viral entry or cell-cell transmission . GSDMB and DFNA5 are two members of the gasdermin protein family – DFNA5 has been recently shown to participate in p53-dependent apoptosis [40] . MYOF is a membrane-associated protein involved in both caveolin and clathrin-mediated endocytosis pathways along with membrane resealing after damage [41] . This gene is highly over-expressed in HIV-1-infected cells and could participate in the repair of the plasma membrane following the budding of a multitude of virions , allowing the cell to live longer and to produce more viruses . The expression pattern of CLC ( also called galectin-10 ) differs from that of other modulated genes . CLC mRNA levels are significantly diminished in virus-infected cells whereas they steadily increase over time in uninfected bystander cells ( although not reaching statistical significance ) . CLC has been shown to be a crucial determinant of Treg suppressor function [42] . PCR quantification for the aforementioned genes reveals excellent concordance with the microarray data ( Table 1 ) . We are currently investigating the precise role of some of these genes in the HIV-1 infection process and T-cell biology . Affymetrix Exon arrays allow for the detection of alternative splicing events . Using the PECA-SI algorithm [43] , we detected 323 , 129 and 107 differential splicing events in transcripts of virus-infected versus uninfected bystander CD4+ T cells at 24 , 48 and 72 h post-infection , respectively ( Figure 5A and Dataset S3; see Methods for filtering parameters ) . Similarly to gene expression , more events were detected when comparing HIV-1-infected to uninfected bystander cells than to mock-infected ones , and no alternative splicing events were detected in the uninfected bystander population . We looked for a splicing event known to occur in HIV-1-infected cells . In T lymphocytes , CD45 isoforms are a marker of naive ( CD45RA ) and memory phenotypes ( CD45RO ) – the latter being preferentially infected by HIV-1 [44] . This is indeed reflected in the data , as exon 4 of CD45 ( corresponding to CD45RA ) is under-expressed in virus-infected cells , implying that this fraction contains more CD45RO ( Figure 5C , top ) . We next focused our attention on events susceptible to be important in the biology of both HIV-1-infected and CD4+ T cells . Multiple events are detected in the C-terminal portion of inositol 1 , 4 , 5-triphosphate receptor ( ITPR1 ) , yielding a short isoform containing only the calcium-channel domains of the protein ( Figure 5C , center ) . ITPR1 plays a role in lymphocyte activation [45] and this isoform could represent a constitutionally active form of the receptor present in highly activated and/or memory cells . RUNX1 ( AML1/EVI1 ) is a master regulator of hematopoietic development important in T-cell differentiation [46] and is known to have multiple isoforms arising from alternative promoter usage [47] ( Figure 5C , bottom ) . We identify a short isoform of RUNX1 being enriched specifically in virus-infected cells . The isoforms of RUNX1 have been implicated in different stages of haematopoiesis [47] . However their precise role in T cells or potential interaction with HIV-1 are still unclear . Other interesting alternative splicing events include an isoform of LEF1 , a transcription factor implicated in the regulation of HIV-1 transcription [48] , and EVI5 , an oncogene implicated in cell cycle control [49] – both are RUNX1 interaction partners . qRT-PCR confirmation for all the aforementioned events was performed and results are highly consistent with microarray-acquired data ( Figure 5B ) .
In this manuscript , we provide the first comparative analysis of exon-level transcriptomic profiles between HIV-1-infected primary human CD4+ T cells and their uninfected bystander cell counterparts . By doing so , we define the virus-induced genes and microenvironment most favorable to allow productive HIV-1 infection and show that even within a population of activated CD4+ T cells , the permissive environment for HIV-1 infection is very specific . The profile of virus-infected cells is consistent with activated/effector memory CD4+ T cells expressing high levels of cytokines . We found that Th1 and Th17 were to some extent more permissive to virus infection in this specific in vitro experimental setting . The expression patterns of most genes identified in this study are in agreement with the current literature in regard to HIV-1 and lymphocyte activation status , which provide significance to our observations . However , it must be emphasized while we rediscovered many well-studied genes and pathways known to be important for HIV-1 , the precise function of a large number of other genes that were identified in this work is still currently poorly described . We believe that efforts should be made to understand their function , as yet unexplored avenues might allow deepening our understanding of the interplay between HIV-1 and lymphocyte biology . The design of our microarray experiment captures both the transcriptomic portrait of highly permissive cells and the changes induced by the virus itself . While a clear discrimination between theses two events is complicated by confounding factors such as asynchronous infection and a potential cell death of some CD4+ T cells , a longitudinal analysis of the data allows us to achieve a comparison of gene expression patterns between HIV-1-infected and uninfected bystander cells . We concluded that genes for which levels change significantly over time in virus-infected cells are directly modulated by HIV-1 . As an example , the p53 apoptosis pathway is clearly induced in virus-infected cells at 48 and 72 h post-infection . However , the vast majority of differentially expressed genes are relatively stable over time in virus-infected CD4+ T cells and thus define the transcriptomic programme of a subpopulation preferentially infected by HIV-1 . In this context , over-expressed genes are potential viral permissiveness factors , while underexpressed genes are candidate restriction factors . Functional assays are currently underway to determine which of the newly identified candidate genes play a key role in HIV-1 and/or lymphocyte biology . One of our primary objectives was to identify changes occurring in uninfected bystander CD4+ T cells exposed to HIV-1 , as previous studies have reported dramatic effects in cells exposed to virions or its components . To this end , we purposely left the initial viral inoculum in our purified CD4+ T cell populations to allow for putative virus-mediated signal transduction events . We were surprised to find no significant changes in the transcriptome of the uninfected bystander cell population at least at early time points after HIV-1 infection ( i . e . 24 , 48 and 72 h ) . It is possible that the presence of cells other than CD4+ T lymphocytes is required to mediate changes in gene expression in uninfected bystander cells . We nonetheless detected an increase of apoptosis in uninfected bystander cells at late time points ( data not shown ) . This could imply that gene regulation is not necessary for apoptosis induction . For example , direct caspase activation induced by FAS or TRAIL ligation could explain this phenomenon . It should also be noted that we used an R5-tropic variant of HIV-1 , as this tropism is more representative of early infection events . However , it cannot be excluded that an X4-tropic virus would have an effect on the transcriptome of uninfected bystander cells , as this variant characteristic of late-stage infection is known to have higher apoptosis-inducing activity [50] . On a similar note , a somewhat different HIV-1-mediated gene expression pattern might be obtained when using a distinct R5-tropic virus . Additional experiments are needed to solve these issues . As we demonstrated in this work , the isolation of human primary CD4+ T cells productively infected with HIV-1 is a powerful approach which amplifies the power of transcriptome analysis . We believe that further dissection of virus-infected CD4+ T cell subtypes could yield even more information , as the profile we obtained is undoubtedly an average of different types of susceptible subpopulations . While observations made in this manuscript describe the relationship between HIV-1 and CD4+ T cells , it is in the absence of the multitude of other factors influencing dynamics of infection in vivo . Therefore , transcriptomic analysis of virus-infected cells in more complex experimental settings such as total peripheral blood mononuclear cells or humanized mice models would provide additional insight in the intricate relationship between the virus and its host environment . We hope that the data provided here can serve as a roadmap to focus efforts on neglected aspects of T-cell and HIV-1 biology , leading to a better understanding of the complex relationship between the virus and its host .
Human peripheral blood mononuclear cells were obtained from healthy blood donors , in accordance with the guidelines of the Bioethics Committee of the Centre Hospitalier de l'Université Laval Research Center , by density-gradient centrifugation on Ficoll-Hypaque ( Wisent , St-Bruno , QC ) . All blood donors were informed and agreed to a written consent prior to blood donation . Cells were plated in 75-cm2 flasks at 15×106/mL for 2 h . The non-adherent cells from the supernatant were enriched in CD4+ T cells with the human CD4+ T Cell Enrichment Kit ( Stemcell Technologies Inc . , Vancouver , BC ) . The purity obtained was routinely higher than 98% of CD3+CD4+ T cells . Cells were cultured at 2×106/ml in RPMI-1640 medium ( Invitrogen , Burlington , ON ) supplemented with 10% foetal bovine serum ( Invitrogen ) , L-glutamine ( 2 mM ) ( Wisent ) , penicillin G ( 100 U/ml ) , streptomycin ( 100 µg/ml ) ( Wisent ) and primocine ( Amaxa Biosystems , Gaithersburg , MD ) . Cells were rested for 24 h after isolation and treated with phytohemagglutinin-L ( PHA ) ( 1 µg/ml ) ( Sigma-Aldrich , St-Louis , MO ) and recombinant human IL-2 ( rhIL-2 ) ( 30 U/ml ) ( AIDS Research and Reference Reagent Program , Germantown , MD ) for 2 days at 37°C under a 5% CO2 atmosphere prior to HIV-1 infection and rhIL-2 was refreshed when the medium was changed . Human embryonic kidney 293T cells were maintained in DMEM ( Invitrogen ) supplemented with 10% FBS , L-glutamine , penicillin G and streptomycin . NL4-3 BAL-IRES-HSA virions were described previously [3] and produced in 293T cells using a commercial calcium phosphate kit ( CalPhos Mammalian Transfection kit , Clontech , Palo Alto , CA ) . Cell-free supernatants were ultracentrifuged to eliminate free p24 . Finally , samples were aliquoted before storage at −85°C . A homemade ELISA test was used to normalize the p24 content in all viral preparations [51] . Virus infection was achieved by inoculating primary human CD4+ T cells with a fixed amount of reporter virus standardized in term of p24 ( i . e . 10 ng of p24 per 1×105 target cells ) . All virus preparations underwent a single freeze-thaw cycle before initiation of infection studies . A control consisting of mock-infected cells was obtained by transient transfection of 293T cells with an equimolar amount of pCDNA 3 . 1 ( control empty vector ) . Next , purified CD4+ T cells were treated with a volume of supernatant from 293T cells transfected with pCDNA 3 . 1 similar to the one used for HIV-1 infection experiments . A total of 100×106 primary human CD4+ T cells were exposed to NL4-3 BAL-IRES-HSA and 30×106 target cells were used for mock-infected controls for each of the three donors tested . In order to get about 5×105 CD4+ T cells productively infected with HIV-1 ( i . e . HSA+ ) , a total of 50 , 30 and 20×106 cells were used to isolate an average of 5×105 virus-infected cells at 24 , 48 and 72 h post-infection , respectively . Following infection of primary human CD4+ T cells with the fully competent HSA-encoding virions , HIV-1-infected and uninfected bystander cell populations were isolated using a previously described protocol with slight modifications [3] . In brief , all magnets were pre-cooled for one hour at 4°C . Only the first HSA-negative fraction was used to obtain uninfected bystander cells because subsequent negative fractions have an increased risk of containing HIV-1-infected cells ( unpublished data ) . Mock-infected cells were subjected to the same procedure as the uninfected bystander fraction . A protocol was designed for RNA isolation , as neither Trizol nor silica-based column methods could yield sufficient amounts of high quality RNA from HIV-1-infected fractions . Indeed , the positive fractions contain magnetic beads which were found to interfere with the Trizol reagent ( See Protocol S1 ) . Flow cytometry analyses were performed with 5×105 cells that were incubated with 100 µl of wash buffer ( PBS [pH 7 . 4] , BSA 1% , and EDTA 2 mM ) containing a saturating amount of a monoclonal rat anti-mouse HSA antibody ( clone M1/69 , PE-coupled , BD Biosciences , Mississauga , ON ) , anti-CD4 , anti-CD3 , anti-CD27 , anti-CD45RO ( all from BD Biosciences ) or a corresponding isotype-matched control antibody for 30 min at 4°C . Cells were then washed , fixed with 2% paraformaldehyde for 30 min at 4°C and analyzed on a cytofluorometer ( FACSCanto , BD Biosciences ) . Further analyses were performed using FCS Express V3 . 0 software ( De Novo Software , Los Angeles , CA ) . A total input of 200 ng of RNA was used to prepare targets for array hybridization using the Ambion WT Expression Kit ( Applied Biosystems , Austin , TX ) . Data was normalized using RMA at the gene and exon level with Affymetrix Power Tools – core-level probe definition were used in both cases for results presented in this manuscript . Bioconductor package limma [52] was used to find modulated genes – an FDR of 1% and a fold change of 1 . 7 were used to filter the lists . A minimum signal filter of 100 on the average of three replicates was also applied . Time-wise and aggregate comparisons were done between infected , bystander and mock treated cells . DAVID analysis was done using version 6 . 7 with standard parameters using the following categories: GOTERM_BP_FAT , GOTERM_MF_FAT , KEGG_PATHWAY , BIOCARTA , SP_PIR_KEYWORDS , UP_SEQ_FEATURE , SMART , INTERPRO , UCSC_TFBS . Bonferroni corrected p-values<0 . 001 were considered as significant . Bibliosphere analysis was performed using version 7 . 24 with the compiled list of 835 modulated genes identified by limma . We settled on the presence of three co-citations or more in the same sentence in a PubMed abstract as a relationship criterion . We exported the data to Gephi ( http://gephi . org/ ) , a powerful and interactive network visualization and exploration platform for further analysis using the Gefx library . Nodes were arranged using a directed force algorithm ( Force2 ) , colored according to fold change between uninfected bystander and HIV-1-infected cells at 24 h and sized according to the –log10 of the p-value for the same comparison – these can be dynamically changed at will using the original Gephi file containing the graph and all associated data , available as Dataset S2 . Alternative splicing analysis was performed with PECA-SI . The following filters were applied: a threshold of splicing index of at least 1 . 7 fold , a significant DABG signal ( p<0 . 001 ) in at least 3 groups ( equivalent to 9 chips ) , probes contained in exonic regions and an FDR of 1% . Data was dynamically overlaid and visualized on both Annmap ( http://annmap . picr . man . ac . uk/ ) via Bioconductor xmapcore and xmapbridge packages and Splice Center [53] . qRT-PCR . qRT-PCR against the Tat-spliced isoform was performed to quantify the level of enrichment of HIV-1-infected CD4+ T cells achieved in the HSA-positive fraction and their absence in the HSA-negative fractions . TaqMan RNA-to-CT 1-Step Kits from Invitrogen was used for quantification . The following probe and primers were used in our study: probe , TATCAAAGCAACCCACCTCC , forward primer , GAAGCATCCAGGAAGTCAGC , reverse primer , CTATTCCTTCGGGCCTGTC . PCR was performed under standard TaqMan cycling conditions using a Rotor-gene 3000 ( Corbett Life Science , San Francisco , USA ) . SYBR Green detection was used for all subsequent PCR targets . qRT-PCR confirmation of genes was performed with the Power SYBR Green RNA-to-CT 1-Step kit from Invitrogen . Expression values were normalized to 18S and quantification was performed using the CT method . Primers for gene expression and alternative splicing confirmations are shown in Table S1 .
|
Some previous studies have monitored HIV-1-induced gene expression in various host cell targets and tissues but the discrimination between productively infected cells and uninfected bystander cells represents a technical challenge yet to be solved . Consequently , data interpretation has always been biased towards the transcriptional response of a majority of uninfected bystander cells that were exposed to soluble factors released by virus-infected cells . Following the design of a unique and innovative molecular tool to identify cells productively infected with HIV-1 and the description of an efficient magnetic beads-based technique to separate them from uninfected bystander cells , we undertake this challenge and perform the first comparative whole-genome transcriptomic and large-scale proteomic profiling of both HIV-1-infected and uninfected bystander CD4+ T cells . We demonstrate herein that HIV-1- infected and uninfected bystander cells display distinctive transcriptomic signatures which might permit to identify new susceptibility and resistance factors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"genome",
"expression",
"analysis",
"infectious",
"diseases",
"genome",
"analysis",
"tools",
"hiv",
"retrovirology",
"and",
"hiv",
"immunopathogenesis",
"biology",
"genomics",
"viral",
"diseases",
"genetics",
"and",
"genomics",
"transcriptomes"
] |
2012
|
Exon Level Transcriptomic Profiling of HIV-1-Infected CD4+ T Cells Reveals Virus-Induced Genes and Host Environment Favorable for Viral Replication
|
There was a large dengue outbreak in Taiwan in 2015 , in which the ages of the affected individuals were higher than those in other countries . The aim of this study was to explore the characteristics and prognostic factors for adults with severe dengue in intensive care units ( ICUs ) . All adults admitted to ICUs with dengue virus infection ( DENV ) at a medical center from July 1 , 2015 to December 31 , 2015 were enrolled . DENV was diagnosed by the presence of serum NS1 antigen , IgM antibodies to dengue virus , or dengue virus RNA by real-time reverse transcriptase polymerase chain reaction . Demographic data , clinical features , and lab data were collected , and a multivariate Cox model was used to identify the predictive factors for in-hospital mortality . Seventy-five patients admitted to ICUs with laboratory-confirmed DENV were enrolled ( mean age 72 . 3±9 . 3 years ) . The most common comorbidities included hypertension ( 72 . 0% ) , diabetes ( 43 . 7% ) , and chronic kidney disease ( 22 . 7% ) . The in-hospital case fatality rate ( CFR ) was 41 . 3% . The patients who died were predominantly female , had higher disease severity at ICU admission , shorter ICU/hospital stay , longer initial activated partial thromboplastin time ( APTT ) , and higher initial serum aspartate transaminase levels . Cardiac arrest before ICU admission ( hazard ratio [HR]: 6 . 26 [1 . 91–20 . 54] ) , prolonged APTT ( >48 seconds; HR: 3 . 91 [1 . 69–9 . 07] ) , and the presence of acute kidney injury on admission ( HR: 2 . 48 [1 . 07–5 . 74] ) , were independently associated with in-hospital fatality in the Cox multivariate analysis . During the 2015 dengue outbreak in Taiwan , the patients with severe dengue in ICUs were characterized by old age , multiple comorbidities , and a high CFR . Organ failure ( including cardiac failure , and renal failure ) and coagulation disturbance ( prolongation of initial APTT ) were independent predictive factors for in-hospital fatality .
Dengue fever ( DF ) is a mosquito-borne disease that affects at least 50 million people annually worldwide [1 , 2] . There have been several outbreaks during the past three decades in Taiwan [3] , most recently , in southern Taiwan in 2015 which involved 43 , 784 cases [4] . DF is often considered to be a pediatric disease in Southeast Asia [5] . However , severe dengue can also occur in adults and the elderly [6–8] . The age of DF cases in Taiwan is generally older than that in other countries [9–13] . The predominant age of patients with DF in the Philippines during 2000 to 2009 was 5–14 years [11] , and 24 years in Thailand during 2001 to 2011 [13] . In comparison , the median age of Taiwanese patients with DF was 45 years from 2010 to 2012 with the major age group being older than 65 years [12] . Old patients with DF are often associated with atypical presentations , longer hospitalization and more underlying diseases [14] . In particular , diabetes mellitus , hypertension , and aging are associated with a higher risk of DHF/DSS and a worse outcome [6 , 15–18] . Various predictive factors for mortality have been identified in DF , including old age , acute renal injury , acute respiratory failure , hepatitis , altered mental status , bleeding and coagulopathy [19–21] . The acute physiology and chronic health evaluation ( APACHE ) II score may predict mortality in patients admitted to an intensive care unit ( ICU ) in a prospective observational cohort study [22] . Recently a Brazilian study also showed that APACHE II score and sequential organ failure assessment ( SOFA ) score were associated with in-hospital fatality in patients in an ICU [23] . During the dengue outbreak in 2015 , many elderly patients were admitted to ICUs . Therefore , the aim of this study was to explore the clinical characteristics and prognostic factors among this elderly cohort in our ICUs in Taiwan .
There are eight ICUs with a total of 104 beds at National Cheng Kung University Hospital , a tertiary hospital in southern Taiwan . The medical records of all patients with DF admitted to our ICUs from July 1 , 2015 and December 31 , 2015 were reviewed . The diagnosis of DENV was confirmed by one or more of the following tests: presence of serum dengue NS1 antigen , dengue IgM antibodies as detected using a Bioline Dengue Duo™ kit ( Standard Diagnostics , Korea ) , or dengue virus RNA detected by real-time reverse transcriptase-polymerase chain reaction ( RT-PCR ) ( TIB Molbiol , Lightmix kit; Roche Applied Science , Berlin , Germany ) . Demographic data , underlying diseases , admission characteristics , and laboratory data were collected through the ICU Clinical Information System ( IntelliVue Clinical Information Portfolio , Philips ) and electronic medical records . The dengue severity was evaluated at hospital admission and ICU admission according to the dengue classification of the World Health Organization ( WHO ) 2009 [24] . Acute kidney injury ( AKI ) at ICU admission was defined according to the Kidney Disease: Improve Global Outcomes ( KDIGO ) clinical practice guidelines for AKI in the first 24 hours after ICU admission [25] . SOFA and APACHE II scores were calculated in the first 24 hours after ICU admission [26 , 27] . Bacteremia and fungemia were defined by the presence of clinically significant microorganism during the hospital course . Laboratory data were recorded on the first day of admission to the ICU . All data were delinked , and are provided in the S1 Dataset . This study adhered to the Declaration of Helsinki and was approved by the Human Research and Ethics Committee of National Cheng Kung University Hospital ( IRB number: A-ER-104-367 ) . The Institutional Review Board waived the need for informed consent . Continuous variables were tested for normal distribution using the Shapiro-Wilk test . Normally distributed variables were reported as means with standard deviation ( SD ) and compared using the Student’s t-test . Non-normally distributed continuous variables were reported as medians with inter-quartile range ( IQR ) and compared using the Mann-Whitney U-test . Categorical data were reported as proportions and compared using the Fisher’s exact test . A univariate Cox model was used to identify the possible predictive factors for death . Receiver operating characteristic ( ROC ) curves were constructed for the severity scores and the continuous variables of significant prognostic factors . The cut-off value for each significant continuous variable was selected using the Youden index . The cases with missing data were excluded in the Cox model analysis . A multivariate Cox model with binary variables was used to identify the predictive factors for death . Most data were analyzed using SPSS Statistical software for Windows , version 17 . 0 ( SPSS Inc . , Chicago ) . The comparison of ROC curves was analyzed using MedCalc , version 12 . 7 . 8 . 0 with DeLong test [28] . A two-sided P value of less than 0 . 05 was considered to be statistically significant .
Six hundred and twenty-five inpatients with laboratory-confirmed dengue were identified during the 6-month study period . Of these patients , 532 patients were excluded because they were admitted to a general ward only , and 93 ( 14 . 9% ) patients were admitted to an ICU during the study period . Among them , six patients were younger than 18 years old , and the ICU course was irrelevant to dengue infection in 12 . Finally , 75 adult patients with laboratory-confirmed DENV were included ( Fig 1 ) . Age distribution of all hospitalized patients with dengue fever and of those admitted to ICUs was shown in Fig 2 . Sixty-two ( 82 . 7% ) patients had NS1 antigen detected in their serum , 29 ( 38 . 6% ) had a positive PCR for dengue virus RNA , and 25 ( 33 . 3% ) had IgM antibodies to dengue virus . Thirty-three ( 44 . 0% ) patients had at least two positive results of these three tests . The overall age distribution of dengue in Taiwan during the previous year and this outbreak is listed in S1 Fig . Fourteen ( 18 . 7% ) patients were classified as having dengue with warning signs , and the other 61 ( 81 . 3% ) patients were classified as having severe dengue at hospital admission . All patients were classified as having severe dengue at ICU admission according to the dengue classification of World Health Organization ( WHO ) 2009 [24] . The primary indications for ICU admission included severe gastrointestinal bleeding ( 23 cases , 30 . 7% ) , acute respiratory failure ( 18 , 24 . 0% ) , and shock ( 10 , 13 . 3% ) . Other indications for ICU admission included encephalopathy ( eight , 10 . 7% ) , post cardiac arrest ( five , 6 . 7% ) , intracranial hemorrhage ( five , 6 . 7% ) , myocarditis ( four , 5 . 3% ) , and severe hepatitis ( two , 2 . 6% ) . The mean age of the 75 patients was 72 . 3±9 . 3 years ( ranging from 50 . 7 to 88 . 5 years , Table 1 ) . Males accounted for 61 . 3% of the patients . The most common comorbidities included hypertension ( 72 . 0% ) , diabetes ( 43 . 7% ) , and chronic kidney disease ( 22 . 7% ) . Median APACHE II score and SOFA score at the ICU admission was 20 and 11 , respectively . At the ICU admission , 41 ( 54 . 7% ) patients had AKI , according to the KDIGO criteria and 12 ( 29 . 3% ) of whom needed renal replacement therapy during the ICU stay . Four ( 5 . 3% ) patients had end-stage renal disease and received maintenance dialysis . Approximately three-quarters ( 57 , 76 . 0% ) of all patients required ventilator support during the ICU stay . Seven ( 9 . 3% ) patients experienced cardiac arrest before ICU admission . The median resuscitation time was 10 minutes , ranging from 2–38 minutes . Five of them were admitted to the ICU immediately after the resuscitation . One experienced cardiac arrest during the endotracheal intubation at the second hour of emergency department ( ED ) arrival and was admitted to the ICU on the next day . The another one experienced cardiac arrest due to massive gastrointestinal bleeding and severe metabolic acidosis at the 23rd hour of ED arrival and was admitted to the ICU at the 45th hour of ED arrival . Fifty-four ( 72 . 9% ) patients received blood transfusions ( Table 1 ) . The in-hospital CFR was 41 . 3% ( 31 patients ) , and these patients had a median ICU stay of 4 ( IQR: 1–8 ) days . The leading cause of fatality was multiple organ failure ( 14/31 , 45 . 2% ) , and nine of these 14 patients had overt bleeding . Seven ( 22 . 6% ) patients died of refractory shock and severe metabolic acidosis , six ( 19 . 4% ) intracranial hemorrhage , three ( 9 . 6% ) bloodstream infections , and one ( 3 . 2% ) pneumonia with respiratory failure . Nineteen bacterial and three fungal pathogens were identified in the bloodstream of 18 ( 23 . 7% ) patients . Seven patients experienced bloodstream infections early within 48 hours after ICU admission . The etiological species were complex and mainly Enterobacteriaceae ( 40 . 1% , 9/22 ) . Two patients had polymicrobial bacteremia . Notably , two patients experienced recurrent fever at the eighth and ninth day after the onset of DF , respectively , which was caused by candidemia . Bloodstream infections occurring late in the ICU course were due to the common healthcare-associated pathogens , Acinetobacter baumannii ( three isolates ) , Stenotrophomonas maltophilia ( one ) , Chryseobacterium meningosepticum ( one ) , and Candida albicans ( one ) . Compared with the survivors , the non-survivors were more often females and had a shorter ICU and hospital stay . The rates of underlying diseases and conditions necessitating blood component transfusions were similar between the survivors and non-survivors . The non-survivors had higher APACHE II and SOFA scores and a higher rate of organ failure , including acute respiratory failure and AKI . Thrombocytopenia and elevated levels of serum transaminases were common findings in both groups . The non-survivors had higher initial activated partial thromboplastin time ( APTT ) than the survivors ( 50 . 8 vs . 40 . 8 s , P < 0 . 001 ) , and higher serum levels of aspartate aminotransferase ( 662 . 0 vs . 240 . 5 U/L , P = 0 . 042 ) ( Table 2 ) . In the univariate Cox model , female gender , cardiac arrest before ICU admission , respiratory failure at ICU admission , high creatinine level , AKI at ICU admission , a high APACHE II or SOFA score , and APTT longer than 48 s were significantly associated with in-hospital fatality . In the multivariate analysis , cardiac arrest before ICU admission ( hazard ratio [HR] 6 . 26 [95% confidence interval: 1 . 91–20 . 54] ) , AKI at ICU admission ( HR 2 . 48 [1 . 07–5 . 74] ) , and initial APTT longer than 48 s ( HR 3 . 91 [1 . 69–9 . 07] ) were independent predictive factors for in-hospital fatality after adjusting for gender , cardiac arrest before ICU admission , respiratory failure at ICU admission , presence of AKI at ICU admission and initial APTT longer than 48 s ( Table 3 ) . APACHE II and SOFA scores include many clinical parameters and were not included in the final model of multivariate Cox analysis as they demonstrated collinearity with other variables . They were positively associated with acute respiratory failure , AKI , and cardiac arrest . Creatinine was also not included in the final model due to collinearity with the presence of AKI at ICU admission . More detailed information about the Cox model construction is included in the S1 File . Moreover , APTT prolongation > 48 s remained to be an independent predictive factor for in-hospital fatality after adjusting for APACHE II score ( HR: 3 . 34 , P = 0 . 006 ) , and was marginally significant after adjusting for SOFA score ( HR: 2 . 43 , P = 0 . 053 ) . Detailed information of the Cox regression analysis is listed in the S1 File . Fig 3 shows the ROC curve with the three continuous variables: APTT , APACHE II score and SOFA score . The area under the ROC curve ( AUC ) of APTT was 0 . 76 ( 95% confidence interval [CI]: 0 . 64–0 . 88 ) , APACHE II score 0 . 84 ( 0 . 75–0 . 93 ) , and SOFA score 0 . 85 ( 0 . 77–0 . 94 ) . Though there was no significant difference between APTT and two scores ( APTT vs . APACHE II , P = 0 . 27; APTT vs . SOFA , P = 0 . 16 ) , AUCs of APACHE II and SOFA scores , which both were a constellation of multiple clinical and laboratory variables , were higher than that of APTT . However , APTT , a single laboratory parameter , exhibited valuable prognostic significance .
There are several limitations to this study . First , this is a retrospective study conducted in the ICUs of a single tertiary hospital , in which a high severity of disease and high selection bias could be expected . The conclusions cannot be generalized to all age groups , especially pediatric populations . Second , the number of cases is too limited to include many variables in one analysis . Accordingly , the confidence intervals of the hazard ratios in the Cox model are wide . Third , only the laboratory data on the first day of ICU admission were included for analysis , and their dynamic changes , which may signify additional clinical impact , were not considered . However , we believe that the early prognostic significance resulting from initial laboratory abnormalities at the time of ICU admission will be valuable for intensive care physicians when treating elderly patients with severe dengue . Finally , though APTT prolongation can be an early marker of in-hospital fatality , it remains obscure that more or early intensive care can improve the outcome of dengue patients with APTT prolongation . Prospective clinical studies are warranted to verify the benefit of early risk stratification of the elderly with severe dengue .
During the 2015 dengue outbreak in Taiwan , the patients with severe dengue in ICUs were characterized by old age , multiple comorbidities , and a high CFR . Organ failure ( including cardiac failure and renal failure ) and coagulation disturbance ( prolongation of initial APTT ) were independent predictive factors for in-hospital fatality . Further studies are needed to clarify the relationship and mechanism between APTT prolongation and in-hospital fatality in patients with DF .
|
Severe forms of dengue fever ( DF ) are usually considered as a pediatric disease in southern Asia and are graded from dengue hemorrhagic fever ( DHF ) to dengue with shock syndrome ( DSS ) . However , the age of affected individuals is increasing in Singapore , Thailand , Mainland China , Taiwan and many other countries . Limited data are available on the elderly with DF in intensive care units ( ICUs ) . DF in the elderly is an emerging infectious disease and poses a new clinical challenge to physicians . We enrolled the patients with laboratory-confirmed dengue in our ICU during the 2015 dengue outbreak in Taiwan . These patients were characterized by old age , multiple comorbidities , and a high case fatality rate ( CFR ) . All of the patients were classified as severe dengue , and the in-hospital CFR was 41 . 3% . Renal failure and cardiac arrest were associated with fatality . In addition to organ failure , initial prolonged activated partial thromboplastin time ( APTT ) in our study was consistent with an independent predictive factor for in-hospital fatality . Previous studies have also reported that prolongation of APTT is a clinical predictor of dengue virus infection ( DENV ) or DHF . Our results highlight that APTT prolongation may be a prognostic factor in critically ill adults with severe dengue .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
[
"dengue",
"virus",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"elderly",
"pathogens",
"respiratory",
"failure",
"tropical",
"diseases",
"geographical",
"locations",
"microbiology",
"pulmonology",
"health",
"care",
"viruses",
"age",
"groups",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"cardiology",
"infectious",
"diseases",
"intensive",
"care",
"units",
"taiwan",
"medical",
"microbiology",
"dengue",
"fever",
"microbial",
"pathogens",
"hospitals",
"critical",
"care",
"and",
"emergency",
"medicine",
"people",
"and",
"places",
"cardiac",
"arrest",
"health",
"care",
"facilities",
"asia",
"flaviviruses",
"viral",
"pathogens",
"population",
"groupings",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"geriatrics",
"organisms"
] |
2017
|
A Cohort Study of Adult Patients with Severe Dengue in Taiwanese Intensive Care Units: The Elderly and APTT Prolongation Matter for Prognosis
|
In the era of systems biology , multi-target pharmacological strategies hold promise for tackling disease-related networks . In this regard , drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome . Here , we perform a pairwise comparison of 90 , 000 putative binding pockets detected in 3 , 700 proteins , and find that 23 , 000 pairs of proteins have at least one similar cavity that could , in principle , accommodate similar ligands . By inspecting these pairs , we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology . Finally , we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types , and in a genome-scale metabolic model of leukemia .
Multi-target strategies are a natural approach to tackling complex diseases . A fine way to achieve a multi-target effect is through drug polypharmacology , i . e . the simultaneous modulation of several targets by means of one single agent [1 , 2] , which poses pharmacokinetic advantages over drug combinations [3] . In the light of systems biology , it seems reasonable to first select a combination of receptors that will modify the biological network as desired , and then design a ligand that it is able to simultaneously bind them [3] . Unfortunately , in practice , most target combinations that are identified in the network analysis step will not show cross-pharmacology , since the discovery of intended promiscuous drugs is still restricted to members of the same protein family [4] . Besides few remarkable exceptions [5–9] , the rational molecular design of ligands that intentionally bind several unrelated proteins is far too complicated , yielding ambivalent , non-drug like molecules . Although challenging to achieve rationally , polypharmacology is a recognized feature of many approved drugs [10] , and even those molecules praised to be highly specific , like imatinib , end up eliciting a quite rich interaction profile [11] . This unavoidable promiscuity has long been regarded as detrimental due to adverse off-target reactions [12 , 13] , but at the same time it paves the way to a reverse drug design strategy , where one would first massively look for proteins that are likely to bind the same ligand , and only then do network analysis to identify the small fraction of putative target combinations that are of therapeutic interest . A systematic way to detect pairs of proteins that could share a ligand is to compare binding sites in their 3D structures [14 , 15] . Arguably , the design of ligands that dock to similar pockets is simpler and more suited to the current medicinal chemistry toolbox , and binding site characterization and comparison methods have flourished with this aim [16] . Using these methods , today it is possible to identify alternative drug targets [17] , predict molecular functions [18] and uncover links between remote proteins [6] . Surprisingly , though , there is a lack of bona fide systems pharmacology continuations of the binding site comparison approach , and it remains unclear whether the space of cross-pharmacology uncovered by structural analysis will ultimately be useful to yield relevant impact on large biological networks . To address this question , we have applied binding site similarity analysis in several systems biology scenarios . For this , we have exhaustively compared pockets across a large fraction of the human proteome , finding connections between close and distant proteins belonging to families with varied tradition in drug discovery . Then , inside the rich collection of polypharmacology opportunities , by applying systems biology techniques , we have pinpointed those cases that could have an impact on protein-protein interaction networks ( PPIs ) related to several therapeutic areas and tumor-types [19] , and to a genome-scale metabolic model ( GSMM ) of cancer cell lines [20] .
In order to navigate the space of putative binding sites in human proteins , a fast and automated protocol is necessary . We used BioGPS [14] , which first characterizes cavities using molecular interaction fields , and then summarizes them with quadruplet fingerprints . We found 87 , 300 cavities ( Fig 1A ) in 31 , 900 protein chains from 3 , 700 unique proteins . Then , we performed a pairwise pocket comparison . Pairs of cavities with a BioGPS score above 0 . 6 were classified as ‘similar’ . This threshold was tuned while we were developing BioGPS , five years ago . Thereafter , we’ve tested it in other datasets , proving its validity to capture polypharmacology [15 , 21] . Reassuringly , when analyzing co-crystallized ligands in the PDB , we observed that pairs of pockets above this cutoff indeed tend to accommodate the same ligands ( S1A–S1C Fig ) , being cavities able to embed the totality of the ligand ( S1D Fig ) while remaining relatively small ( S1E Fig ) and highly specific for ligand-binding regions ( >40% of the cavities overlap with ligands ) . In addition , our cavity pairs are able to account for experimental cross-pharmacology , such as that observed in kinase inhibition screens ( S1H Fig ) [22] , or in closely related natural products ( Figs 1D and S1I ) . Similarly , we found that targets of the same drug tend to display similar pockets ( odds ratio OR = 4 . 02 , P = 2 . 9·10−48 ) , proving the pharmacological relevance of the pockets identified and compared by BioGPS . In total , we discovered 181 , 500 pairs of similar cavities , 68 . 8% of them corresponding to pockets in different structural instances of the same protein , and the other 31 . 2% to cavities in distinct proteins . Corresponding cavities in different structures of the same protein have remarkably high BioGPS scores , proving the sensitivity of this similarity measure ( S1G Fig ) . The fact that most of the paired cavities may be matched to another structural instance of the same protein or fold ( S1F Fig ) demonstrates that , while sensitive , the similarity measure is robust to small structural fluctuations and variations . Overall , 23 , 148 pairs of proteins shared at least one putative binding site ( see Supporting S1 Data ) . From this structural standpoint , the average protein was related to about 7 other proteins , some of which are structurally and functionally unrelated ( Fig 1B and 1C ) , illustrating the known degeneracy of protein binding sites [23] . To demonstrate the power of BioGPS , in Fig 1F we focus on two allosteric cavities that we found in the Epidermal Growth Factor Receptor ( EGFR ) and the Mitogen-Activated Protein Kinase 2 ( MAPK2 ) . While distant in the kinase phylogeny , off-target interactions between EGFR and MAPK2 have been previously detected in high-throughput kinase inhibition screens [24 , 25] . The allosteric pockets that we found , when superimposed , show similar shape and non-bonded hydrophobic and polar patterns ( S1F Fig ) , hinting towards a structural hypothesis for the MAPK2-EGFR cross-pharmacology . Due to their functional relevance , binding sites are under higher evolutionary pressure than the rest of the protein structure [26] . Thus , similar cavities can be found between apparently unrelated proteins . However , even if strongly conserved , the sites need to confer specificity within the same family , as it is well known for kinases [27] . These two aspects are apparent from our results . While , in general , sequence-related proteins tend to share cavities ( P = 2 . 1·10−74 ) , we could find many examples of far-off proteins whose pockets are alike , and of close sequences with divergence in the binding site ( see the modest odds ratios in Fig 1B ) . The same trend could be observed at the fold level ( P = 2 . 4·10−43 ) , detecting analogous pockets in distinct folds , while sometimes failing to match pockets inside the same structural family . Together , these results outline an optimal scenario for the multi-targeting idea , suggesting that more than one function can be perturbed at a time , and also that specificity can be achieved among functionally related proteins . Over the years , the functional connection of proteins has introduced biases in the chemical libraries screened in binding assays [28] . Drug discovery is eminently incremental , and chemotypes known to bind one receptor are often tested in closely related proteins [29] . Chemogenomics databases now collect ligands for about three thousand human proteins [30] , and these ligands can be used to describe targets from a chemical viewpoint [31] . Accordingly , two proteins are associated if they globally recognize similar ligands , providing valuable guidance for the eventual molecular design of polypharmacology . This approach has been successful so far [32] , but it inherits some major shortcomings , namely the strong dependence on the amount and quality of pharmacological data [33] , and the aforementioned bias introduced by incremental library design . In order to evaluate the overlap between the ligand- and structure-centered views of cross-pharmacology , we have used an in-house version of the similarity ensemble approach ( SEA ) developed by Keiser et al . [34] , a popular method to relate proteins through the chemistry of their ligands ( S2 Fig ) . In general , and as a further proof of the relevance of cavity comparisons , we observed a significant correspondence between ligand- and cavity-based similarities ( P = 3 . 1·10−5 ) . Even if so , the two cross-pharmacology spaces differed in many aspects . Given the biases in chemogenomics data , the chemocentric view correlates more strongly with sequence and fold relatedness ( P ~ 0 , Fig 1C ) . As it can be seen in Fig 1B , proteins in the same family shared similar ligands , being kinases and G-protein coupled receptors ( GPCRs ) the most prominent examples due to their pharmaceutical importance . On the contrary , the structural viewpoint was poorly applicable to GPCRs , for which 3D-crystal data are scarce [35] , and highlighted other receptors and nucleic acid binding proteins instead ( Fig 1B ) . Overall , it seems that cavity comparisons offer a complementary catalogue of protein pairs with new , less trivial inter-family opportunities at the expense of certainty in the pharmacology , since ligand binding assay data are not always available for the candidate receptors . While the space of cross-pharmacology opens wide when we focus on binding sites , we observe a drastic decrease in its density: in relative terms , given a protein we found less partners by inspecting cavities than sequences or folds , and far less than from the ligand viewpoint ( Fig 1E ) . This demonstrates the specificity that can be achieved at the binding pocket level , and suggests that structure-based polypharmacology could enable a fine-tuned promiscuity , i . e . a good selectivity in the eventual systems-level chemical-protein interactome . To achieve systems-level interactions , multi-target agents should be able to modulate more than one molecular function . As suggested above , structural cross-pharmacology facilitates this feature , which has been classically achieved through drug combinations [36–38] . Despite some major difficulties , the asset of drug combinations is that , in principle , no restrictions exist to modulate multiple cellular processes . While largely incomplete , the human binary interactome sketches a map of these cellular processes , and can be used to unravel their crosstalk [39] . In the context of the human interactome , targets of successful drug combinations are close to each other ( Fig 2A ) . This relative proximity is in part due to the pathway-based mindset applied to drug combination discovery [40] , and also reflects that , in order to be effective , a multi-target intervention should interplay within the network . Because of its inherent functional bias , ligand-based similarity offers combinations of targets that are even more local than the standards of efficacious drug combinations [41] ( Fig 2A ) . On the contrary , structure-based cross-pharmacology opportunities , while still closer in the network than the background expectation resulting from an unbiased sampling , are further apart . Likewise , beyond measuring pairwise network influence , modules can be encircled in the interactome to reveal communities of proteins devoted to a certain biological process [42] . We have seen that , in general , targets of drug combinations act in few modules , and that groups of proteins with structural cross-pharmacology are more disperse ( Fig 2B ) . In turn , the functional diversity achieved by drug combinations and proteins with similar cavities is comparable , and greater than the diversity obtained from the chemo-centric viewpoint ( Fig 2B ) . It appears , thus , that polypharmacology opportunities based on binding site similarity are widespread enough to provide functional variety , but it is clear that , at least from the network viewpoint , many multi-target opportunities do not resemble the successful ones achieved through drug combinations . The space of structural cross-pharmacology is vast: to detect the small portion of polypharmacology opportunities that will be of therapeutic interest , automated systems biology setups are necessary [43] . In the dawn of systems pharmacology , several approaches have been proposed to identify those groups of targets that will cooperate to elicit the desired therapeutic effect [44 , 45] . Following the module-specificity observed for drug combinations , one possibility is to focus on those regions of the interactome that are related to a phenotype of interest , and then do network analysis on these specific regions to prioritize multi-target opportunities . To exemplify this approach , we have collected drug targets in several therapeutic categories and built sub-networks for each of them [46] . These therapy-specific networks will only be sound if the corresponding drug targets are strongly connected , i . e . if the interactome is capturing the underlying biology of the therapy . This was the case for six major therapeutic classes ( P < 0 . 01 , Fig 3 ) . The resulting networks grown around approved drug targets included a median of 60 . 5 proteins ( Fig 3A ) , and were thus appropriate for sensitive network modulation analysis [47 , 48] ( see Methods and S3 and S4 Figs for further details on the construction of these networks ) . Since therapy-specific networks are seeded with known therapeutic effectors , a reasonable assumption is that node ablations are bound to a beneficial response . With this premise , a way to prioritize protein combinations is to measure their global impact on the network , i . e . to evaluate the effect of each node on the rest of the proteins . In the context of disease mutations , this feature has been successfully modeled by heat flow analysis [49] . Analogously , in each network , we simultaneously perturbed ( assigned heat to ) protein sets with structural cross-pharmacology ( i . e . sets of proteins that all of them have cavities similar to one or more of the proteins in the group , and where at least one cavity is similar to cavities in all of the other proteins ) ( Fig 3A ) . Those target combinations that best spread the heat were prioritized . While many combinations had no major advantage over single targets , in two of the six networks it was possible to find a multi-target case with a marked systems level clout ( Fig 3D ) . In the therapeutic network related to antithrombotic agents ( ATC: B01 ) , for instance , the simultaneous inhibition of the known target FGFR2 and KLK6 was predicted to exert an effect on the network twice as great as the best individual target , requiring half of the heat , i . e . less inhibition strength , in each of the nodes . While intimately related to a therapeutic response , the therapy-specific networks above cannot go beyond current medicinal knowledge , since they are based on the available drug repertoire . More commonly , systems pharmacology departs from a disease-specific network [50] , and target combinations are mined therein . Yet , in the context of PPIs the connection between changes in a disease-specific network and the eventual therapeutic effect is poorly understood . Perhaps an exception is cancer , where the desired outcome is cell death , which can be roughly modeled as a global impact on the network; arguably , this impact is captured by the heat distribution method applied above [49] . To confirm the therapeutic relevance of the measure , we computed the heat released by known targeted therapies on the corresponding tumor types , and observed a significantly high heat circulation ( S3D Fig , P < 10−4 ) . Of the 34 tumor types considered [19] , 22 had significantly connected driver genes ( P < 0 . 01 ) , and we focused on these tumors to build the tumor-specific networks ( median size of 264 proteins ) . Following the assumption that good candidate therapies will prominently distribute heat in the tumor network , we screened our polypharmacology opportunities and observed that , for most ( 20 ) tumor types it was possible to detect at least one combination that was able to distribute heat at least 25% better than any of the corresponding individual seed ( on average , 2 . 7-fold advantages could be achieved ) ( Fig 3D ) . In some tumor types , like stomach adenocarcinoma ( STAD ) , skin cutaneous melanoma ( SKCM ) and heat and neck squamous cell carcinoma ( HNSC ) , the advantage of the combination reached the 5-fold . In Fig 3 we display the network of ovarian cancer , which is representative in terms of size and sensitivity to multiple targets . Here , the simultaneous interference with CDK2 , PPARG and ATRX yields a global heat distribution of the 32% , and a 2 . 1-fold advantage over the ATRX driver gene modulation . As exemplified above , PPI networks are widely applicable , yet mostly descriptive . A less generic , but more predictive , systems biology tool is genome-scale metabolic modeling ( GSMM ) [51] . These models unprecedentedly link the properties of the network to phenotypic traits [52] , yielding very informative simulations compared to the tentative measures of network impact that can be obtained from protein interactomes . Interference with single metabolic genes or reactions are routinely quantified in GSMMs , and have been shown to mimic drug inhibition [20] . However , the brute-force simulation of multi-target effects is impractical , especially beyond double inhibitions , due to combinatorial explosion . In this sense , the constrained space of cross-pharmacology drastically reduces the number of combined inhibitions to simulate , offering a viable corpus with enriched properties for drug design . We illustrate this advantage by considering a GSMM of leukemia cell lines and one of healthy leukocytes [20] . Both models are based on the same reconstruction of the human metabolism and only differ in the flux bounds of the reactions . We could collect structures for 567 of the 1 , 878 ( 30 . 2% ) metabolic proteins , and find cavities in a large proportion of them ( 70 . 2% ) , as expected given their interplay with endogenous small molecules . Moreover , we detected a marked cross-pharmacology between proximal metabolic proteins ( OR = 4 . 43 , P < 10−4 ) , where the product metabolite of the first yields the substrate of the second . To identify promising polypharmacology , we simulated the concurrent inhibition of proteins with similar cavities , and measured the impact on biomass production and metabolic cancer hallmarks , like high lactate secretion or anoxia [53] . Biomass production correlates well with proliferation rates [20] . Thus , a multiple inhibition causing larger reduction of biomass in the cancer GSMM than in the healthy one is both effective and selective . If , in addition , the decrease in biomass production cannot be achieved by silencing the individual genes , then the multiple inhibition corresponds to genuine polypharmacology . Due to the limited structural coverage of the metabolic network , and to obtain a sufficiently long list of protein combinations to screen , we applied milder constraints on the inter-similarity of cavities ( see Methods ) . In Fig 4A , we present five proteins ( GPI , DLD , PGD , SORD , and RPE ) whose simultaneous inhibition could produce a selective decrease of proliferation rates in leukemia cell lines Fig 4C . Additionally , we observed in the simulation that metabolic cancer hallmarks were reversed upon the multiple inhibition . Glucose uptake , lactate secretion , and production of reactive oxygen species ( ROS ) were reduced , while oxygen consumption was increased ( Fig 4D ) . None of the single inhibitions was able to produce such a widespread effect . It is out of the scope of this work to further explore the feasibility of simultaneously targeting these five proteins , which are mostly involved in the metabolism of carbohydrates and share chemotypes in their metabolites , like the fructose and ribulose scaffolds ( Fig 4B ) [54] . In order to discover more combinations , ideally with yet stronger cavity similarity , and to ensure the inhibitory effect of the binding event , deeper structural annotation of metabolic reconstructions will be necessary . This claim has also been raised for other types of biological networks [12] , highlighting the importance of keeping structural details in the systems era . The emergence of systems biology in the last decade has brought about new therapeutic opportunities . Among them , the idea of exploiting drug promiscuity to exert systems-level effects is particularly compelling , but its feasibility remains unclear . The few examples of intended polypharmacology are usually within protein families [4] , and , traditionally , the discovery of alternative targets has been applied to drug repositioning instead of holistic therapies [55] . The reason for this is that most target combinations are not of therapeutic value , making it very unlikely to hit interesting target profiles if only a few closely related proteins are inspected . To overcome this issue , structural biology offers a systematic means to explore the space of protein cross-pharmacology by detecting and comparing putative binding sites [16] . We have seen that proteins with similar binding pockets are scattered all over biological networks , holding promise for enabling systems pharmacology . Our analysis has uncovered a large amount of multi-target opportunities to be screened against cellular networks , and the exploratory simulations in different systems biology scenarios are very encouraging . However , there is a long agenda for drug polypharmacology . We lack full structural coverage in most biological systems [56 , 57] , sometimes missing relevant drug targets , or having only partial structures ( S5 Fig ) . Even if this gap can be diminished with homology models [58] , it is not clear whether the corresponding binding sites will be accurate enough , making it critical to develop integrative methods that are able to account for homologs from other species . Further , although structural cross-pharmacology offers a priori a good drug design scenario , it is not yet clear if the rational design of polypharmaceuticals is feasible at large: detecting similarity of cavities is only the first step of an artisanal discovery process to identify molecules that are suitable as drugs [59 , 60] , keep the right balance of potency across targets [61] and do not compromise their off-target selectivity . In this work , we have focused on protein structures , and only slightly explored the ligand side . In the light of our results , we envision that methods that combine structural and ligand information will help alleviate the intrinsic limitations of the structural viewpoint [62 , 63] . Equally critical is the dearth of methods to prioritize target combinations among the enormous pool of possibilities . Thus far , no blueprint exists to use protein interaction networks for predictive means , and more quantitative systems biology frameworks , like metabolic models , are only applicable to certain diseases . Nowadays , active research is addressing these problems . In our opinion , the discovery of multi-target therapies can be empowered by the constrained sampling of combined effectors , and it is our belief that efforts put on fast and automated protocols will be key to finally shift pharmacology to the systems level .
We collected experimental protein structures from the human interactome available in Interactome3D [58] , considering each chain separately . Then , we used BioGPS to perform the structural analysis of putative binding pockets . BioGPS has three steps: ( 1 ) cavity search using FLAPsite , ( 2 ) cavity description using GRID [64] molecular interaction field potentials , and ( 3 ) cavity comparison [14] . We performed an all-vs-all comparison of all detected cavities , and considered those cavities with a BioGPS score above 0 . 6 as ‘similar’ . This score is recommended by the BioGPS developers , and we have confirmed its validity by analyzing the enrichment of cavity pairs containing the same ligand ( S1A and S1B Fig ) , and the best compromise between precision and recall ( S1C Fig ) . To calculate the ligand-based similarity of proteins , we did an in-house implementation of SEA using molecules in ChEMBL ( v19 ) with affinity below 10 uM [34] . After analyzing background data , we found that a Tanimoto cutoff of 0 . 55 optimally fitted an extreme-value distribution instead of a Gaussian curve ( FP2 fingerprints ) . S2A–S2C Fig shows the background adjustments; E-values of ligand set similarities were calculated therefrom . We chose 10−4 as an E-value cutoff . This cutoff was used in a study to detect off-targets [32] , and we have seen that , indeed , it captures pairs of proteins containing the same ligand ( OR = 18 . 7 , P ~ 0 ) . In order to confirm that SEA is a good representative of ligand-based similarity , we performed in parallel a Naïve Bayes ( NB ) multi-target virtual screening , this time with Morgan fingerprints . This approach is , in nature , very different to SEA . Reassuringly , NB and SEA trends were strongly correlated , as it can be seen in S2D–S2F Fig . To calculate sequence similarity of proteins , we applied JackHMMER with default parameters [65] , E-value < 10−4 . As for the fold annotation of structures , we used the classification of ECOD ( January 2015 ) at level 4 of the hierarchy [66] . For the analysis of drug combinations , we considered those classified as ‘approved' or ‘clinical' in DCDB ( v2 ) [41] , excluding ‘non-efficacious’ cases . Targets from DCDB were also extracted and used for the interactome-based analysis of the combinations . Influence between pairs of proteins was determined using a pre-computed influence matrix , obtained with a PageRank-like algorithm with a default β of 0 . 5 . When more than one protein was to be compared , we parsimoniously took the pair with the highest influence as representative . Normalization for group size was achieved with 10 , 000 random groups at each size , and the mean and standard deviation of these background sets were calculated to derive a Z-influence score so that the background had a mean of 0 and a standard deviation of 1 . To analyze topological clusters , the human interactome was submitted to the overlapping cluster generator ( OCG ) [42] . Slim BP and MF annotations were obtained from GO ( January 2015 ) . In the therapy-specific networks , seeds were obtained from DrugBank ( v3 ) targets [67]; and in the tumor-specific networks drivers were fetched from IntoGen [19] . To select only those cases with a significant interconnectivity , we used the method based on percolation theory described by Menche et al . [68] , applying a significance cutoff of P < 0 . 01 on the size of the largest connected components . For cases with significant seed connectivity , we built specific networks by sequentially including non-seed ( candidate ) proteins and extracting the corresponding subgraphs from the interactome . Nodes were included following the DIAMOnD score [46] , which is again based on percolation theory . To automatically determine the number of nodes to include , we performed a 10-fold cross-validation on the seeds , i . e . we removed seeds from the seed set and checked ( 1 ) whether they were up-ranked ( recalled ) by DIAMOnD , and ( 2 ) whether their inclusion was helping in building a big connected component that gathered a large number of seeds ( Fig 3C and 3D ) . We cut at the mean point of flattening of the two recall curves . We used Hotnet ( v2 ) to measure heat propagation on the networks [49] . In a first step , Hotnet calculates an influence matrix from the network . We did so for each of the therapy- and tumor-specific networks . For each network , we optimized the β parameter of Hotnet as recommended by the authors , i . e . by maximizing the average influence at the inflection point of the cumulative distribution of level-one neighbors of nodes of random and topologically varied nodes . In most networks , β ranged from 0 . 4 to 0 . 6 . In each modulation , 1 , 000 heat units ( h . u ) were arbitrarily assigned . Therefore , in a single modulation , the target protein contained all of the initial heat , e . g . while in a three-node interference , 333 h . u . were given to each node . Qualitatively , this means that in a three-node intervention simulated inhibitions are considerably milder . The area under the cumulative heat distribution , after running Hotnet , was used to measure the global heat . This area was normalized by the ideal heat distribution , obtained by gently ( 1 , 000/n h . u . ) heating the n nodes of the network . The advantage of a combination over single interference was measured by dividing the global heat of the combination by the best global heat of the individual components . Details are given in S4 Fig . To model the effect of multiple inhibitions on leukemia metabolism , we used the PRIME metabolic models , which are based on the NCI-60 ( cancer ) and HapMap ( healthy ) cell line panels [20] . In the PRIME collection , we arbitrarily took the first leukemia cell line of the NCI-60 and the first cell line of the HapMap panels . We modeled inhibitions at the gene level by reducing to the 1% the Vmax of the corresponding reactions , with the OptKnock method[69] . Reaction rules ( AND and OR ) of the PRIME models were appropriately taken into account to translate gene inhibitions to the reaction level . In the OptKnock flux variability analysis , we focused on the biomass reaction , and also on other reactions that are representatives for metabolic cancer hallmarks , e . g . oxygen consumption or reactive oxygen species production . In the flux variability analysis of these reactions , we put as a constraint the maintenance of 80% of the wild-type biomass production .
|
Traditionally , the fact that most drugs are promiscuous binders has been a major concern in pharmacology , due to the occurrence of undesired off-target clinical events . In the recent years , however , the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature , since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks . One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites . We have massively done so for all human proteins with a known high-resolution three-dimensional structure , unveiling a vast space of ‘polypharmacology’ opportunities . Of these , we know , a great majority is not of therapeutic interest . To pinpoint promising multi-target combinations , we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"genetic",
"networks",
"protein",
"metabolism",
"protein",
"interaction",
"networks",
"systems",
"science",
"mathematics",
"network",
"analysis",
"protein",
"structure",
"pharmacology",
"drug",
"metabolism",
"computer",
"and",
"information",
"sciences",
"proteins",
"proteomics",
"molecular",
"biology",
"pharmacokinetics",
"drug",
"discovery",
"systems",
"biology",
"biochemistry",
"drug",
"research",
"and",
"development",
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"metabolism",
"macromolecular",
"structure",
"analysis"
] |
2017
|
Detecting similar binding pockets to enable systems polypharmacology
|
Inflammatory bowel diseases ( IBD ) are chronic inflammatory disorders of the gastrointestinal tract , strongly associated with an increased risk of colorectal cancer development . Parasitic infections caused by helminths have been shown to modulate the host’s immune response by releasing immunomodulatory molecules and inducing regulatory T cells ( Tregs ) . This immunosuppressive state provoked in the host has been considered as a novel and promising approach to treat IBD patients and alleviate acute intestinal inflammation . On the contrary , specific parasite infections are well known to be directly linked to carcinogenesis . Whether a helminth infection interferes with the development of colitis-associated colon cancer ( CAC ) is not yet known . In the present study , we demonstrate that the treatment of mice with the intestinal helminth Heligmosomoides polygyrus at the onset of tumor progression in a mouse model of CAC does not alter tumor growth and distribution . In contrast , H . polygyrus infection in the early inflammatory phase of CAC strengthens the inflammatory response and significantly boosts tumor development . Here , H . polygyrus infection was accompanied by long-lasting alterations in the colonic immune cell compartment , with reduced frequencies of colonic CD8+ effector T cells . Moreover , H . polygyrus infection in the course of dextran sulfate sodium ( DSS ) mediated colitis significantly exacerbates intestinal inflammation by amplifying the release of colonic IL-6 and CXCL1 . Thus , our findings indicate that the therapeutic application of helminths during CAC might have tumor-promoting effects and therefore should be well-considered .
A close relationship between inflammation and tumor development has been described for numerous human cancers over the last years [1] . In terms of inflammatory bowel diseases ( IBD ) such as ulcerative colitis ( UC ) , it has been clearly demonstrated that patients are predisposed to develop colorectal cancer [2] . The pathology of IBD is thought to be caused by either genetic susceptibility , environmental influences , infectious microbes , or dysregulated intestinal immune responses . Due to one or more of these factors , the tolerance towards dietary antigens and the commensal microbiota in the gastrointestinal tract are perturbed . Excessive inflammation is initiated by innate immune cells , but further pathology is driven by a prevalent activation of T helper cells ( Th ) 1 , Th2 or Th17 cells [3] . While studying mouse models of intestinal inflammation , regulatory T cells ( Tregs ) were shown to play a critical role in maintaining mucosal homoeostasis . As they exert a variety of suppressive functions Tregs are able to prevent aberrant activation of intestinal immune responses [4] . In the murine model of colitis-associated colon cancer ( CAC ) , CD4+ Foxp3+ Tregs are crucial for the control of the inflammatory process . However , during tumor progression , highly activated Tregs accumulate within colonic tumors and suppress CD8+ T cell antitumor responses effectively [5] . Since the etiology of IBD is still unknown and causative therapies aren’t available , the patients`exposure to helminths appeared to be a novel and promising approach in the treatment of colitis . During helminth infection , pronounced Th2 immune responses as well as an activation of B cells , basophils , mast cells , dendritic cells , and eosinophils are evoked in the host to control and expel the parasites . By the expansion of regulatory cells such as alternatively activated macrophages , CD4+ and CD8+ Tregs or regulatory B cells , and the consequent induction of anti-inflammatory cytokines , e . g . IL-10 or TGF-β , helminths constitute immunoregulatory conditions to ensure their survival [6] . This immunomodulatory state was suggested to limit intestinal inflammation in IBD . However , utilization of helminths in different human studies and animal experiments of colitis highlighted controversial results . First results of clinical trials of Trichuris suis ova ( TSO ) therapy in UC and Crohn`s disease patients showed a reduction of the disease activity index [7 , 8] . In retrospect , evaluation of completed studies did not illustrate any significant beneficial effect of oral TSO application on clinical response or remission in Crohn’s disease or UC patients ( ClinicalTrials . gov Identifier: NCT01433471 , NCT01576471 , NCT01953354 ) . Therefore , all clinical trials were either terminated or completed without publication of final results . In contrast , murine models of H . polygyrus infection during DSS [9] or T cell transfer colitis [10] as well as the application of Hymenolepis diminuta in dinitrobenzene sulfonic acid induced colitis [11] illustrated an amelioration of the intestinal inflammation . In almost all studies , efficacy of treatment was accompanied by an expansion of Foxp3+ Tregs or a production of IL-10 . Nevertheless , adverse effects of helminth application were also shown in different mouse models . Disease severity was enhanced when mice were infected with H . diminuta in oxazolone-induced colitis [12 , 13] , or after Trichuris muris infection of genetic susceptible mice which develop colitis spontaneously [14] . Moreover , H . polygyrus infection in a mouse model of bacterial colitis was shown to exacerbate intestinal inflammation by inducing alterations of the colonic microbiota [15] . Regardless of IBD , infections are known to account for many malignancies , including cancer , as they can induce chronic inflammation , genomic instability , inhibition of apoptosis or tumor suppressors , and modulation of cell proliferation , angiogenesis or glucose metabolism . With respect to helminths , data indicated that at least blood and liver flukes can be responsible for carcinogenesis . Schistosoma haematobium infections could be clearly linked to bladder cancer , and Chlonorchis sinensis or Opisthorchis viverrini infections have been shown to cause cholangiocarcinoma and bile duct cancer [16 , 17] . Mechanisms that facilitate carcinogenesis in O . viverrini infections are the production of reactive oxygen intermediates which induce chronic inflammation and the release of proteins which promote cell proliferation and interfere with DNA repair and apoptosis pathways [18] . Whether an association between helminths other than flukes or schistosomes and cancer exists , remains unclear . For intestinal nematodes , like T . suis , which have already been applied to IBD patients , not much is known about their potential to exert long-term effects in the host . Given that helminths potently suppress inflammation through versatile immunoregulatory mechanisms but simultaneously may facilitate carcinogenesis , we aimed to unravel the apparently controversial function of helminth infection in a mouse model of colitis-associated colon cancer . Thereby , we focused on the distribution and phenotype of Foxp3+ Tregs and CD8+ T cells which play an important role in tumor immunity .
Intestinal infections with the parasitic nematode H . polygyrus are known to expand CD4+ Foxp3+ Tregs [19] and lead to chronic inflammation in susceptible mouse strains; two features that are considered to promote carcinogenesis in colitis-associated colon cancer [5] . Before we applied the intestinal nematode H . polygyrus in the AOM/DSS colon cancer mouse model , we tested whether helminth infection alone might modulate the host immune response distal to the site of infection , particularly in the colon . Fourteen days after infection of BALB/c mice with 200 third-stage larvae of H . polygyrus , we did not observe any mucosal damage but a slight induction of smooth muscle thickening in the colon ( Fig 1A ) . This was in line with a minimal reduction of the colon length between 7 and 14 days post infection ( Fig 1C ) . As expected , in the small intestine , where H . polygyrus larvae encyst , molt and reemerge as adult worms to the gut lumen , severe goblet cell hyperplasia was detected by day 14 post infection ( Fig 1A and 1B ) . Intestinal helminth infections are considered to modulate CD4+ T cell subsets [20] . Thus , we further analyzed the T cell compartment of the mesenteric lymph nodes ( mLNs ) and colonic lamina propria ( LP ) at different time points after H . polygyrus infection . Besides a considerable expansion of CD4+ Foxp3+ Tregs in the mLN of infected mice , most prominent by day 10 post infection , we also detected increased frequencies of Tregs in the LP during the course of infection ( Fig 1D ) . To explore the phenotype of these cells , we determined the expression of the integrin αE ( CD103 ) on CD4+ Foxp3+ Tregs from naïve mice and mice infected with H . polygyrus as CD103+ Tregs were shown to represent an effector/memory phenotype [21] . The CD103 expression was significantly elevated on Tregs isolated from the colon already 7 days post infection and showed the highest increase at day 10 ( Fig 1E ) . To determine whether this activated phenotype is correlated with a higher functional activity , we isolated CD4+ Foxp3+ Tregs from mLN at day 10 post infection and cultured them with CD4+ responder T cells . Indeed , Tregs isolated from H . polygyrus infected mice were more potent in the suppression of responder T cell proliferation than CD4+ Foxp3+ Tregs from naïve animals ( Fig 1F ) . We previously demonstrated that CD4+ Foxp3+ Tregs elicit tumor-promoting functions during CAC in mice . More precisely , CD4+ Tregs were shown to be involved in the suppression of an effective CD8+ T cell-mediated antitumor response [5] . To investigate whether the alterations in Treg frequencies , which are provoked by H . polygyrus infection , might have an impact on the development of CAC , we infected BALB/c mice with 200 third-stage larvae of H . polygyrus at week 8 of the AOM/DSS regimen ( Fig 2A ) . At this time point , the basis for adenocarcinomas had been created and tumor growth set in . As seen in Fig 2B , H . polygyrus infection at week 8 did not prejudice the weight course of the mice which is only dictated by the administration of DSS via the drinking water . When tumor development was monitored by endoscopy at week 12 , no differences in the frequencies or sizes of adenocarcinomas as indicated by the tumor score were observed ( Fig 2C ) . Due to the tumor growth , the colon weight to length ratio was increased in AOM/DSS treated mice . However , additional H . polygyrus infection had no further influence on the carcinogenesis ( Fig 2D ) . By increasing the number of T cells expressing Foxp3 in the colon , H . polygyrus infection was shown to protect mice in some models of experimental colitis [10 , 22] . On the contrary , helminth infections themselves modulate their host´s immune system , which may favor tumor development . In order to examine the influence of H . polygyrus infection on DSS-induced inflammation during CAC we infected BALB/c mice with 200 third-stage larvae of H . polygyrus one day after AOM injection , 6 days before the first DSS administration , respectively ( Fig 3A ) . Interestingly , we observed a significantly accelerated loss of body weight during the first DSS cycle in mice infected with H . polygyrus , indicating more severe intestinal inflammation ( Fig 3B ) . Colonoscopy at week 12 revealed that mice infected with H . polygyrus before the first DSS administration exhibited a significantly higher tumor score compared to AOM/DSS treated mice ( Fig 3C ) . Augmented tumor formation was accompanied by a significant gain of colon weight to length ratios in H . polygyrus infected CAC mice compared to non-infected CAC mice ( Fig 3D ) . Due to the increased frequencies of Tregs in the colonic lamina propia during the course of infection , we speculated whether alterations in the colonic cellular composition might be responsible for the enhanced tumor development after H . polygyrus infection . As expected , mice suffering from CAC showed a strong accumulation of Foxp3+ Tregs in the colon ( Fig 3E and 3F ) . However , no additive effect was observed when mice were infected with H . polygyrus . In contrast , absolute numbers of Foxp3+ Tregs was significantly decreased in the colon of H . polygyrus infected CAC mice compared to CAC mice ( Fig 3E and 3F ) . This could be ascribed to the overall reduction of cells in the colon of H . polygyrus infected CAC mice as also the frequencies and absolute numbers of CD8+ effector T cells were significantly diminished in the colon of infected CAC mice , although the initial H . polygyrus infection was induced 12 weeks earlier ( Fig 3G ) . In summary , these data show that H . polygyrus infection seems to have a long-lasting impact on the anti-tumor immune response , but the increase in the tumor score after H . polygyrus is not dependent on an enhanced Treg frequency . Interestingly , we observed that mice subjected to AOM/DSS treatment and H . polygyrus infection before the first DSS administration , exhibited a stronger loss of body weight compared to AOM/DSS treated mice during the first DSS cycle ( Fig 3B ) . This observation indicated a more severe intestinal inflammation in H . polygyrus infected CAC mice , which was shown to enhance tumor formation in this experimental mouse model [23 , 24] . To elucidate the magnitude of inflammation , we infected BALB/c mice with 200 third-stage larvae of H . polygyrus 6 days before administration of 2% DSS in the drinking water ( Fig 4A ) . In accordance with our hypothesis , H . polygyrus infected DSS treated mice started to loose body weight earlier and had significantly reduced body weights at day 14 when compared to DSS only treated mice ( Fig 4B ) . The determination of the disease activity index , which consists of body weight loss , stool consistency and rectal bleeding , confirmed that H . polygyrus infected mice are significantly more sensitive to DSS-induced intestinal inflammation than non-infected mice ( Fig 4C ) . Increased colonic inflammation in H . polygyrus infected DSS treated mice was associated with a significant reduction in colon length and enhanced pathology characterized by more severe loss of the entire crypt structure , surface epithelial cell erosion , goblet cell depletion and massive leukocyte infiltration compared to DSS only treated mice ( Fig 4D and 4E ) . Well in line , the production of IL-6 and CXCL1 was significantly enhanced in the colon of infected DSS treated mice compared to non-infected DSS treated mice ( Fig 4F ) . In contrast to the pathological changes in the colon , no signs of DSS-induced pathology were observed in the small intestine , but H . polygyrus mediated goblet cell hyperplasia was present ( S1A and S1B Fig ) . Cytokine and chemokine production in intestinal explant cultures from the ileum confirmed that the DSS administration did not alter constitutive IL-6 or CXCL1 expression in the small intestine . However , H . polygyrus infected and DSS treated mice produced significantly lower levels of IL-6 and CXCL1 compared to DSS treated mice , suggesting an at least partly immunoregulatory environment in the small intestine ( S1C Fig ) . Determining the cellular composition in the colonic lamina propria we detected the highest frequency of CD4+ T cells in H . polygyrus infected DSS treated mice ( Fig 5A ) . The strong expression of the proliferation marker Ki67 and the downregulation of CD62L on CD4+ Foxp3- T cells in the colon of H . polygyrus infected DSS treated mice reflected a significantly enhanced activation of CD4+ Foxp3- T cells which was independent of DSS application ( Fig 5B ) . Interestingly , the frequency of CD8+ T cells was significantly reduced in the colon of H . polygyrus infected mice but not in DSS treated infected mice ( Fig 5A ) . Finally , the frequency of Foxp3+ Tregs was increased in the colon of H . polygyrus infected mice , which was further enhanced by DSS-treatment ( Fig 5C ) . In summary , we conclude that H . polygyrus infection prior to DSS-induced colitis further enhances the inflammatory response in the colon based on enhanced IL-6 and CXCL1 expression and increased CD4+ T cell activation . As these results are unexpected and in part in contrast to the published literature we made use of two other mouse models for colonic inflammation to validate our results . We used the RAG2-/- T cell transfer colitis model [25] and the VILLIN-HA T cell transfer model [26] . Based on our experimental set up , mice were infected with H . polygyrus before T cell transfer and the pathology in the colon was analyzed . Well in line with the results obtained in the DSS colitis model , we absolutely not observed any improvement in the disease outcome after H . polygyrus infection ( S2A–S2E Fig ) . In contrast , H . polygyrus pre-infection in the RAG2-/- T cell transfer colitis model seems to induce IL-6 and CXCL1 in the colon in the same manner as in the DSS model ( S2C Fig ) . Pro-inflammatory cytokines like IL-6 have important implications in driving intestinal inflammation [27] . Moreover , IL-6 is a critical tumor promoter during early tumorigenesis in the CAC mouse model [28] . To analyze if the helminth mediated increase in IL-6 and CXCL1 promoted the increased disease severity of DSS-induced colitis , we infected BALB/c mice with 200 third-stage larvae of H . polygyrus 6 days before DSS administration . In addition , mice were rectally treated with siRNA-loaded nanoparticles directed against CXCL1 and IL-6 during DSS application ( Fig 6A ) and the disease activity index was monitored . Importantly , inhibition of H . polygyrus mediated IL-6 and CXCL1 expression by siRNA significantly reduced the disease activity index to the level of DSS only treated mice ( Fig 6B ) . Less severe inflammation was confirmed in colonic tissue sections as infected mice treated with siRNA-loaded nanoparticles showed a more preserved colonic structure compared to non- siRNA-treated mice ( Fig 6C ) . Efficient gene silencing of IL-6 and CXCL1 was determined in the supernatant of colon explant cultures ( Fig 6D ) . Our results clearly underline that H . polygyrus infection mediated colonic IL-6 and CXCL1 release facilitates aggravation of DSS-induced colitis . Our results definitely indicated that H . polygyrus infection facilitates intestinal inflammation in the AOM/DSS model . However , it is unclear whether intestinal inflammation or the immunosuppressive environment during colon cancer impacts the anti-helminth immune response and thereby alters the course of infection . In order to clarify this issue , we measured the fecal egg counts weekly in H . polygyrus infected mice and in mice treated with the AOM/DSS regime after H . polygyrus infection over the course of the experiment ( Fig 7A ) . Interestingly , helminths persisted longer in mice suffering from intestinal inflammation and colon cancer than in naïve mice . At week 10 , only 15% of CAC mice had recovered from infection while 50% of naïve mice showed expulsion of the worms ( Fig 7B ) . Subsequent cytokine and chemokine analysis revealed that H . polygyrus infection in naïve mice elicited a Th2 response in the colon , indicated by a strong IL-4 release , important for the clearance of the parasites . In line with delayed clearance , H . polygyrus infected mice suffering from colon cancer produced significantly less IL-4 in response to the parasites ( Fig 7C ) . Of note , H . polygyrus infection promoted a long term up-regulation of IL-6 and CXCL1 expression in naïve mice which was still detectable 10 weeks after initial infection . However , the immunosuppressive environment of tumors in AOM/DSS treated mice completely abolished the expression IL-6 and CXCL1 ( Fig 7D ) , suggesting that colon cancer also affects the helminth-specific immune response . Taken together , we show that H . polygyrus infection can amplify intestinal inflammation and induces long-lasting alterations in the colonic immune cell compartment , thereby increasing the risk to develop colitis-associated colon cancer .
Carcinogenesis in colitis-associated colon cancer is clearly driven by inflammation [1] . Therefore , controlling the inflammatory immune response is of major interest in IBD patients . Great effort has been done to find alternative drugs which do not solely alleviate the symptoms but interfere with the innate or adaptive immune response to restore intestinal homeostasis . Parasitic helminth infections became a subject of murine and human studies in IBD as they release immunomodulatory molecules that might reduce intestinal inflammation . However , systematic parasite infections in patients should be well-considered as they have been shown causative for different types of cancers [17] . In developing countries , autoimmune diseases such as IBD may be rare , but soil-transmitted helminth infections affect approximately one-third of the population [29] , and 22 . 9% of cancers diagnosed were attributable to infections [30] . With regard to parasitic intestinal infections elicited by nematodes , almost nothing is known about their ability to cause cancer . In the present study , we addressed the impact of H . polygyrus infection in the context of colitis-associated colon cancer ( CAC ) . Importantly , we could show that , dependent on the time point of helminth infection , tumor development was strongly promoted . In contrast to our hypothesis , this effect was not due to a long-term increase in the frequency of immunosuppressive Foxp3+ Tregs in the colon , which are induced during H . polygyrus infection . We identified that infection with H . polygyrus in the early phase of CAC , before DSS-induced inflammation , accelerates the inflammatory immune response in the colon , leading to severe pathology , which were shown to enhance tumor development in this model [23 , 24] . This is in contrast to other studies , which describe a protective role of helminths in different mouse models of IBD [31–33] . To validate our own data , we expanded our studies from the chemical induced IBD model to two T cells mediated IBD mouse model . Most important , in none of our model systems the pre-infection with H . polygyrus resulted in a protective effect and reduced inflammation in the colon . Therefore , the discrepancy to former studies must be dependent on other reasons e . g . in the way of application , the time point of infection or the nematodes that were used . In detail , attenuation of DSS-mediated inflammation was found when extracts or excretory/secretory products of helminths were applied [34 , 35] . Furthermore , the H . polygyrus-mediated alleviation of colitis in the RAG IL-10−/− T cell transfer colitis was observed , when H . polygyrus infected mice were dewormed before T cell transfer colitis [31] . These data strongly suggest that helminth products can be considered anti-inflammatory , rather than the helminths itself . Only one study described a protection against DSS colitis after infection with H . polygyrus larvae [9] . However , the authors infected the mice in the course of DSS treatment and analyzed the clinical outcome 5 days post infection . In contrast , we determined the clinical outcome at later time points , as we measured the inflammatory score two weeks after infection , and in the T cell-mediated colitis models we looked 3 weeks and 9 weeks , respectively , after initial infection . Therefore , the long-term effect of H . polygyrus infection seems to be different from the short-term effect , but must be strongly considered in this context . In line with our results , experiments with H . diminuta infection in oxazolone colitis demonstrated also an enhanced inflammatory response [12 , 13] . Moreover , in a very recent study analyzing the impact of H . polygyrus pre-infection on bacterial colitis the authors showed that the infection with H . polygyrus led to significant changes in the microbiota composition which resulted in exacerbation of intestinal inflammation [15] . Whether alterations in the microbiota account for the increase in pro-inflammatory mediators in the colon and the boost of colitis severity needs further investigation . During DSS-induced colitis , neither the presence of functional T cells , B cells and NK cells , nor commensal bacteria are crucial for the initiation of inflammatory processes [36 , 37] . Nevertheless , in specific-pathogen-free ( SPF ) mice the disruption of the epithelial barrier by DSS leads to the activation of innate immune cells that sense commensal bacteria and initiate inflammation . CD4+ T cells are known to accumulate in the intestine during IBD [3 , 38] , and during DSS colitis , T cells that are specific against oral antigens develop [39] . In the current study , we attempted to identify the influence of H . polygyrus infection on the immunological response in DSS-induced colitis . Interestingly , we could not observed difference in the frequency of Tregs between H . polygyrus infected and non-infected mice suffering from inflammation , but the activation and proliferation of colonic CD4+ effector T cells was only observed when DSS treated mice were infected with H . polygyrus . DSS colitis itself did not expand or activate CD4+ T cells to a significant extent . In addition , high level of pro-inflammatory IL-6 and CXCL1 were produced in the colon of H . polygyrus DSS treated mice . In IBD patients IL-6 serves as a prognostic marker as increased serum levels correlate with enhanced clinical disease activity . In this context , IL-6 acts as a pro-inflammatory mediator that contributes to enhanced T cell survival and resistance to apoptosis by inducing STAT3 activation [40 , 41] . Furthermore , IL-6 facilitates the expansion of Th17 cells which are IL-6 producers themselves and may have a pathogenic role in intestinal inflammation [42] . CXCL1 is highly expressed in the colonic mucosa of IBD patients and its pathophysiological role could be shown in mice lacking its receptor CXCR2 . These mice displayed amelioration of DSS colitis accompanied by reduced infiltration of leucocytes to the colon [43] . Well in line , we show that elevated levels of IL-6 and CXCL1 are causative to H . polygyrus induced amplification of intestinal inflammation as the inhibition of IL-6 and CXCL1 expression by siRNA functionalized nanoparticles significantly reduced the disease activity in H . polygyrus infected DSS treated mice . Exacerbated inflammation is well known to promote tumor formation . In this context , proliferation of tumor-initiating cells is enhanced by IL-6 while normal and premalignant intestinal epithelial cells are protected from apoptosis [28] . Furthermore , the chemokine CXCL1 was demonstrated to promote angiogenesis in colorectal cancer [44] . Our data demonstrate that an augmented inflammation can be linked to a considerable increase of tumor growth . We have already shown that in the murine CAC model CD8+ cytotoxic T cells are crucial for the adaptive anti-tumor immune responses , while Foxp3+ Tregs facilitate immunosuppression [5] . Interestingly , H . polygyrus infection during CAC development induced a long-lasting reduction of CD8+ T cell frequencies in the colon . To our knowledge , our study is the first to describe an association of H . polygyrus infection and a prolonged CD8+ T cell reduction in the context of carcinogenesis . However , our findings are well in line with observations from murine and human helminth co-infection models . Helminth infection in patients suffering from tuberculosis or application of T . suis eggs in multiple sclerosis patients led to a decrease of CD8+ T cells in the peripheral blood [45 , 46] . In mice infected with Ascaris sp . and Vaccinia virus , where CD8+ T cell responses are critical for host protection , decreased frequencies of CD8+ T cells and an increase of mortality were detected [47] . For H . polygyrus infection , it could be shown that CD8+ T-cell responses against T . gondii were suppressed and could not be restored [48] . When CD8+ effector T cells were adoptively transferred in H . polygyrus infected mice , their numbers and memory development were negatively impacted [49] . How precisely helminth infections modulate CD8+ T cells responses , needs to be further investigated . H . polygyrus infection induces strong Th2 immune responses [50] . An increase of IL-4 producing CD4+ T cells could be detected in the colon several weeks after H . polygyrus infection . Remarkably , establishment of CAC significantly reduced the IL-4 response of CD4+ T cells after helminth infection which was accompanied by a prolonged survival of the worms . An altered cytokine milieu during intestinal inflammation might therefore impact immunogenicity of the helminths . This could be confirmed in a study where DSS administration was shown to enhance the adaptation and establishment of H . polygyrus indicated by an elevated egg production , increased worm length and altered distribution of larvae in the small intestine [51] . Cytokines measured in colon explant cultures from long-term H . polygyrus only infected mice showed a trend towards elevated IL-6 and CXCL1 levels ( Fig 7D ) . In contrast , CAC induction after H . polygyrus infection significantly reduced production of IL-6 and CXCL1 in the colon compared to naïve infected mice . These results seem to be surprising as one would expect that tumor progression in infected CAC mice is connected to higher levels of IL-6 and CXCL1 . However , it is possible that these pro-inflammatory mediators promote inflammation at early stages of CAC development and this is sufficient to enhance carcinogenesis in a long-term perspective . In conclusion , we have shown that H . polygyrus infection in the context of DSS-induced colitis and CAC did not ameliorate colonic inflammation but activates intestinal immunity that facilitates tumor development . The data suggest that H . polygyrus infection not only interferes with CD4+ T cell responses but directly impacts on CD8+ effector T cells . Thus , mechanisms of helminths immune modulation should be defined precisely before being applied in autoimmune diseases like IBD .
All animal experiments were performed in accordance with institutional , state , and federal guidelines ( approved by the Landesamt fuer Natur , Umwelt und Verbraucherschutz North Rhine-Westphalia , Germany; reference number: 84–02 . 04 . 2014 . A243 and 87–51 . 04 . 2010 . A163 ) . Six to eight week old female BALB/c mice were purchased from Envigo ( Rossdorf , Germany ) . Female C . Cg-Foxp3tm2Tch/J ( termed Foxp3/eGFP ) mice and CBy . PL ( B6 ) -Thy1a/ScrJ ( termed Thy1 . 1 ) mice ( The Jackson Laboratory , Bar Harbor , ME ) were bred in-house and used at an age of six to ten weeks . Animals were housed under specific pathogen-free conditions in the Laboratory Animal Facility of the University Hospital Essen . To induce CAC , mice were injected intraperitoneally with a single dose of azoxymethane ( AOM , 12 . 5 mg/kg of body weight; Sigma-Aldrich , Munich , Germany ) , followed by 3 cycles of 3% dextran sulfate sodium salt ( DSS , MP Biomedicals , Eschwege , Germany; MW , 36–50 kDa ) given via the drinking water for 5 to 7 days . Due to the enhanced susceptibility of H . polygyrus infected mice to DSS treatment , these mice were treated with 2% DSS in the first cycle . Ten to twelve weeks after AOM injection , tumor distribution in the distal part of the colon was determined by murine colonoscopy [52] , and tumor scores were calculated . In brief , mice were anesthetized by intraperitoneal injection of ketamine/xylazine , and a rigid endoscope ( Coloview miniendoscopic system , Karl Storz , Tuttlingen , Germany ) was inserted as far as possible into the rectum under visual control . Endoscopies were recorded while the endoscope was slowly withdrawn , starting at the flexure and stopping at the anus . Tumor sizes were graded on a scale of 1–5 according to the following grades: grade 1 ( very small but detectable tumor ) , grade 2 ( tumor covering up to one eighth of the colonic circumference ) , grade 3 ( tumor covering up to a quarter of the colonic circumference ) , grade 4 ( tumor covering up to half of the colonic circumference ) , and grade 5 ( tumor covering more than half of the colonic circumference ) . Tumor scores per mouse were calculated by summing up the sizes/grades of all the tumors in a given mouse . Mice received 2% DSS ( MP Biomedicals ) in drinking water for 7 days . DSS was replaced by normal drinking water and mice were sacrificed one day later . Some of the mice were infected with 200 L3 H . polygyrus 6 days before DSS treatment . To determine disease activity indexes ( DAI , 0–12 ) we used a validated scoring system; DAI consisted of loss of body weight ( 1 , 1–5%; 2 , 6–10%; 3 , 11–15%; 4 , 16–20% ) , rectal bleeding ( 0 , no blood; 2 , blood visible; 4 , gross bleeding ) and stool consistency ( 0 , normal; 2 , loose stool; 4 , diarrhea ) [53] . CaP-PLGA Nanoparticles functionalized with siRNA directed against IL-6 and CXCL1 were prepared as described previously [54] . In brief , single shell nanoparticles were synthesized by fast mixing equal amounts of calcium-l-lactate and diammonium hydrogen phosphate . Instantly after mixing , the calcium phosphate dispersion was mixed with solutions of siRNA ( 4 mg mL− 1 , GE healthcare life sciences , Chalfont St . Giles , UK ) to functionalize the particles . To encapsulate the calcium phosphate nanoparticles into the biodegradable polymer poly ( d , l-lactide-co-glycolide ) ( PLGA , Resomer RG 502 H , Evonik Industries , Darmstadt , Germany ) , a water-in-oil-in-water double emulsion solvent evaporation method was applied . The dispersed polyvinylalcohol ( PVA ) -coated nanoparticles were shock-frozen in liquid nitrogen and lyophilized . Nanoparticles ( 8 μg of each siRNA per application ) were administered intrarectally into mice sedated with low amounts of isoflurane using a small catheter . Mice were treated from day 1 onwards , after the start of 2% DSS treatment . The severity of colitis was assessed by disease activity index . Heligmosomoides polygyrus life cycle and production of larvae were conducted at the University of Edinburgh as described elsewhere [55] . BALB/c mice were infected with 200 third-stage ( L3 ) larvae by oral gavage either at week 0 ( 6 days before the first DSS application ) or at week 8 ( 6 days after the last DSS application ) of CAC induction . Fecal egg counts were determined according to a standard protocol [56] . Small intestines and colons were prepared and rinsed with PBS . Approximately 1 cm of the distal colon and the ileum were embedded in paraffin . Tissue sections of 4 μm were stained with hematoxylin and eosin ( H&E ) and periodic acid Schiff ( PAS ) to demonstrate mucopolysaccharides . Goblet cells were enumerated and referred to the length of the villi . After the preparation of the small intestines or colons , they were rinsed with PBS and cut open longitudinally . A small explant from the distal part of the colon or the ileum was taken and the weight was determined ( around 10–20 mg ) . The biopsies were cultured for 6 h in 300 μl of RPMI ( Invitrogen ) supplemented with 10% heat-inactivated FCS , 25 mmol/L HEPES ( both Biochrom , Berlin , Germany ) , 100 U/mL penicillin , and 0 . 1 mg/mL streptomycin ( both Sigma-Aldrich ) ( complete media ) . Cytokine levels in the supernatants were measured by Polystyrene bead-based Luminex Assay ( R&D Systems , Abingdon , UK ) . The assay was run on a Luminex 200 instrument . Cytokine concentrations were calculated using the Luminex IS software ( Luminex Corporation , Austin , TX ) , and were normalized to the respective colon/ileum weight . Single-cell suspensions from mesenteric lymph nodes ( mLNs ) were prepared by meshing the lymph nodes through a 70 μm cell strainer and washing with PBS containing 2 mM EDTA and 2% FCS . Lamina propria lymphocytes ( LPLs ) were isolated as described previously [5] . In brief , tissue pieces of the colon were washed in PBS containing 3 mM EDTA . EDTA was removed by washing the tissue with RPMI containing 1% FCS , 1 mM EGTA , and 1 . 5 mM MgCl2 . Single cells were obtained by digesting the tissue in RPMI containing 20% FCS and 100 U/mL collagenase ( Clostridium histolyticum , Sigma-Aldrich , St . Louis , MO ) at 37°C for 1 h with subsequent filtration through 40 μm and 30 μm cell strainer . For flow cytometry analysis , single cells were incubated with fluorochrome-labeled antibodies against CD4 ( RM4-5 or H129 . 19; BD Biosciences , Heidelberg , Germany ) , CD8 ( 53–6 . 7; BD Biosciences ) , CD62L ( MEL-14; BD Biosciences ) , CD90 . 1 ( OX-7; BD Biosciences ) , Ki67 ( SolA15; eBioscience ) , IL-4 ( 11B11; BD Biosciences ) , and Foxp3 ( FJK-16s; eBioscience ) . The Foxp3 staining kit from eBioscience was used according to the manufacturer‘s recommendations to stain Foxp3 and Ki67 intracellularly . To assess IL-4 , cells were cultured for 4 h with 10 ng/mL PMA and 1 μg/mL ionomycin in the presence of 5 μg/mL Brefeldin A ( all Sigma-Aldrich ) in complete media . After surface staining , cells were fixed with 2% paraformaldehyde , permeabilized with 0 . 1% NP-40 , and stained with antibodies against IL-4 . Cells were acquired with a LSR II instrument and analyzed using DIVA software ( both from BD Biosciences ) . CD4+Foxp3+ ( eGFP+ ) Tregs were sort purified from mLNs of naïve Foxp3/eGFP reporter mice and Foxp3/eGFP mice infected with H . polygyrus for 10 days using a FACSAria II cell sorter ( BD Biosciences ) . Using the CD4+ T-cell isolation kit II ( Miltenyi Biotec , Bergisch-Gladbach , Germany ) , CD4+ responder T cells were enriched from spleens of naïve Thy1 . 1 mice . CD4+ responder T cells were labeled with eFluor670 ( eBioscience ) . 1×105 CD4+ responder T cells were either cultured alone or co-cultured with CD4+Foxp3+ ( eGFP+ ) Tregs ( 1×105 ) for 3 days in complete media in the presence of 1 μg/mL anti-CD3 ( 2C11; BD Biosciences ) and irradiated splenocytes from naïve BALB/c mice , which served as antigen-presenting cells ( APCs ) ( 3×105 ) . Data were expressed as mean ± SEM . Results were tested for normal distribution using Kolmogorov-Smirnov or D'Agostino & Pearson omnibus normality test . Where appropriate , the unpaired Student t test or Mann Whitney test was used to compare two groups . Differences between means of more than two groups were assessed using one-way ANOVA followed by Tukey`s or Dunn's Multiple Comparison Test . Statistical significance between two groups at different time points was calculated using two-way ANOVA followed by Bonferroni posttests . Kaplan-Meier plots were used to analyze recovery of H . polygyrus infected mice . Comparisons of survival curves were made using the log-rank ( Mantel-Cox ) test . Statistical significance was set at the level of p<0 . 05 . All analyses were calculated using the GraphPad Prism 7 . 03 software ( La Jolla , CA ) .
|
Evidence from epidemiological studies indicates an inverse correlation between the incidence of certain immune-mediated diseases , including inflammatory bowel diseases , and exposure to helminths . As a consequence , helminth parasites were tested for treating IBD patients , resulting in clinical amelioration of the disease due to the induction of an immunosuppressive microenvironment . However , some infection–related cancers can be attributed to helminth infection , probably due to the generation of a microenvironment that might be conductive to the initiation and development of cancer . In the present study , we aimed to unravel the apparently controversial function of helminths in a mouse model of colitis-associated colon cancer . We show that helminth infection in the onset of colitis and colitis-associated colon cancer does not ameliorate colonic inflammation but activates intestinal immune cells that further facilitate tumor development . Therefore , a better understanding of mechanisms by which helminths modulate host immune responses in the gut should be defined precisely before application of helminths in autoimmune diseases like IBD .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"immunology",
"parasitic",
"diseases",
"animal",
"models",
"colitis",
"model",
"organisms",
"signs",
"and",
"symptoms",
"gastroenterology",
"and",
"hepatology",
"experimental",
"organism",
"systems",
"inflammatory",
"bowel",
"disease",
"digestive",
"system",
"research",
"and",
"analysis",
"methods",
"white",
"blood",
"cells",
"inflammation",
"animal",
"cells",
"t",
"cells",
"mouse",
"models",
"immune",
"response",
"gastrointestinal",
"tract",
"helminth",
"infections",
"diagnostic",
"medicine",
"anatomy",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"regulatory",
"t",
"cells",
"colon"
] |
2017
|
Intestinal helminth infection drives carcinogenesis in colitis-associated colon cancer
|
Dominant mutations in the alpha-B crystallin ( CryAB ) gene are responsible for a number of inherited human disorders , including cardiomyopathy , skeletal muscle myopathy , and cataracts . The cellular mechanisms of disease pathology for these disorders are not well understood . Among recent advances is that the disease state can be linked to a disturbance in the oxidation/reduction environment of the cell . In a mouse model , cardiomyopathy caused by the dominant CryABR120G missense mutation was suppressed by mutation of the gene that encodes glucose 6-phosphate dehydrogenase ( G6PD ) , one of the cell's primary sources of reducing equivalents in the form of NADPH . Here , we report the development of a Drosophila model for cellular dysfunction caused by this CryAB mutation . With this model , we confirmed the link between G6PD and mutant CryAB pathology by finding that reduction of G6PD expression suppressed the phenotype while overexpression enhanced it . Moreover , we find that expression of mutant CryAB in the Drosophila heart impaired cardiac function and increased heart tube dimensions , similar to the effects produced in mice and humans , and that reduction of G6PD ameliorated these effects . Finally , to determine whether CryAB pathology responds generally to NADPH levels we tested mutants or RNAi-mediated knockdowns of phosphogluconate dehydrogenase ( PGD ) , isocitrate dehydrogenase ( IDH ) , and malic enzyme ( MEN ) , the other major enzymatic sources of NADPH , and we found that all are capable of suppressing CryABR120G pathology , confirming the link between NADP/H metabolism and CryAB .
The maintenance and integrity of specialized functional structures such as sarcomeres , the basic unit of contractile force in striated muscles , are inextricably linked to the cellular machinery of molecular chaperones and protein quality control pathways . Evidence for this notion is provided by the identification of myopathic mutations in genes that encode proteins with chaperone function , such as CryAB and Bag3 , and whose products have been localized to Z-discs . Moreover , an increasing number of genes encoding Z-disc associated proteins , such as desmin , ZASP , myotilin and filamin C are linked to myofibrillar diseases [1] . The Z-disc , which is formed by a complex network of diverse proteins , defines the structural boundaries of sarcomeres and integrates the actin filaments of neighboring contractile units . Major morphological and cellular hallmarks that define myofibrillar disorders include disintegration of the Z-disc lattice network , mitochondrial disruption , and ectopic protein aggregates . The autosomal dominant R120G mutation in the αB-crystallin gene ( CryABR120G ) manifests adult-onset cataracts , skeletal muscle weakness and heart failure [2] . CryAB , a small molecular weight heat shock protein , is expressed constitutively in the lens and in non-lenticular tissues associated with high rates of oxidative metabolism , such as heart and type I and type II skeletal muscle fibers . A primary function of CryAB in these tissues is to prevent aggregation of intermediate filament proteins such as desmin , a characteristic subcellular phenotype of desmin-related myopathies [3] . Earlier studies by several laboratories supported a loss-of-function mechanism for the CryABR120G mutation , based on alterations of its secondary and quaternary structures , decreased interactions for client substrates with intermediate filaments and reduced stability on heat denaturation in vitro [4] , [5] . Subsequently , expression of either the mouse or human CryABR120G allele in the mouse heart produced cardiomyopathy , heart failure and shortened lifespan , all of which phenocopy the disease condition in humans [6] , [7] . Additional missense , truncation and autosomal recessive mutations have underscored the dominant inheritance patterns of disease-causing CryAB expression but their underlying molecular mechanism ( s ) have remained elusive . An important concept in the pathogenesis of neurodegenerative and , perhaps , myofibrillar disease is that misfolding and aggregation of destabilized mutant proteins trigger ‘toxic’ gain of function etiologies [8] . It has been hypothesized that the accumulation of amyloidogenic aggregates is preceded by the appearance of soluble , oligomeric species whose toxicities arise from multiple , non-exclusive mechanism ( s ) mediated by ( 1 ) flexible hydrophobic surfaces that promote aberrant interactions and sequestration [9] , ( 2 ) poor clearance mechanisms that disrupt central protein quality control and propagate folding defects [10] , [11] , and ( 3 ) perhaps by compromised lipid integrity as suggested by in vitro model membrane systems [12] . Although CryABR120G does not appear to result from a “classical” amyloid , recent studies have shown that aggregates of mutant CryAB are recognized by the anti-oligomer A11 [13] , [14] providing a link with well-known amyloid protein aggregation diseases such as Alzheimer's and Huntington's diseases . Our recent work on a mouse model of the inherited human CryABR120G cardiomyopathy has provided the first persuasive case for pathology resulting from “reductive” , as opposed to oxidative , stress in disease pathogenesis . The molecular events and damaging effects of reactive oxygen species ( ROS ) on biological molecules and systems are well known . When ROS are generated in excess of a cell's capacity to neutralize them , by enzymatic and non-enzymatic antioxidant pathways , the cell experiences oxidative stress . A large number of disease states have been attributed , in whole or in part , to damage from ROS or oxidative stress . However , there has been little appreciation for the possibility that an excess of reducing equivalents might also cause problems for the cell . Reductive stress can be induced by supplying strong reducing compounds , such as dithiothreitol , to cells in culture , but only recently has it been shown by Rajasekaran and coworkers that reductive stress may occur as a pathological state consequent to expression of a mutant protein . CryABR120G-induced cardiomyopathy was accompanied by a significant shift towards a more reduced intracellular environment , as measured by the glutathione redox couple [7] . The dominance of CryABR120G , and its unexpected link to an excessively reduced environment , suggested a ‘toxic’ gain of function mechanism . To investigate the reductive stress hypothesis of CryABR120G pathology , we have developed the first Drosophila melanogaster model of human CryABR120G toxicity in multiple organs and tissues . Because there is a strong evolutionary conservation of key developmental and metabolic pathways between humans and Drosophila [15]–[18] , studies of human conditions in Drosophila have been very productive . This has been especially true in the fields of heritable developmental defects , including congenital heart diseases , aging-related conditions and neurodegenerative diseases [19]–[22] . Recently , with advanced microscopic technology [23] , Drosophila has been used to model human cardiac physiology and aging [24]–[27] . Interestingly , flies with a dilated heart have been reported in certain genetic backgrounds [28] . Strikingly , cardiac dilation is observed in patients with protein aggregation cardiomyopathy and in mice that over-express CryABR120G [7] . In this study , we demonstrate that CryABR120G pathologies in the fly heart and eye are regulated by key enzymes that reduce nicotinamide adenine dinucleotide phosphate ( NADP ) to NADPH . As was the case in the mouse heart , the deleterious effects of expressing CryABR120G were strongly ameliorated by knockdown or mutation of the gene encoding G6PD . We further exploited the fly model and report that reduced function of other major generators of NADPH also strongly suppressed the CryABR120G phenotype , implicating the entire cellular NADP/NADPH network in CryABR120G pathology .
To extend our tests of the reductive stress hypothesis of CyrABR120G pathology , we used the Gal4-UAS modular expression system [29] , [30] to permit expression of the human CryABR120G allele in various cell types . We first generated transformants carrying either a wild-type UAS-CryAB+ or UAS-CryABR120G construct . Next , to determine whether CryABR120G expression would affect cardiac function in Drosophila , we drove its expression in the fly heart with a Hand-Gal4 driver . We confirmed cardiac-specific expression from Hand-Gal4 by examining GFP fluorescence using a UAS-CryABR120G-GFP fusion construct . Visual inspection of fluorescent micrographs confirmed cardiomyocyte restricted expression of the construct . Remarkably , the marked CryABR120G appeared targeted to a repetitive myofibrillar component of the cardiac fibers of flies , as found in higher organisms [31] , likely the Z-discs ( Figure S1 ) . To investigate the effects of human wild type or mutant CryAB expression on the simple , linear Drosophila cardiac tube ( Figure 1A ) we imaged surgically exposed beating hearts and tracked wall movements of semi-intact flies using direct immersion DIC optics in conjunction with a high speed digital video camera [32] . We characterized the effects of CryAB+ and CryABR120G on the contractile performance and general morphology of Drosophila hearts . Heart period , which is defined as the length of time between the ends of two consecutive diastolic intervals , and arrhythmia indices , a quantitative measure that reflects cardiac rhythmicity and permits exploration of heart rhythm irregularities , were calculated for ∼45 three week old semi-intact Drosophila from each line . Cardiac diameters were measured directly from individual video frames at peak diastolic and systolic time points at multiple locations along the linear portion of abdominal segment three of each heart tube . These measurements revealed that expression human CryAB+ did not significantly perturb any analyzed index of cardiac function relative to control hearts ( Figure 1 , Figure S2 ) . In contrast , expression of CryABR120G significantly affected several parameters of heart function . Arrhythmic beating patterns appeared to increase ( Figure 1C ) , although these trends were not statistically significant . Diastolic and systolic diameters were significantly increased in response to CryABR120G expression , and fractional shortening of the fly heart was significantly reduced thus notably impairing Drosophila cardiac function ( Figure 1D–F ) . In accord with the mouse observations [7] , simultaneous RNAi-mediated knockdown of G6PD significantly improved several indices of cardiac function and overall heart performance . Hearts expressing both CryABR120G and RNAi targeted against Zw ( the gene encoding G6PD ) exhibited significantly shorter heart periods ( increased heart rates; Figure 1B ) , significantly reduced arrhythmia indices and systolic diameters , and significantly greater fractional shortening ( Figure 1C–F ) . Interestingly , cardiac restricted expression of G6PD RNAi alone significantly decreased heart periods relative to those of w1118 x HandGal4 control hearts ( Figure 1B ) . Many of these differences in contracting heart tubes were qualitatively visualized via M-mode traces , which display the dynamics of cardiac contractions of representative hearts from the various genotypes ( Figure 2 ) . These traces show the positions of the heart wall edges ( Y direction ) over time ( X direction ) . M-modes from semi-intact heart preparations from control flies show fairly regular contractions . However , these traces reveal a subtly arrhythmic beating pattern in CryABR120G cardiac tubes relative to controls . Further , the CryABR120G hearts were dilated , and exhibited a lower extent of shortening . Co-expression of Zw RNAi increased heart rate , promoted rhythmic beating and rescued percent fractional shortening in the CryABR120G mutant hearts . Thus , overall , cardiac output of the mutant hearts is likely to be significantly enhanced by reducing the enzymatic activity of G6PD in flies as found in mouse models [7] . We also asked whether CryAB expression produced any deleterious effects if expressed in other tissues . When CryAB+ was expressed ubiquitously ( using a Tub-Gal4 driver ) , or in the eye only ( using either ey-Gal4 or GMR-Gal4 drivers ) no abnormal phenotypes were observed in 32 of 33 lines , in spite of easily detectable expression ( in eight lines examined by Western blotting; not shown ) . We did recover a single CryAB+ transformant that produced a rough eye phenotype . However , the insertion was located upstream of the escargot ( esg ) gene , a location where mis-expression elements are known to produce rough eyes by promoting esg expression [33] . We used qRT-PCR and verified that this particular CryAB+ insertion also drove overexpression of esg . We therefore attribute its phenotype to esg overexpression , and not to CryAB+ . In contrast , we identified four UAS-CryABR120G lines that produced complete or partial lethality with ubiquitous expression ( using either A5C-Gal4 or Tub-Gal4 drivers ) . Western blotting of protein extracts from the single line with any survivors revealed abundant CryABR120G expression . No expression was detected in seven fully viable lines ( Figure S3 ) . We speculate that the high frequency of non-expressing transformant lines reflects a strong selection against lines that show even slightly leaky expression . Subsequent Western blots of flies with expression limited to the eye ( which allows survival ) revealed that the three lines that were lethal when constitutively expressed also had abundant Gal4-induced expression of CryABR120G ( Figure 3 ) . When CryABR120G expression was driven in the wing , flies with deformed and mis-shapen wings were produced ( Figure 4B ) , while expression in the eye throughout development ( with ey-Gal4 ) resulted in variably rough and small eyes ( Figure 4D ) . A stronger and more consistent eye phenotype was observed when expression was driven in differentiating eye discs ( with GMR-Gal4 ) , characterized by irregular patterning and loss of ommatidia and pigment ( Figure 4E ) . Cardiomyopathy caused by mutations in CryAB is correlated with the presence of cytoplasmic protein aggregates containing CryAB and a number of other proteins [7] , [14] . To determine whether human CryABR120G in Drosophila was also found in aggregates we extracted Triton-X 100 soluble and insoluble protein fractions from eyes expressing CryAB+ and CryABR120G and used Western blotting to determine the distribution of CryAB between these fractions ( Figure 3 ) . We found a portion of the CryABR120G mutant protein in the insoluble pellet fraction ( 6 . 6%±2 . 9 , 23%±13 , 1 . 9%±1 . 0 and 4 . 9%±1 . 0 for lines 7B , 13A , 14A and 16A , respectively ) , while CryAB+ protein was found virtually exclusively in the soluble fraction ( with only 0 . 3%±0 . 2% and 0 . 2%±0 . 1% in the pellet for lines 8B and 12A , respectively ) . Thus , CryABR120G in Drosophila shares the characteristic of being found , in part , in insoluble aggregates . The eye is dispensable for normal development and viability , and is an established model for examining the cellular effects of human neurodegenerative disease genes [34] , [35] . Therefore , we chose to use the phenotype produced by GMR-Gal4 UAS-CryABR120G in the eye as a model to test the reductive stress hypothesis . Eye phenotypes were quantitated by scoring into five groups , ranging from ∼wildtype ( category 1 ) , to having all or nearly all of the eye strongly affected ( category 5; Figure 4E ) . We then tested whether a reduction of G6PD could suppress the CryABR120G phenotype , as it does in mouse and fly hearts . Two RNAi lines that significantly reduced expression of Zw both suppressed the CryABR120G eye phenotype ( Figure 5B , C; Table S1 ) . To extend these observations we then asked whether overexpression of Zw would exacerbate the CryABR120G phenotype . Four overexpression lines were tested and , unlike the heart phenotype ( Figure S2 ) , all strongly enhanced the CryABR120G phenotype ( Figure 5E–H; Table S1 ) . The results shown in Figure 5 using the CryABR120G line 16A were confirmed with the independent CryABR120G insertion line 14A that also has a strong eye phenotype: responses to Zw knockdown and overexpression were extremely similar for the two lines ( not shown ) . There was no phenotypic effect of altered G6PD levels on normal eyes . We considered the possibility that the UAS-controlled RNAi constructs might titrate GAL4 and produce apparent suppression simply through reduced expression of CryABR120G . However , since the G6PD overexpression constructs are also driven by GAL4 , but they enhance the CryABR120G phenotype , this concern appears unfounded . The enzyme 6-phosphogluconate dehydrogenase ( PGD ) acts downstream of G6PD in the pentose phosphate pathway , and like G6PD , reduces NADP to NADPH . We tested whether mutations in the gene encoding PGD ( Pgd ) would also affect the CryABR120G phenotype . Two deletions that removed the gene ( along with neighboring genes ) suppressed the CryABR120G phenotype when tested as heterozygotes ( Figure 6B , C; Table S1 ) . In addition , flies carrying a chromosome with mutations in Zw and Pgd , in heterozygous condition , also showed strong suppression of the CryABR120G phenotype ( Figure 6D ) . Finally , RNAi directed against Pgd produced very strong suppression of the CryABR120G phenotype ( Figure 6E; Table S1 ) . These mutants or RNAi knockdowns , by themselves , had no effect on the phenotype of normal eyes . These results are consistent with the reductive stress hypothesis , but it is still conceivable that G6PD and PGD influence CryABR120G pathology via other products of the pentose phosphate pathway . To determine whether varying the enzymatic production of NADPH would affect the CryABR120G phenotype , regardless of the source of that variation , we tested whether alteration of isocitrate dehydrogenase ( IDH ) or malic enzyme ( MEN ) levels would affect the phenotype . We observed that RNAi-mediated knockdown of IDH ( either the putative mitochondrial ( CG6439 ) or cytoplasmic ( CG7176 ) form ) was capable of suppressing the CryABR120G phenotype ( Figure 7B–F; Table S1 ) . Knockdown of MEN also suppressed the CryABR120G phenotype ( Figure 7G; Table S1 ) . Knockdown of IDH or MEN had no effect on normal eyes . Although only one of three lines expressing RNAi against Idh ( CG7176 ) achieved significant suppression of the CryABR120G phenotype , it is also the case that these lines only achieved an ∼20–30% reduction in Idh RNA levels . In the cases of Zw , Pgd and Men , where strong suppression was observed , ∼40–50% knockdowns of RNA levels were achieved ( Table S1 ) . To assess the redox environment of cells in response to altered dosage of genes that mediate NADP/H metabolism we carried out two series of experiments to determine the ratio of reduced to oxidized glutathione ( GSH∶GSSG ) . When RNAi-mediated knockdown of G6PD , PGD , MEN or IDH was driven in heads with GMR-Gal4 the GSH∶GSSG ratio was reduced in every case ( Figure 8A ) , though the reduction as a result of PGD knockdown was not significant at P = 0 . 05 level . The results confirm our supposition that these knockdowns impair cells' ability to generate NADPH . We observed a slight , though not statistically significant , increase in the GSH∶GSSG ratio when G6PD was overexpressed in heads . However , when we assayed whole larvae , with expression driven ubiquitously , G6PD overexpression produced a very large and significant increase in the GSH∶GSSG ratio ( Figure 8B ) . These results confirm the involvement of the NADP/H network in cellular dysfunction produced by CryABR120G expression and strongly implicate reductive stress as the causative agent for pathology . One concern that arose when assessing the phenotypic effect of human CryABR120G expression was that two Drosophila lines ( 7B and 13A ) showed only mild phenotypic effects in the eye ( Table S2 ) , despite exhibiting robust protein expression ( Figure 3 ) . However , these two lines do show rough eye phenotypes if subjected to a strong heat shock during late larval/early pupal development , a treatment that also strongly enhances the phenotype of lines 14A and 16A ( Table S2 ) . Flies carrying GMR-Gal4 alone showed no eye phenotype when subjected to this heat shock . Moreover , one of these lines ( 7B ) also exhibits a wing phenotype when expression is driven in the wing ( Table S2 ) . To test whether these particular insertions might be associated with suppression of the CryABR120G phenotype , we combined them with GMR-Gal4 and the CryABR120G line 16A that was used in the experiments of Figures 4 , 5 and 7 . The combination of line 7B or 13A with 16A resulted in an eye phenotype that was reduced from that of 16A alone , indicating that these two lines do suppress the CryABR120G eye phenotype to some degree ( Figure S4 ) . We have not determined why these two lines show little phenotypic consequence in the eye , but we speculate that it may have to do with subtle differences in timing of gene expression . Perhaps slightly earlier onset of expression in lines 7B or 13A results in a protective response , in the same way that mild heat shocks , by inducing synthesis of heat shock proteins , can protect against subsequent , more severe heat shocks [36] , [37] . Alternatively , the expression of genes in the region of the 7B and 13A insertion loci might be altered to result in phenotypic suppression . In spite of these lines showing only mild effects , our experiments convincingly show that CryABR120G expression , but not CryAB+ , can cause strong phenotypic effects in the fly heart , eyes and wings . Moreover , detailed examination of the heart and wing defects show that they are responsive to altered levels of the enzymes that reduce NADP to NADPH .
Our results show that Drosophila provides a suitable model in which to study the pathology of the human CryABR120G mutation . Expression of the mutant allele , but not the wildtype , in fly hearts , causes heart dilation and dysfunction very reminiscent of the cardiomyopathy produced in humans carrying this dominant allele [2] . We also found that a reduced level of G6PD ameliorates many of the perturbed cardiac functional parameters in CryABR120G flies , just as it does in the CryABR120G mouse [7] . Despite a lack of reduction in CryABR120G diastolic diameters in response to Zw ( G6PD ) RNAi co-expression , systolic diameters in the double mutants were rescued and did not significantly differ from those found in fly hearts expressing wildtype human CryAB . Thus , fractional shortening was indistinguishable between wildtype CryAB-expressing and CryABR120G+Zw RNAi-expressing hearts . Furthermore , cardiac restricted expression of Zw RNAi , either with CryABR120G or alone , significantly increased heart rates relative to control hearts . This suggests G6PD deficiency can improve cardiac output in either mutant or non-mutant backgrounds and may be a potent modifier of cardiac function . In Drosophila , overexpression of G6PD can extend lifespan and protect against oxidative stress [38] . In mammalian cells , overexpression of wildtype small heat shock proteins leads to increased G6PD expression and protection against oxidative stress [39] . Our finding , that reduction of G6PD can be beneficial in some circumstances , is also not without precedent . Some studies have suggested a link between G6PD deficiency and protection against cardiovascular disease in humans , although such findings have not been replicated in larger patient studies [40]–[42] . Previous work from one of our laboratories showed that G6PD reduction is highly beneficial in one specific case — when human CryABR120G is expressed in the mouse heart [7] . We do not see a conflict in these differing outcomes , but instead conclude that the effect of modifying G6PD levels may range from beneficial to deleterious , with the outcome determined by the constellation of genetic variation present in individuals' genomes and the environmental stressors that they experience . To generate an experimental paradigm suited to rapid genetic exploration we expressed CryABR120G in the fly eye and found that it strongly disturbs normal development and pattern formation . The eye phenotype was also responsive to altered G6PD levels , validating it as a model for investigation of the underlying mechanism of CryABR120G pathology . Unlike a recent report [43] , we saw no abnormal eye phenotype that could be attributed to expression of the wild-type human CryAB gene . In the one case where we did observe an eye phenotype it most likely resulted from induced expression of the esg gene that lay adjacent to that particular insertion of the CryAB transgene . Our findings indicate that CryABR120G induces cellular dysfunction in both the heart and the eye , or lethality if expressed ubiquitously , while wild-type CryAB is relatively benign . The human disease produced by the CryABR120G allele is sometimes called Desmin-Related Myopathy ( DRM ) , owing to the presence of desmin in the characteristic cytoplasmic protein aggregates , and to similarities with diseases caused by mutations in the gene encoding the intermediate filament desmin [2] , [6] , [44] . Although Drosophila do not have a desmin homolog , several lines of evidence argue that the cellular dysfunction in Drosophila resulting from CryABR120G expression is , nonetheless , a legitimate model for this disease . First , a number of other proteins have been identified within the aggregates , including ( at least ) another small heat shock protein and G6PD [7] , [14] , both of which have homologs in Drosophila . Second , CryABR120G causes the formation of cytoplasmic aggregates even when expressed in human cell types that do not express desmin [45] , [46] , and our results show that a portion of the CryABR120G is also found in aggregates in Drosophila . Third , the identical response of mouse and Drosophila CryABR120G pathologies to G6PD reduction strongly suggests an identical mechanism of action . The pyridine nucleotides NADH and NADPH are essential co-factors of oxidative and reductive enzymatic processes involved in energetics , oxidative metabolism , redox homeostasis , calcium homeostasis , macromolecular biosynthesis , mitochondrial functions , gene expression , aging and cell death [47] . In this study , we examined the effect of altered levels of the four enzymes that are primarily responsible for reducing NADP to NADPH ( Figure 9 ) : two enzymes of the pentose phosphate pathway , G6PD and PGD which together account for ∼40% of NADPH levels in the adult; MEN which generates pyruvate for import into mitochondria and accounts for another ∼30%; and , IDH , which accounts for ∼20% of NADPH [48]–[50] . These enzymes constitute a metabolic network linked by a common substrate ( NADP ) and interacting regulation [50] . A major finding of our study is that , even though G6PD , PGD , MEN and IDH carry out varied metabolic reactions , alterations to any of their activities have significant consequences for the phenotypes produced by CryABR120G expression , implying a common mechanism of action through NADP/H . In our experiments , reduction of IDH was less effective at CryABR120G suppression than reductions of G6PD , PGD or MEN , a result that is not entirely surprising . Alteration of either G6PD or PGD activity is likely to affect both activities coordinately since they constitute sequential steps in the tightly regulated pentose phosphate pathway , and MEN by itself produces more NADPH than any of the other three enzymes of this network . IDH produces less NADPH than either MEN or the G6PD/PGD couple . Additionally , our RNAi-mediated knockdowns of IDH were relatively ineffective . What was surprising was that knockdown of the mitochondrial NAD-dependent IDH resulted in significant suppression of the CryABR120G phenotype . We surmise that mitochondrial metabolism affects the cytoplasmic NADP/H network . The NADP∶NADPH redox couple , and the linked glutathione redox couple ( GSSG∶GSH ) , participate in a diverse array of biological processes . Therefore , we envision a number of possible mechanisms through which CryABR120G could alter the cellular redox potential and thereby contribute to toxicity . Most obviously , redox-sensitive sequestration of both existing and newly synthesized proteins could seriously disturb cellular regulation . The function of many proteins depends on the reduced or oxidized state of thiol-containing cysteine residues . It is conceivable that structurally flexible hydrophobic protein surfaces , which are normally buried within a folded protein , could engage in non-productive protein-protein interactions , in part , due to alterations of intra-chain disulfide links . Partially folded proteins might , in this way , become soluble toxic intermediates [9] . Alternatively , misfolding might occur as a result of alterations in other redox-sensitive post-translational modifications , for example glutathionylation , nitrosylation , and ( de ) acetylation . Co-aggregation of several distinct polypeptides might cripple multiple disparate functions within the cell . As mentioned , our results also suggest that alterations in mitochondrial homeostasis and energy metabolism could affect the levels of oxidized or reduced NADP/H . The reciprocal is most certainly true as well , with normal mitochondrial function dependent upon the function of the NADP-reducing enzyme MEN . Additionally there are scores of enzymes that use NADP/H as a cofactor , and the activity of one or more of these enzymes could be affected to generate the phenotypes we observed . It will require significant further work to identify the critical determinants of NADP/H involvement in CryABR120G pathology . Drosophila provides powerful tools for genetic screening to identify such factors and the model for CryABR120G pathology that we describe here provides a context for carrying out such screens . We anticipate that such efforts may ultimately lead to the identification of potential targets for therapy and the promise of useful treatments for the human disorders .
P{UASP-CryAB+} and P{UASP-CryABR120G}: A fragment DNA containing human CryAB cDNA was released from donor plasmids ( pCMVHA-wtCryAB [51] or pCDH1-MCS1-R120GhCryAB ( unpublished ) ; by NcoI and DraI digestion , followed by Klenow filling in to make blunt ends . The vector pUASP [52] was digested with EcoRI or NotI respectively and filled in to make blunt ends . Ligation of respective inserts and vectors produced P{UASP-CryAB+} and P{UASP-CryABR120G} . Each was verified by restriction enzyme mapping and sequencing , then was used in P-element mediated transformation by standard methods [53] , [54] . P{UASTattB-GFP-CryABR120G}: The CryABR120G cDNA was released from pCDH1-MCS1-R120GhCryAB by EcoRI digestion and was ligated in frame to the same site at the C terminus of GFP coding region an intermediate vector . The fusion protein was released using flanking XbaI sites and sub-cloned into pUASTattB vector provided by J . Bischof [55] . The plasmid was injected into y w P{ry+t7 . 2 , hsFLP}1; M{3xP3-RFP . attP}ZH-86Fb; M{vas-int . B}ZH-102D ( from the Bloomington Drosophila stock center ) to integrate the fusion protein construct onto chromosome 3R cytological location 86F . The X chromosome carrying FLP was removed by crossing after transformation . Insertion sites of transformed P elements were mapped either by inverse PCR or Splinkerette PCR method [56] . P{tub-GAL4} , P{ey-GAL4} and P{Act5C ( FRT . y+ ) Gal4} flies were obtained from the Bloomington , IN , USA , Drosophila stock center ( lines #5138 , #5535 and #25374 , respectively ) . After being exposed to FLP , P{Act5C ( FRT . y+ ) Gal4} lost the y+ marker and became recessive lethal . It was then kept as the balanced stock y w; P{Act5C-GAL4}/S2 CyO . P{GMR-GAL4} flies are discussed in [57] . Fly lines carrying P{UAST-G6PD} along with control y w2 were provided by W . C . Orr [38] . Mutations for Zw and Pgd were obtained from Bloomington stock center . RNAi fly stocks were from Vienna Drosophila RNAi Center [58] . Table S1 lists the stock numbers of all UAS-RNAi lines ( Vienna Drosophila RNAi Center ) , Zw overexpression lines ( W . C . Orr ) , and mutants ( Bloomington Drosophila Stock Center ) . Flies were raised at 25°C , unless otherwise specified , on standard cornmeal-agar medium in standard 25×90 mm vials . Assay for CryABR120G protein expression: The UASP-CryABR120G lines were crossed to flies carrying the tub-GAL4 driver . One male and one female were taken from seven crosses with surviving progeny . Each fly was homogenized in 100 µl of 1X sample buffer . Eight µl of lysate was loaded in each lane of a 12% SDS-PAGE gel , separated by electrophoresis and then examined by Western blotting to detect the CryAB protein . A lysate of mammalian cells expressing CryABR120G was included as a positive control . Rabbit antiserum ( 1∶5000 dilution ) against human CryAB protein was the primary antibody [7] . Assay for CryAB protein solubility: To determine whether CryAB expressed in eyes existed in a soluble or insoluble form we modified the procedure described by Carbone et al . [59] . Ten heads from females expressing CryAB ( wildtype or mutant ) were collected and homogenized for 10 minutes on ice in 100 µl lysis buffer ( 10 mM Tris pH 7 . 5 , 5 mM EDTA , 1% NP40 , 0 . 5% deoxycholate , 150 mM NaCl and 1% Triton X-100 ) . After incubation in lysis buffer for 30 minutes on ice , samples were frozen at −20°C overnight , then thawed out . Cuticle and debris in the lysate were separated by brief centrifugation at 1 , 000 rpm for 1 minute . Supernatant was collected . Soluble and insoluble fractions were further separated by centrifugation at 14 , 000 rpm for 15 minutes . After collecting the soluble fraction , the insoluble fraction was washed three times with 200 µl lysis buffer each , and then solubilized in 40 µl 1X sample buffer for Western . The soluble fraction was TCA precipitated and washed before being resuspended in 40 µl 1X sample buffer . 2 . 5 head's worth of soluble or insoluble fraction ( 10 µl ) was loaded into each lane . Western blotting was carried out following standard procedures using the Odyssey Western Blot Kit ( Li-cor ) . Rabbit antiserum against Human CryAB and mouse anti-β-tubulin ( Developmental Studies Hybridoma Bank clone E7 ) were used as primary antibodies . After incubation with fluorescent anti-rabbit and anti-mouse secondary antibodies , the membrane was scanned on an infrared Odyssey scanner by Li-cor . The Western signal was quantified on the Li-cor scanner and the results from four independent experiments were averaged . The insoluble CryAB is reported as mean percent of total CryAB ± standard error . Stocks that carried the GMR-Gal4 driver and UAS-CryAB elements in homozygous condition were generated . Males from this stock were crossed to females from control lines ( y w2 or w1118 ) , or from lines carrying modifying elements or mutations . The eyes of daughters from these crosses were scored for the severity of the eye phenotype by assigning the eye to one of five categories ( Figure 1E ) . All transgenes were hemizygous in the scored females . Mann-Whitney tests for significance were performed using GraphPad Prism software . Total RNA from 15–25 female fly heads was harvested using Tri reagent and protocol ( Sigma-Aldrich ) . cDNA was synthesized from total RNA using RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) . Quantitative PCR of the cDNA was carried out on an iQ-PCR machine ( Bio-Rad ) using Maxima SYBR green/Fluorescein qPCR Master Mix ( Fermentas ) . Relative copy number was calculated against a set of common standard templates for each PCR reaction . For each cDNA sample , the relative copy numbers of gene of interest ( X ) and ribosomal protein L32 ( RPL ) were both obtained . Abundance of X in the sample was calculated by dividing the copy number of X by that of RPL . The average of three independent experiments was used to represent the abundance of X in a given genotype . Two independent wildtype UAS-CryAB+ controls and two UAS-CryABR120G mutant fly lines , as well as UAS-CryABR120G combined with UAS-Zw or UAS-Zw RNAi were crossed to Hand-Gal4 ( II ) driver flies ( Hand is a direct target of Tinman and GATA factors during Drosophila cardiogenesis and hematopoiesis , [60] ) . As an additional control , Hand-Gal4 ( II ) driver-flies were crossed to w1118 flies . The progeny were raised at 25°C on standard cornmeal-agar medium . All flies were transferred to fresh food every 2–3 days . At three weeks of age , 45–50 female offspring from each cross were anaesthetized and dissected . All procedures were done at room temperature ( 18–22°C ) as previously described [17] , [24] , [32] , [61] , [62] . Briefly , each head , ventral thorax and ventral abdominal cuticle was removed exposing the abdomen [62] . All internal organs and abdominal fat were removed leaving only the heart and associated muscles for each fly . Dissections were performed in oxygenated adult hemolymph . The semi-intact preparations were allowed to equilibrate with oxygenation for 20–30 min prior to filming . Analysis of heart morphology and physiology was performed using high speed movies of the semi-intact Drosophila preparations . 30 sec . movies were taken at rates of 100–200 frames/sec . using a Hamamatsu EM-CCD digital camera on a Leica DM LFSA microscope with a 10× immersion lens . All images were acquired and contrast enhanced using Simple PCI imaging software ( Compix , Inc . ) . M-modes were generated and determination of cardiac parameters , including heart periods , diastolic and systolic diameters , fractional shortening and arrhythmicity indices for each group was performed using a MatLab-based image analysis program [63] . The “arrhythmicity index” , which is defined as the standard deviation of the heart period normalized to the median of each fly allowed us to quantify the average severity of arrhythmic beating patterns for each line . One-way ANOVAs of genotype as a function of each measured cardiac parameter , with Bonferroni multiple comparison tests , were employed to determine if significant differences among all Drosophila lines were present . P values<0 . 05 were considered significant . Fluorescent imaging of Drosophila heart tubes were performed according to [64] . The UAS-GFP-CryABR120G fly line was crossed to Hand-Gal4 ( II ) driver flies . The progeny were aged to 1 and 3 weeks . Beating hearts of semi-intact Drosophila were placed in artificial Drosophila hemolymph containing 10 mM EGTA . Cardiac tubes were examined to ensure contractions were inhibited . Hearts were fixed in 1×PBS containing 4% formaldehyde at room temperature for 20 minutes with gentle shaking . Washing of hearts was performed three times for ten minutes with PBSTx ( PBS containing 0 . 1% Triton-X-100 ) at room temperature with continual shaking . After washing , the hearts were incubated with Alexa584-phalloidin in PBSTx ( 1∶1000 ) for 20 minutes with continual agitation . Washing of the hearts was again carried out three times for ten minutes with PBSTx at room temperature . The hearts were rinsed in 100 µl of PBS for 10 minutes . The specimens were mounted on microscope slides and viewed at 10–25× magnification using a Zeiss Imager Z1 fluorescent microscope equipped with an Apotome sliding module . Female flies from transgenic or control lines were crossed to w1118; GMR-Gal4 males . 30–60 heads were collected from flies of the appropriate genotype for each cross and then immediately put in 60–120 µl ( 2 µl per head ) 5% SSA ( 5-sulfosalicylic acid; Sigma #S-7408 ) and homogenized with a small eppendorf dounce pestle . Samples were then frozen at −80° and maintained frozen until assayed as described [65] . Female flies from UAS-RNAi , UAS-G6PD or control stocks were collected and crossed to y w; tub-Gal4/T ( 2;3 ) SM6 , Cy; TM6 , Tb males . Resulting third instar Tb+ larvae were collected and washed with 0 . 7% NaCl solution . Each collection was weighed and frozen on dry ice . The collected larvae were then stored at −80°C until ready for testing . At least 30 mg of larvae from the same genotype were pooled for each sample . Larvae were homogenized in 1X GSH MES buffer from the Glutathione Assay Kit by Cayman Chemical Company ( 10 µl/mg sample ) . The protocol provided with the kit was followed . Total GSH and GSSG measurements were calculated based on the Kinetic Method with minimal time course of 30 minutes . At least three independent samples were assayed for each genotype . The GSH/GSSG ratio for each sample was calculated independently . Statistical analysis of GSH/GSSG ratios was conducted using Graphpad Prism .
|
Cardiomyopathy is a specific form of heart disease that involves progressive restructuring of the heart muscle , resulting in reduced function and increased chance of sudden heart failure . Several mutations have been identified that cause inherited cardiomyopathy , including mutations in a gene called alpha B-crystallin . The work we report here puts this mutant gene into the fruit fly Drosophila melanogaster , where it causes heart defects that are similar to the human condition . Like cataracts reported in humans , this mutant gene also causes defective development of the fly eye . By examining defective fly hearts and eyes , and asking how genes involved in oxidation/reduction ( redox ) reactions influence these defects , we find that genes which control the redox environment of the cell also control the heart and eye defects . Oxidative stress , defined as an excess of harmful oxygen radicals , is a common concept in disease . Our work shows that in some circumstances , the genes that generate a more reduced cellular environment may also contribute to disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"models",
"drosophila",
"melanogaster",
"animal",
"genetics",
"congenital",
"hereditary",
"myopathies",
"model",
"organisms",
"cellular",
"stress",
"responses",
"genetic",
"screens",
"genetics",
"biology",
"human",
"genetics",
"molecular",
"cell",
"biology",
"autosomal",
"dominant"
] |
2013
|
The NADPH Metabolic Network Regulates Human αB-crystallin Cardiomyopathy and Reductive Stress in Drosophila melanogaster
|
Intraocular pressure ( IOP ) is a highly heritable risk factor for primary open-angle glaucoma and is the only target for current glaucoma therapy . The genetic factors which determine IOP are largely unknown . We performed a genome-wide association study for IOP in 11 , 972 participants from 4 independent population-based studies in The Netherlands . We replicated our findings in 7 , 482 participants from 4 additional cohorts from the UK , Australia , Canada , and the Wellcome Trust Case-Control Consortium 2/Blue Mountains Eye Study . IOP was significantly associated with rs11656696 , located in GAS7 at 17p13 . 1 ( p = 1 . 4×10−8 ) , and with rs7555523 , located in TMCO1 at 1q24 . 1 ( p = 1 . 6×10−8 ) . In a meta-analysis of 4 case-control studies ( total N = 1 , 432 glaucoma cases ) , both variants also showed evidence for association with glaucoma ( p = 2 . 4×10−2 for rs11656696 and p = 9 . 1×10−4 for rs7555523 ) . GAS7 and TMCO1 are highly expressed in the ciliary body and trabecular meshwork as well as in the lamina cribrosa , optic nerve , and retina . Both genes functionally interact with known glaucoma disease genes . These data suggest that we have identified two clinically relevant genes involved in IOP regulation .
Primary open-angle glaucoma ( hereafter referred to as glaucoma ) is a progressive optic neuropathy responsible for 12 . 3% of global blindness [1] . The evidence for a genetic etiology of glaucoma is well-established [2] . However , genes consistently implicated so far ( MYOC , OPTN , WDR36 ) [3]–[5] are relevant only in a limited number of families and explain a small proportion of the glaucoma cases in the general population [6]–[8] . So far , 3 genome-wide association studies ( GWASs ) for glaucoma have been published . A study from Iceland identified a common variant near CAV1 and CAV2 [9] . Both genes are expressed in the trabecular meshwork as well as in retinal ganglion cells . A Japanese study identified 3 putative loci , although none of these reached genome-wide significance [10] . A recent study in an Australian cohort of 590 patients with severe glaucomatous visual field loss identified susceptibility loci at TMCO1 and CDKN2B-AS1 [11] . The latter region had already at a genome-wide significant level been associated with vertical cup-to-disc ratio , which is an important clinical marker of glaucoma [12] . Finally , a study in an Afro-Caribbean population identified a genome-wide significant association between glaucoma and a locus on chromosome 2p by genotyping a previously identified linkage region [13] . Intraocular pressure ( IOP ) is the major risk factor of glaucoma and existing glaucoma therapies are exclusively aimed at lowering IOP . An elevated IOP ( >21 mmHg ) influences both the onset and the progression of glaucoma [14] . Genetic effects have been shown to account for a significant proportion of the variance in IOP , with heritability estimates ranging from 0 . 29 to 0 . 67 [15]–[19] . Five genome-wide linkage studies of IOP have been performed [20]–[24] . This resulted in 15 potential regions of interest , 2 of which were genome-wide significantly linked to IOP . The first was identified in an Australian glaucoma pedigree and was located on 10q22 [20] . The second was identified in individuals without glaucoma in West Africa and Mongolia and was located in the 5q22-23 region , which had already been implicated in glaucoma ( WDR36 gene and GLC1M locus ) [3] , [23]–[25] . Taken together , these findings suggest that extensive heterogeneity underlies the genetics of IOP and that the same genetic factors may possibly affect both the variance in normal IOP and the risk and onset of glaucoma . Thus , unraveling the genetic background of IOP may shed light upon the pathophysiology of glaucoma . To date , no GWAS has been reported for IOP . To identify genetic determinants of IOP , we performed a GWAS in 11 , 972 participants from 4 independent population-based studies in The Netherlands , and we replicated our findings in 7 , 482 participants from 4 additional independent cohorts of Caucasian ancestry . We investigated whether the IOP associated SNPs were also related to glaucoma in 1 , 432 glaucoma cases . Lastly , we examined expression levels of the identified candidate genes in human ocular tissues . We identified common variants in GAS7 and TMCO1 that altered the susceptibility to both IOP and glaucoma .
Genotypic and IOP data were available for 11 , 972 participants from the Rotterdam Study cohort I ( RS-I ) , RS-II , RS-III , and the Erasmus Rucphen Family ( ERF ) Study ( Table 1 ) . Genomic inflation factors were 1 . 037 for RS-I , 1 . 006 for RS-II , 1 . 015 for RS-III , and 1 . 029 for ERF . QQ-plots for the observed versus expected p-values for the individuals cohorts as well as for the discovery meta-analysis have been provided in Figure S1 . The genome-wide association analyses in the ERF study were performed with and without adjustment for the time of the IOP measurement . As this adjustment did not significantly affect the results , the unadjusted ( other than for age and sex ) data were taken forward to the meta-analysis . Four SNPs on chromosome 17p13 . 1 were significantly associated with IOP in the discovery meta-analysis ( p<5×10−8; Figure 1 , Table 2 ) . These SNPs are located in the growth arrest-specific 7 ( GAS7 ) gene ( Figure 2 ) [26] . The SNP that showed strongest association with IOP was rs11656696 . The effect of the rs11656696 alleles was consistent across all 4 discovery cohorts ( Table S1 ) . A further 6 chromosomal loci showed more moderate but nevertheless suggestive associations with IOP ( p<1×10−5; Table 2 , Figure S2 ) and were also taken to the replication phase . Of these , rs7555523 is located in the trans-membrane and coiled-coil domains 1 ( TMCO1 ) gene on chromosome 1q24 . 1 ( Figure 2 ) [26] , which is located 7 . 6 MB from MYOC . A list of all the SNPs that were associated with IOP at a significance level of p<1×10−5 has been provided in Table S2 . We examined at least 416 KB of the chromosomal regions spanning the known disease genes MYOC , OPTN , and WDR36 in more detail in the discovery meta-analysis . None of the 1507 SNPs assessed in total showed significant association with IOP ( Figure S3 ) [26] . We also evaluated 12 SNPs which had approached genome-wide significance in earlier association studies ( Table S3 ) [9] , [10] , [13] . Of these , rs4236601 in the CAV1-CAV2 region , previously identified in Caucasians , was consistently associated with increased IOP in our discovery meta-analysis ( beta = 0 . 19 , 95%CI = 0 . 09–0 . 29 , p = 1 . 1×10−4 ) [9] . The rs4656461 locus , identified in patients with severe glaucoma from Australia , overlapped with the rs7555523 locus that was identified with suggestive evidence in our study [11] . The 2 SNPs are at a disctance of 31774 base pairs from each other and are in linkage disequilibrium ( R squared = 1 ) . Rs4977756 , the second locus that emerged from the study in Australia , was not associated with IOP in our discovery cohorts . Of the three regions identified in Japan , only rs7081455 on chromosome 10 showed nominal evidence for association with IOP ( beta = 0 . 12 , 95%CI = 0 . 08–0 . 16 , p = 4 . 6×10−3 ) . Our data did not replicate the association in the 2p16 locus which was previously identified in Afro-Caribbeans . Finally , we examined the two chromosomal regions that had previously been identified in genome-wide linkage studies of IOP [20] , [24] . Both regions showed suggestive evidence of association with IOP in our discovery meta-analysis: Rs7894966 , located in the bone morphogenetic protein receptor 1A ( BMPR1A ) gene on chromosome 10q23 . 2 , is in the region previously identified in an Australian linkage study of IOP ( 16 . 2 MB from the peak LOD score ) [20]; Rs216146 , in the colony stimulating factor 1 receptor ( CSF1R ) gene on chromosome 5q32 , is close to the region that previously showed genome-wide significant linkage to IOP in West Africans [24] . This SNP is located at a distance of 21 . 0 MB to the peak LOD score , 10 . 0 MB to the glaucoma locus GLC1M , and 39 . 0 MB to WDR36 . Replication of the IOP association was done in 4 additional cohorts from the TwinsUK study ( N = 2 , 235 ) , the Australian Twin study ( N = 1 , 807 ) , the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study ( DCCT/EDIC; N = 1 , 304 ) , and the Wellcome Trust Case-Control Consortium 2 / Blue Mountains Eye Study ( WTCCC2/BMES; N = 2 , 136 ) ( Text S1 ) . The results of the replication analyses are presented in Table 3 . Although in most studies the association did not reach nominal significance ( p<0 . 05 ) , most likely explained by the low statistical power of these relatively small studies , the directionality of the effects was consistent across the 4 replication cohorts for most SNPs . The exceptions were rs7894966 and rs216146 for which the effects were in opposite direction compared to the discovery cohorts . When the gene discovery and replication cohorts were combined , two intronic SNPs reached genome-wide significance . Each copy of the rs11656696 minor allele ( A ) , located in GAS7 , was associated with a 0 . 19 mmHg IOP reduction ( 95% confidence interval [CI] = 0 . 12–0 . 26 mmHg; p = 1 . 4×10−8 ) , and each copy of the rs7555523 minor allele ( C ) , located in TMCO1 , with a 0 . 28 mmHg IOP increase ( 95%CI = 0 . 18–0 . 37 mmHg; p = 1 . 6×10−8 ) . We investigated the associations of the GAS7 rs11656696 minor allele ( A ) and the TMCO1 rs7555523 minor allele ( C ) with glaucoma in 4 case-control studies from the Netherlands and Germany ( Text S1 ) . The results are presented in Figure 3 . For rs11656696 A we found a decreased glaucoma risk in the Amsterdam Glaucoma Study ( AGS; OR = 0 . 71 , 95%CI = 0 . 51–0 . 99 ) and the Erlangen and Tübingen study ( OR = 0 . 82 , 95%CI = 0 . 69–0 . 97 ) , but not in RS-I and the Genetic Research in Isolated Populations ( GRIP ) program . When combining the 4 case-control studies , rs11656696 A showed a decreased glaucoma risk ( OR = 0 . 88 , 95%CI = 0 . 78–0 . 98 , p = 2 . 4×10−2 ) . For rs7555523 C , we found an increased glaucoma risk in all 4 case-control studies . Combined , these studies showed an increased glaucoma risk with an OR of 1 . 31 ( 95%CI = 1 . 12–1 . 53 , p = 9 . 1×10−4 ) . In a first study of expression levels in human ocular tissues , we observed moderate to high expression of GAS7 , and high expression of TMCO1 in the ciliary body ( CB ) , the secretory neuroepithelium that produces the aqueous humor ( Table 4 ) . Both genes were moderately to highly expressed in the choroid , the retinal pigment epithelium and photoreceptors . In a second , independent study , mRNA expression profiles in human eyes of GAS7 and TMCO1 displayed an ubiquitous expression of both gene products , with the highest expression levels of GAS7 in the trabecular meshwork , the lamina cribrosa , and the optic nerve , whereas TMCO1 expression was most prominent in the trabecular meshwork and the retina ( Figure 4 ) .
We identified rs11656696 in GAS7 and rs7555523 in TMCO1 as common variants associated with IOP . In a joint analysis of the discovery and replication cohorts each copy of the rs11656696 minor allele ( A; allele frequency 0 . 43 ) was associated with a 0 . 19 mmHg decrease in IOP ( 95%CI = 0 . 12–0 . 26 mmHg ) , whereas each copy of the rs7555523 minor allele ( C; allele frequency 0 . 12 ) was associated with a 0 . 28 mmHg increase in IOP ( 95%CI = 0 . 18–0 . 37 mmHg ) . Both variants showed marginal evidence for association with glaucoma when combining data from 4 case-control studies , although for rs11656696 significance was only obtained in 2 studies . GAS7 is located in a chromosomal region previously identified by a linkage study of glaucoma [27] . We observed high expression levels of GAS7 in the optic nerve , and , in particular , the lamina cribrosa . The lamina cribrosa is the connective tissue network through which the nerve fibers traverse to form the optic nerve , and is assumed to be the main site for glaucomatous damage to the optic nerve . We also observed moderate to high expression of GAS7 in the ciliary body ( CB ) , the secretory neuroepithelium that produces the aqueous humor , and high expression of GAS7 in the trabecular meshwork ( TM ) , which is the main tissue involved in aqueous humor outflow [28] . Together , the CB and TM largely control IOP . Previously , Liton and colleagues already reported significant downregulation of GAS7 expression in TM of glaucomatous eyes [28] . In absence of the ( in vivo ) typical mechanical forces on the TM , a similar effect was also observed in cultured TM cells [28] . High GAS7 expression has previously been shown in amacrine cells in the mouse retina , while lower expression was found in retinal cell types which are usually not affected by glaucoma [29] . Protein pathway analyses and evidence from previous literature allude to functional effects of GAS7 in both the TM and retina . GAS7 has been implicated in cell remodelling , possibly facilitated through its capacity to associate with actin and mediate the reorganization of microfilaments [30] , [31] . In neuronal cells , GAS7 expression is critical for neurite formation [30] , [32] . MYOC , the major glaucoma gene previously associated with elevated IOP cases , also affects the actin cytoskeletal structure and neurite outgrowth [33] . Whereas MYOC has an inhibitory effect on neurite outgrowth , GAS7 is involved in the formation of neurites . Interestingly , experimental ischemic retinal damage in rats , resembling retinal damage due to glaucoma , leads to extensive remodelling of inner retinal neurons [34] . GAS7 may also contribute to remodelling of the TM , as is the case for the myocilin protein which has been shown to alter the actin structure and modulate TM cell morphogenesis [35] . GAS7 interacts with MYOC , as well as with other genes implicated in glaucoma , such as OPTN , WDR36 , CAV1 , NOS2 , FOXC1 , APOE , APP and CLU ( Figure 5; www . ingenuity . com ) . The latter three genes are primarily known for their association with Alzheimer's Disease , a neurodegenerative disease previously linked to glaucoma [36] . GAS7 interacts with both MYOC and CAV1 through β-catenin ( CTNNB1 ) and RhoA ( RHOA ) . B-catenin anchors the actin cytoskeleton and is part of the Wnt signalling pathway , which has previously been implicated in trabecular outflow regulation [37] , [38] . RhoA signalling regulates the intracellular levels of phosphorylated myosin light chain , which directly influence trabecular meshwork cellular contraction and thus aqueous humor outflow [39] . Finally , GAS7 is regulated by transforming growth factor ( TGF ) beta , which has previously been implicated in trabecular outflow as well as in the development of the optic disc ( the primary site of neuronal damage by glaucoma ) [40]–[42] . The frequency of the GAS7 rs11656696 A-allele is 0 . 44 in the HapMap CEU population of European ancestry whereas it is 0 . 12 in the HapMap Yorubian population of African ancestry . The lower frequency of the A-allele in the African population is consistent with the higher prevalence of glaucoma with elevated IOP in this population and warrants further research into the association of rs11656696 with IOP and glaucoma in African populations . The second variant that we found to be associated with IOP and glaucoma was rs7555523 in TMCO1 , a highly evolutionary conserved gene of largely unknown function [43] , [44] . TMCO1 has recently been associated with severe glaucomatous visual field loss , indicating that this locus may influence both the normal variance in IOP and the risk of developing severe glaucoma [11] . These findings support the hypothesis that studies of IOP can assist in identifying susceptibility genes for glaucoma . Rs7555523 is located in a region which previously showed suggestive evidence for linkage with blood pressure [45] . IOP and blood pressure have already been shown to correlate [46] . TMCO1 is highly expressed in the human TM and CB , which together regulate IOP , and in the retina [28] . TMCO1 interacts with CAV1 via VHL ( Figure 5 ) . A homozygous frameshift mutation in TMCO1 has been associated with a genetic syndrome involving multiple organ systems , including renal agenesis and hydronephrosis [43] . Extensive ophthalmic examination was not reported , however a high incidence of strabismus was noticed . No previous GWASs of IOP have been conducted to date . When comparing our findings to those of association studies of glaucoma , we found an overlap with 3 regions . First , we replicated the association with the TMCO1 region , as has been described in the previous paragraph . Second , rs4236601 in the CAV1-CAV2 region , previously identified in Caucasians , was consistently associated with increased IOP in our discovery meta-analysis [9] . Our findings in this region did not reach genome-wide significance . However , multiple testing adjustment by using a Bonferroni correction for the 12 SNPs evaluated ( Table S3 ) yields a criterion for significance of p<4×10−3 . Thus , our findings strongly support an association between the CAV1-CAV2 region and IOP , despite the fact that the original report that identified CAV1-CAV2 did not find evidence for a stronger relation to high pressure glaucoma . Third , a locus on chromosome 10p , which had previously been identified in Japan , also passed this Bonferroni threshold [10] . Similar to Nakano and coworkers , we could not assign a specific glaucoma disease gene to this region . The replication of this locus in our study is remarkable as most glaucoma patients in Japan present with normal tension glaucoma ( i . e . , glaucoma with IOP≤21 mmHg ) . Our study design had three potential limitations . First , we did not measure central corneal thickness ( CCT ) in the majority of the participants of the discovery cohorts . CCT is an important determinant of IOP measurements and may be an IOP-independent risk factor for glaucoma [47] , [48] . The genes involved in CCT may also associate with IOP and glaucoma . CCT has previously been reported to account for 1–6% of the variance in IOP measured with Goldmann applanation tonometry [49]–[52] . Heritability estimates of CCT range from 0 . 68 to 0 . 95 [53]–[55] , implying that this trait is even more heritable than IOP . Because we did not include CCT as a covariate in our discovery analyses , the identified SNPs may determine CCT rather than IOP . To test this hypothesis , we assessed whether the identified SNPs were associated with CCT in a randomly selected subpopulation of 784 participants from RS-I for whom CCT data were available . None of the 7 SNPs identified in the discovery meta-analysis was associated with CCT ( p>0 . 24 ) . The results for rs11656696 ( p = 0 . 28 ) and for rs7555523 ( p = 0 . 31 ) suggest that the associations of these SNPs with IOP were not explained by CCT . Furthermore , in a recent GWAS conducted in the Australian twins and TwinsUK cohorts , these SNPs were not associated with CCT [56] . We also assessed the associations of rs11656696 and rs75555623 with IOP in the TwinsUK cohort after including CCT as a covariate in the multivariate model . The association changed from −0 . 316 ( 95%CI = −0 . 536–−0 . 096 ) to −0 . 400 ( 95%CI = −0 . 620–−0 . 180 ) for rs11656696 and from 0 . 242 ( 95%CI = −0 . 048–0 . 532 ) to 0 . 220 ( 95%CI = −0 . 080–0 . 520 ) for rs7555523 after correction for CCT , suggesting that the controlling for CCT only produces relatively minor changes with respect to effect size and significance of association . Second , in the gene discovery analyses , the initial IOP levels were not known for the participants who received IOP lowering medication or who had a history of IOP lowering surgery . We imputed these IOPs , because ( particularly in the elderly population of RS-I ) participants with extreme IOPs , which are likely to be genetically determined , are otherwise excluded . Similar approaches have been applied to research of blood pressure , where an analogous problem occurs: those with the higher blood pressures are otherwise excluded [57] , [58] . Although the imputations for IOP lowering medication are based on a large meta-analysis [59] , the justification for the imputations for IOP lowering surgery is not based on empirical evidence . Exclusion of any participants who received IOP lowering treatment or who had received this treatment in the past ( either by medication or surgically ) , did not substantially change the betas for rs11656696 and rs7555523 ( see Text S1 ) . However , for rs11656696 , it did result in a loss of statistical power , as the participants on treatment had a significantly ( p = 8 . 1×10−6 ) lower frequency of the protective A-allele ( odds ratio = 0 . 58 , 95%CI = 0 . 45–0 . 74 ) . Third , some replication cohorts differed from the discovery cohorts with respect to their age , sex or disease status . The participants of the Australian Twin study and DCCT/EDIC were evidently younger than the participants of the other cohorts were . Aging has previously been associated with an increase in the accumulation of extracellular material in the trabecular meshwork , as well as a decrease in trabecular meshwork cells [60] . A different genetic mechanism underlying aqueous humor dynamics in different age categories may therefore explain the lack of association of IOP with rs11656696 and rs7555523 in these younger cohorts . In the TwinsUK cohort , 97 . 5% of the participants were women . Sex was not significantly related to IOP in the discovery cohorts . Moreover , the results from the TwinsUK strongly replicate the association of IOP with rs11656696 , and are also supportive for the association with rs7555523 . We therefore believe that the differences in sex have not substantially influenced our results . Finally , DCCT/EDIC comprised only participants with type I diabetes mellitus . DCCT/EDIC was the only cohort that showed an inconsistent effect for rs11656696 . The reduced association in the joint analysis when compared to the discovery analysis ( p-value increased from 9 . 8×10−9 to 1 . 4×10−8 ) was mainly driven by DCCT/EDIC . When DCCT/EDIC was not included , the association in the joint analysis became stronger than it was in the discovery ( p-value decreased from 9 . 8×10−9 to p = 1 . 1×10−9 ) . Although the association of type 1 diabetes with IOP is controversial , any changes in IOP may have different origins , which may explain the inconsistent replication results in this cohort . In conclusion , this genome-wide association study in 8 independent Caucasian cohorts identified rs11656696 in GAS7 at chromosome 17p13 . 1 and rs7555523 in TMCO1 at chromosome 1q24 . 1 as common genetic variants associated with IOP . The variants were also marginally associated with glaucoma . GAS7 and TMCO1 are expressed in ocular cells and tissues implicated in glaucoma . Biochemical protein interactions with known glaucoma disease genes , as well as functional data support the involvement of these genes in aqueous humor dynamics and glaucomatous neuropathy .
All participating studies adhered to the tenets of the Declaration of Helsinki and were approved by their Medical Ethics Committees . Written , informed consent was obtained from all participants . For the gene discovery phase , we combined data of 11 , 972 participants derived from 4 large , independent population-based cohort studies in The Netherlands: the Rotterdam Study cohort I ( RS-I ) , RS-II , RS-III , and the Erasmus Rucphen Family ( ERF ) Study . Replication of the findings was sought in 4 independent populations: the TwinsUK Adult Twin study , the Australian Twin Study , the Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications study ( DCCT/EDIC ) [61] , and the Wellcome Trust Case-Control Consortium 2 / Blue Mountains Eye Study ( WTCCC2/BMES ) . Clinical relevance of the identified loci was assessed by evaluating associations between the variants and glaucoma . To this end , we performed case-control analyses using 4 different glaucoma cohorts from The Netherlands and Germany . Finally , we examined the expression levels of the identified candidate genes in ocular tissues . SNPs showing strongest association in the discovery phase were carried forward and assessed for association with IOP in 2 , 235 participants from the TwinsUK Study , 1 , 807 from the Australian Twin Study , 1 , 304 from the DCCT/EDIC Study , and 2 , 136 from the WTCCC2/BMES Study . The TwinsUK , Australian Twin and WTCCC2/BMES were also population-based studies , and participants were ascertained regardless of their phenotypes or clinical status . The DCCT/EDIC study comprised only patients with type 1 diabetes included in a preventive trial . Descriptions of the study populations , clinical examinations , and genotyping methods of the replication cohorts are provided in Text S1 and Table S4 . Two independent expression studies were performed . In the first , retinal expression data were obtained essentially as described by Booij and colleagues [70] . Human healthy donor eyes ( n = 4 ) were collected in collaboration with the Dutch Cornea Bank and snap frozen . History of the donor eyes revealed no glaucoma or other eye diseases . Cryosections ( 20 µm ) of the CB were cut and mounted on PEN membrane slides ( Carl Zeiss MicroImaging ) . With the use of laser dissection microscopy , the CB epithelium was cut out . RNA isolation ( RNeasy Micro Kit , Qiagen ) and amplification ( Amino Allyl MessageAmp II aRNA Amplification , Ambion Applied Biosystems ) were conducted according to the manufacturers' protocols . After labelling of experimental aRNA with Cy5 and reference aRNA ( composed of RPE and choroid ) with Cy3 , we performed hybridization on catalogue human 4×44k microarrays ( Agilent Technologies ) . Mean expression intensity data were normalized with R software ( R Development Core Team , 2009 ) . The mean expression data were further subdivided based on percentiles in Windows Excel . We used the 90th , 50th and 10th percentile of the mean expression intensity to categorize our data into groups with high ( >90th ) , moderate ( 50th–90th ) , low ( 10th–50th ) and very low ( <10th ) expression . In the second expression study , ocular tissues were obtained for quantitative real-time PCR from four donor eyes ( age: 81 . 2±4 . 5 years , 2 female , 2 male ) without any known ocular disease . These eyes were obtained at autopsy and were processed within 8 hours after death . Informed consent to tissue donation was obtained from the donors or their relatives , and the protocol of the study was approved by the local Ethics Committee and adhered to the tenets of the Declaration of Helsinki for experiments involving human tissue . Total RNA was extracted from various ocular tissues by using the RNeasy kit ( Quiagen , Hilden , Germany ) including an on-column DNase I digestion step . First strand cDNA synthesis was performed by using 0 . 1 µg of total RNA , 200 U Superscript II reverse transcriptase ( Invitrogen; Karlsruhe , Germany ) , and 500 ng oligo dT primers ( Roche Diagnostics; Mannheim , Germany ) in a 20 µl reaction volume . Quantitative real-time PCR was performed by means of the MyIQ thermal cycler and software ( Biorad , Munich , Germany ) . PCR reactions ( 25 µl ) contained 2 µl of first-strand cDNA , 0 . 4 µM each of upstream- and downstream-primer , 3 . 0 mM MgCl2 , and 1× SsoFast EvaGreen Supermix ( Biorad ) . All samples were analyzed in duplicates by means of a program with an initial denaturation step of 95°C for 3 minutes and 40 cycles of 95°C for 5 seconds , and 64°C ( GAS7 and TMCO1 ) or 62°C ( GAPDH ) for 15 seconds . Gene-specific primers ( Eurofins , Anzing , Germany ) were designed to anneal with sequences located in different exons by means of Primer 3 software ( http://fokker . wi . mit . edu/primer3/input . htm ) and are summarized in Table S6 . For quantification , serially diluted standard curves were run in parallel , and amplification specificity was checked using melt curve analysis . For normalization of gene expression levels , mRNA ratios relative to the house-keeping gene GAPDH were calculated .
|
Glaucoma is a major eye disease in the elderly and is the second leading cause of blindness worldwide . The numerous familial glaucoma cases , as well as evidence from epidemiological and twin studies , strongly support a genetic component in developing glaucoma . However , it has proven difficult to identify the specific genes involved . Intraocular pressure ( IOP ) is the major risk factor for glaucoma and the only target for the current glaucoma therapy . IOP has been shown to be highly heritable . We investigated the role of common genetic variants in IOP by performing a genome-wide association study . Discovery analyses in 11 , 972 participants and subsequent replication analyses in a further 7 , 482 participants yielded two common genetic variants that were associated with IOP . The first ( rs11656696 ) is located in GAS7 at chromosome 17 , the second ( rs7555523 ) in TMCO1 at chromosome 1 . Both variants were associated with glaucoma in a meta-analysis of 4 case-control studies . GAS7 and TMCO1 are expressed in the ocular tissues that are involved in glaucoma . Both genes functionally interact with the known glaucoma disease genes . These data suggest that we have identified two genes involved in IOP regulation and glaucomatous neuropathy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"medicine",
"public",
"health",
"and",
"epidemiology",
"quantitative",
"traits",
"glaucoma",
"genetic",
"epidemiology",
"disease",
"mapping",
"epidemiology",
"gene",
"expression",
"biology",
"heredity",
"ophthalmology",
"genetics",
"human",
"genetics",
"genetics",
"and",
"genomics"
] |
2012
|
Common Genetic Determinants of Intraocular Pressure and Primary Open-Angle Glaucoma
|
Elucidating genetic mechanisms of adaptation is a goal of central importance in evolutionary biology , yet few empirical studies have succeeded in documenting causal links between molecular variation and organismal fitness in natural populations . Here we report a population genetic analysis of a two-locus α-globin polymorphism that underlies physiological adaptation to high-altitude hypoxia in natural populations of deer mice , Peromyscus maniculatus . This system provides a rare opportunity to examine the molecular underpinnings of fitness-related variation in protein function that can be related to a well-defined selection pressure . We surveyed DNA sequence variation in the duplicated α-globin genes of P . maniculatus from high- and low-altitude localities ( i ) to identify the specific mutations that may be responsible for the divergent fine-tuning of hemoglobin function and ( ii ) to test whether the genes exhibit the expected signature of diversifying selection between populations that inhabit different elevational zones . Results demonstrate that functionally distinct protein alleles are maintained as a long-term balanced polymorphism and that adaptive modifications of hemoglobin function are produced by the independent or joint effects of five amino acid mutations that modulate oxygen-binding affinity .
Many long-standing questions about genetic mechanisms of adaptation remain unanswered due to the difficulty of integrating molecular data with evidence for causal effects on organismal fitness . In principle , progress could be made by identifying key proteins or key components of protein interaction networks that are known to mediate an adaptive response to some specific environmental challenge . Analysis of DNA sequence variation at the underlying genes could then guide the identification of specific nucleotide changes that are responsible for functional modifications of biochemical or physiological pathways , and could also shed light on the role of natural selection in maintaining the observed variation in protein function [1 , 2] . Although this approach holds much promise , very few studies have successfully documented a mechanistic link between allelic variation in protein function and fitness-related variation in whole-organism physiology [3–7] . Hemoglobin polymorphism in the deer mouse , Peromyscus maniculatus , represents an especially promising system for examining the molecular underpinnings of physiological adaptation to different environments . P . maniculatus has the broadest altitudinal range of any North American mammal , as the species is continuously distributed from sea-level environments to alpine environments at elevations above 4 , 300 m . At 4 , 300 m , the partial pressure of oxygen ( Po2 ) is approximately 55% of the sea-level value , and the resultant hypoxia imposes severe constraints on aerobic metabolism . Experimental evidence indicates that adaptive variation in blood biochemistry among mice from different elevations is associated with a complex hemoglobin polymorphism [8–11] . Specifically , experimental crosses involving wild-derived strains of P . maniculatus revealed that variation in blood oxygen affinity is strongly associated with allelic variation at two closely linked gene duplicates that encode the α-chain subunits of adult hemoglobin [12 , 13] . In P . maniculatus , the two α-globin gene duplicates , Hba and Hbc , are each polymorphic for two main classes of electrophoretically detectable protein alleles , Hba0 , Hba1 , Hbc0 , and Hbc1 [8 , 9 , 14 , 15] . These loci are closely linked , and because of strong linkage disequilibrium , nearly all α-globin haplotypes fall into two main classes: a0c0 and a1c1 . The three nonrecombinant genotypes exhibit a highly consistent rank-order of blood oxygen affinities when tested under both high- and low-altitude conditions: mice with the a0c0/a0c0 genotype exhibit the highest affinity ( the most left-shifted oxygen dissociation curve ) , mice with the a1c1/a1c1 genotype exhibit the lowest affinity ( the most right-shifted dissociation curve ) , and the a0c0/a1c1 double heterozygotes are intermediate [12 , 13] . In these experiments , the wild-derived strains of mice carried different α-globin haplotypes in identical-by-descent condition , and the effects of the two genes were isolated against a randomized genetic background . In addition to the effects on blood biochemistry , the phenotypic effects of these α-globin genes are also manifest at the level of whole-organism physiology . In the context of adaptation to high-altitude hypoxia , one especially important measure of physiological performance is Vo2max , which is defined as the maximal rate of oxygen consumption elicited by aerobic exercise or cold exposure . Vo2max sets the upper limit on two important types of physiological performance: capacity for sustained activity ( aerobic capacity ) and internal heat production ( thermogenic capacity ) . This measure of aerobic performance shows a striking pattern of variation among mice with different α-globin genotypes: Vo2max is highest for a0c0/a0c0 mice when tested at an altitude of 3 , 800 m , whereas Vo2max is highest for a1c1/a1c1 mice when tested at 340 m [12 , 13] . Consistent with predictions based on physiological considerations , survivorship studies of free-ranging P . maniculatus revealed that Vo2max is subject to strong directional selection in high-altitude populations of this species [16] . This system therefore fulfills several key requirements for a comprehensive study of adaptive evolution at the molecular level [3] , as there are clearly defined connections between genotype , phenotype , and Darwinian fitness in natural populations . The physiological data indicate that the high-affinity a0c0/a0c0 genotype is associated with superior physiological performance under hypoxic conditions at high altitude but is associated with poor performance ( relative to the a1c1/a1c1 genotype ) in the oxygen-rich environment at sea level . In both altitudinal extremes , the a0c0/a1c1 heterozygotes are generally intermediate with respect to both blood oxygen affinity and Vo2max [12 , 13] . This rank order of genotypic effects appears to be attributable to the fact that the possession of high-affinity hemoglobin facilitates pulmonary oxygen loading in high-altitude environments that are characterized by a low Po2 but hinders the release of oxygen to metabolizing tissues in low-altitude environments with relatively high Po2 . In summary , this system represents a unique case where fitness-related variation in whole-organism physiology can be related to a relatively simple biochemical phenotype ( blood oxygen affinity ) that has a well-characterized genetic basis . Thus , examination of DNA sequence variation at the underlying α-globin genes should reveal the specific molecular changes that underlie physiological adaptation to high-altitude hypoxia . For example , it may be possible to determine whether adaptive modifications of hemoglobin function involve just a few amino acid mutations of large effect or many mutations of individually small effect [3 , 17] . Mutations of large effect might be expected to involve heme–protein contacts , intersubunit contacts , or binding sites for heterotropic ligands that modulate hemoglobin-oxygen affinity [17–20] . By contrast , the gradual accumulation of minor mutations may produce adaptive shifts in oxygen affinity through more subtle and indirect effects on the stereochemistry of ligand binding . In addition to elucidating molecular mechanisms of physiological adaptation , the analysis of DNA sequence variation at fitness-related genes should provide insights into the mode of selection that is responsible for maintaining variation in protein function . Here we report a population genetic analysis of the two-locus α-globin polymorphism in high- and low-altitude samples of P . maniculatus . The objectives are ( i ) to identify the specific mutations that may be responsible for the divergent fine-tuning of hemoglobin function and ( ii ) to test whether patterns of nucleotide variation at the two α-globin genes exhibit the expected signature of diversifying selection between high- and low-altitude populations .
Analysis of DNA sequence variation revealed that the two α-globin paralogs of P . maniculatus have experienced a history of biased gene conversion: 32 . 8% of the sampled 3′ alleles have been partially converted by 5′ α-globin ( median tract length , 114 base pairs [bp]; range , 3 to 257 bp ) , and 10 . 8% of the sampled 5′ alleles have been partially converted by 3′ α-globin ( median tract length , 29 bp; range , 13 to 225 bp ) . Excluding alleles with identified gene conversion tracts , the two α-globin paralogs of P . maniculatus are distinguished by eight fixed differences at nonsynonymous sites , which result in six amino acid substitutions because there are two separate instances in which a pair of nonsynonymous mutations alter the same codon: 34 ( B15 ) Cys/Ser→Glu , 36 ( CD1 ) Phe/Ser→His , 57 ( E6 ) Gly/Ala→Thr , 58 ( E7 ) His→Gln , 71 ( EF1 ) Gly/Ser→Asp , and 72 ( EF2 ) His→Asn ( Figure 1 ) . Of the six amino acid substitutions that distinguish the two α-globin paralogs , the 58 ( E7 ) His→Gln substitution in the 3′ α-globin gene is predicted to have the most important functional consequences with respect to ligand binding kinetics . In all mammals studied to date , the E7 residue of the highly conserved E-helix domain is a histidine that plays a critical role in the reversible binding of oxygen to the heme iron . Specifically , the Nɛ atom of the imidazole side-chain forms a hydrogen bond with the free atom of the bound oxygen molecule [22–24] . In the 3′ α-globin gene of P . maniculatus , the substitution of glutamine for histidine at this position increases the Nɛ–O bond distance by 1 . 1 to 1 . 9 Å , thereby producing a minor shift in the three-dimensional coordinates of the heme–ligand complex ( Table 1 ) . Protein engineering studies of human hemoglobin have revealed that this 58 ( E7 ) His→Gln substitution results in an increased oxygen-binding affinity at low Po2 relative to wild-type ( E7 ) His-containing hemoglobin [25] . The important physiological implication is that the erythrocytes of deer mice contain a heterogeneous mixture of ( E7 ) His- and ( E7 ) Gln-containing hemoglobin isoforms that have different oxygen-binding affinities . The possession of multiple adult hemoglobin isoforms with different oxygen affinities has only been documented in one other mammal , the yak , Bos grunniens [26] , which inhabits high alpine environments at elevations of up to 5 , 400 m on the Tibetan Plateau . The possession of multiple hemoglobin isoforms has also been documented in birds that fly at extremely high altitudes [17 , 19 , 27–30] . These patterns suggest the hypothesis that the possession of multiple hemoglobin isoforms provides a mechanism for fine-tuning blood oxygen affinity in response to ambient variation in oxygen tension ( in the case of animals that experience different oxygen environments on a daily or seasonal basis ) or in response to variation in metabolic demands in a uniformly hypoxic environment . In the case of deer mice , an obvious prediction of this hypothesis is that some mechanism of regulatory control allows the stoichiometric ratio of 5′ and 3′ α-chain hemoglobin isoforms to be adjusted in response to metabolic demand . Functional divergence between the two α-globin paralogs of P . maniculatus stands in stark contrast to the pattern of concerted evolution that has been documented for the α-globin paralogs of almost all other mammals studied to date [31] . For example , the α-globin gene duplicates of humans are typically identical in sequence and therefore encode identical polypeptides [32] . Even after accounting for shared polymorphisms that are attributable to gene conversion between the two α-globin paralogs , both genes are characterized by extremely high levels of nucleotide diversity at both silent sites ( synonymous and noncoding ) and replacement sites ( Table 2 ) . In the 3′ α-globin gene , conversion tracts encompass large portions of the coding sequence and result in a 1 . 51-fold increase in total nucleotide diversity . In addition to the variation introduced by gene conversion between the two paralogs , both genes have also experienced high rates of intragenic recombination ( RM , the inferred minimum number of recombination events = 14 for 5′ α-globin , 95% confidence interval = 6 to 16 , and RM = 8 for 3′ α-globin , 95% confidence interval = 5 to 14 ) . The 3′ α-globin orthologs of P . maniculatus and a closely related congener , Peromyscus leucopus , are distinguished by seven amino acid substitutions: 23 ( B4 ) Asp→Gln , 47 ( CD12 ) His→Asp , 50 ( CD15 ) Pro→His , 57 ( E6 ) Gly→Thr , 58 ( E7 ) Gln→His , 71 ( EF1 ) Asn→Asp , and 113 ( GH1 ) His→Gln ( Figure 1 ) . By contrast , there are no fixed amino acid differences between the 5′ α-globin orthologs of these two species . Within P . maniculatus , both genes exhibit extensive amino acid replacement polymorphism: 5′ α-globin segregates 21 nonsynonymous mutations and 3′ α-globin segregates 33 nonsynonymous mutations . For neutral genes that are evolving under the joint influence of mutation and genetic drift , the level of within-species polymorphism is expected to be positively correlated with the level of between-species divergence , and this correlation should hold for both silent and replacement changes [33] . To test for a departure from this neutral expectation , we used Fisher's exact test of independence to assess whether the ratio of amino acid replacement to silent polymorphisms in each of the two α-globin genes of P . maniculatus differed from the ratio of replacement to silent fixed differences in comparison with their respective orthologs in P . leucopus . Excluding alleles with identified gene conversion tracts , the tests revealed that both genes are characterized by a significant excess of replacement polymorphism ( Table 3 ) . At the 5′ α-globin gene , ten of the 21 nonsynonymous mutations exhibited significant allele frequency differences between high- and low-altitude samples , and at six of these sites , the derived mutation was present at a frequency greater than 0 . 10 in the high-altitude sample . At the 3′ α-globin gene , 21 of the 33 nonsynonymous mutations exhibited significant allele frequency differences between high- and low-altitude samples , and at eight of these sites , the derived mutation was present at a frequency greater than 0 . 10 in the high-altitude sample ( Table S1 ) . This pattern of polymorphism is consistent with a model of diversifying selection that favors different protein variants in different elevational zones [9 , 21] . In contrast to patterns of nucleotide variation at five other unlinked nuclear loci , both α-globin paralogs exhibited a highly significant excess of intragenic linkage disequilibrium as revealed by Kelly's Zns statistic [34] ( Table 4 ) . This excess linkage disequilibrium , in combination with the extremely high levels of nucleotide diversity at both genes , is consistent with a model of diversifying selection in which the maintenance of balanced polymorphism produces nonrandom associations between alleles that are specific to different haplotype backgrounds [35 , 36] . In addition to high levels of linkage disequilibrium within both α-globin paralogs , intergenic comparisons of amino acid replacement polymorphism revealed a highly significant level of genotypic linkage disequilibrium between the two paralogs ( p < 0 . 001 ) . This result is consistent with the pattern of two-locus linkage disequilibrium documented in previous electrophoretic surveys [8 , 14 , 15 , 21] . In principle , it should be possible to determine whether a given polymorphism is subject to divergent selection between different environments by comparing the locus-specific level of differentiation to a multilocus average for unlinked neutral markers [37 , 38] . Again , consistent with a model of diversifying selection between different elevational zones , both α-globin genes are characterized by higher levels of altitudinal differentiation than five other unlinked nuclear loci ( Table 4 ) . However , it is important to recognize that point estimates of genetic differentiation have a large variance , which stems primarily from stochastic variance among the genealogies of unlinked loci . To account for this source of variance , we used coalescent simulations to generate a null distribution of site-specific FST values under a neutral model of population structure ( see Materials and Methods ) . For each of the two α-globin genes , the null distribution was then used to identify specific replacement polymorphisms that exhibit levels of altitudinal differentiation that are too high to be explained by drift alone . If high blood oxygen affinity represents a derived condition for high-altitude deer mice , then elevated levels of altitudinal differentiation at specific α-globin polymorphisms should be attributable to the increased frequency of derived mutations in the high-altitude population . Thus , the best candidate sites for divergent selection on hemoglobin function are those that are characterized by an unusually high FST value and an unusually high frequency of the derived allele at high altitude . At the 5′ α-globin gene , replacement polymorphisms at five closely linked residue positions , 50 ( CD15 ) His/Pro , 57 ( E6 ) Gly/Ala , 60 ( E9 ) Ala/Gly , 64 ( E13 ) Asp/Gly , and 71 ( EF1 ) Gly/Ser , and one more distant position , 116 ( GH4 ) Glu/Asp , were characterized by the highest site-specific FST values in comparisons between the high-altitude sample ( Mt . Evans , Colorado , United States ) and each of the two low-altitude samples ( Yuma County , Colorado , and Pawnee County , Kansas , United States ) , and site-specific levels of altitudinal differentiation exceeded neutral expectations in both pairwise comparisons ( Figure S1A and S1B ) . The replacement polymorphisms at sites 50 , 57 , 60 , 64 , and 71 are in highly significant linkage disequilibrium with one another ( p < 0 . 00001 in all pairwise comparisons ) and with site 116 ( p < 0 . 025 in all pairwise comparisons; Figure 2 ) . These nonrandom associations are not solely attributable to reduced recombination , as pairwise linkage disequilibria decay to background levels over a distance of less than 600 bp across the gene ( Figure S2 ) . The fact that two-locus α-globin haplotypes are maintained despite this short-range decay in linkage disequilibrium is consistent with the hypothesis of Chappell et al . [13] that coadapted combinations of protein alleles at the two genes are maintained by epistatic selection . In the case of sites 50 , 64 , and 71 , the derived variants are present at unusually high frequency in the high-altitude sample ( Table S1 ) and exhibit a monotonic increase in frequency as a positive function of altitude ( Figure 3 ) . Moreover , high levels of differentiation at the five closely linked replacement polymorphisms ( sites 50 , 57 , 60 , 64 , and 71 ) and the replacement polymorphism at site 116 are each associated with elevated FST values at closely linked silent sites ( Figure S1 ) . Because the simulation results suggest that observed levels of altitudinal differentiation at these six replacement polymorphisms are too high to be explained by a neutral model of population structure , the elevated FST values at closely linked silent sites may be attributable to genetic hitchhiking [39 , 40] . Of the six replacement polymorphisms in 5′ α-globin that exhibit higher-than-expected FST values , the 64 ( E13 ) Asp/Gly polymorphism is predicted to have the most important functional consequences with respect to oxygen-binding kinetics of the hemoglobin protein . At site 64 ( E13 ) , substitution of the uncharged glycine residue for the negatively charged aspartic acid residue involves the substitution of a single H atom side-chain for a much larger CH2-COOH side chain ( Figure 4 ) . As a result of the charge and size differences between these two amino acid residues , this 64 ( E13 ) Asp→Gly substitution reduces steric hindrance to oxygen binding in the heme pocket and is predicted to produce a substantial increase in oxygen-binding affinity ( Table 5 ) . The predicted effect of this mutation is corroborated by functional studies of the same 64 ( E13 ) Asp→Gly mutation in human hemoglobin . Functional studies revealed that the rare hemoglobin variant defined by the 64 ( E13 ) Asp→Gly mutation , “Hemoglobin Guangzhou-Hangzhou , ” is characterized by an increased oxygen-binding affinity [41 , 42] . In summary , the erythrocytes of deer mice contain an extraordinary diversity of functionally distinct hemoglobin isoforms due to variation between duplicated α-globin genes in addition to allelic variation that is segregating at each of the two genes . We suggest that functionally important mutations that distinguish the two α-globin paralogs , such as the 58 ( E7 ) His→Gln substitution in the 3′ α-globin gene , are primarily involved in maintaining a physiological division of labor between 5′ and 3′ α-chain hemoglobin tetramers and may therefore provide a regulatory reserve of oxygen transport capacity . By contrast , functionally important mutations that distinguish high- and low-altitude alleles , such as the 64 ( E13 ) Asp/Gly polymorphism in the 5′ α-globin gene , are primarily involved in adaptation of hemoglobin function to different elevational zones . When two or more functionally distinct alleles are maintained by some form of balancing selection , such as diversifying selection that favors different genotypes in spatially separated habitats , coalescence times for sequences belonging to different selectively defined allele classes are expected to be much longer than those for sequences belonging to the same allele class [35 , 36 , 40 , 43 , 44] . Consequently , comparisons of sequence variation between different allele classes should reveal a pronounced peak in nucleotide divergence that is centered on the selected site that defines the different classes . To determine whether the two α-globin paralogs exhibit this characteristic signature of diversifying selection , we conducted a sliding-window analysis of silent site diversity in comparisons between functionally distinct allele classes of each α-globin gene . Silent-site diversity within each allele class was estimated by π [45] , and silent-site divergence between allele classes was estimated by Dxy , the average number of nucleotide substitutions per site ( Equation 12 . 65 in Nei and Kumar [45] ) . In the case of 5′ α-globin , the sliding-window analysis revealed a pronounced peak of silent site divergence in comparisons between alleles defined by the five closely linked replacement polymorphisms that span the E-helix domain of the encoded polypeptide ( sites 50 , 57 , 60 , 64 , and 71; Figure 5 ) . This peak of silent site divergence between the two allele classes is directly centered on the region of exon 2 that harbors all five class-defining replacement polymorphisms , as predicted if one or more of these sites represents the target of divergent selection [35 , 40 , 43] . In the window spanning this region of exon 2 , silent site divergence between the two allele classes is 0 . 121 ( Jukes-Cantor corrected ) , a value that exceeds estimates of average silent site divergence between the 5′ α-globin orthologs of P . maniculatus and P . leucopus ( Table 2 ) . This extraordinarily high level of sequence divergence between the two allele classes is consistent with the maintenance of two functionally divergent protein alleles as a long-term balanced polymorphism . Based on the deduced isoelectric points of the translated amino acid sequences , the five-site protein haplotype that predominates at high altitude ( 50Pro/57Gly/60Ala/64Gly/71Ser ) and the alternative haplotype that predominates at low altitude ( 50His/57Asp/60Gly/64Asp/71Gly ) are referable to the Hba0 and Hba1 alleles , respectively , of Snyder et al . [21] . The predicted rank-order of oxygen-binding affinities for these alleles ( Table 5 ) is also consistent with previous experimental results [10 , 12 , 13] . On the basis of previous physiological experiments [12 , 13] , the “high affinity” protein haplotype is expected to confer enhanced physiological performance under hypoxic conditions at high altitude , whereas the alternative “low affinity” haplotype is expected to confer enhanced physiological performance at low altitude . The fact that the 5′ α-globin gene exhibits such a clear signature of diversifying selection is consistent with the idea that protein alleles with different oxygen-binding affinities are favored in different elevational zones [9 , 10 , 12 , 13 , 21] . Because hemoglobins from different species are typically distinguished by multiple amino acid substitutions , only a fraction of which may be functionally significant , it may often be difficult to identify the causal variants that are directly involved in molecular adaptation to different environments . Species like P . maniculatus provide the opportunity to pinpoint the specific amino acid changes that are involved in hemoglobin adaptation because it is possible to compare the sequences of functionally distinct alleles that are distinguished by a relatively small number of mutations . Adaptive modifications of hemoglobin function are generally thought to involve key changes in heme–protein contacts [such as the 58 ( E7 ) His→Gln substitution that distinguishes the two α-globin paralogs] , intersubunit contacts , or binding sites for heterotropic ligands [17–20] . In contrast to this prevailing view , sequence variation at 5′ α-globin reveals that the enhanced oxygen affinity of the high-altitude protein allele is primarily attributable to a charge-changing 64 ( E13 ) Asp→Gly mutation on the exterior of the E-helix that reduces steric hindrance to oxygen binding in the heme pocket . Results of this study therefore reveal an unexpected mechanism underlying allelic differences in hemoglobin-oxygen affinity . The population genetic analysis of α-globin polymorphism also provides an opportunity to assess the possible adaptive significance of epistasis among different mutations within the same gene . For example , in the case of 5′ α-globin , the derived glycine mutant at site 64 ( E13 ) is in nearly perfect linkage disequilibrium with the derived proline mutant at site 50 ( CD15 ) . The latter residue is located in an exterior , interhelical segment of the α-globin polypeptide and therefore has no direct effect on ligand binding . However , it is worth noting that the change from a positively charged histidine residue to an uncharged proline residue at site 50 ( CD15 ) exactly offsets the change from a negatively charged aspartic acid residue to an uncharged glycine residue at site 64 ( E13 ) . This suggests the hypothesis that the 64 ( E13 ) Asp→Gly mutation represents the primary adaptive change that produced the enhanced hemoglobin-oxygen affinity and that the 50 ( CD15 ) His→Pro mutation represents a compensatory change that maintains the same net surface charge of the molecule . Functional experiments involving site-directed mutagenesis will be required to measure the independent and joint effects of these mutations on hemoglobin-oxygen affinity . In addition to the possible role of epistasis among mutations in the same gene , further work is also needed to assess the potential for epistatic interactions among genes such as the α- and β-globin loci that encode interacting subunits of the same tetrameric protein .
Mice were live-trapped and handled in accordance with Institutional Animal Care and Use Committee guidelines . Sample sizes , sample localities , and specimen numbers are as follows: summit of Mt . Evans , Clear Creek County , Colorado ( 4 , 347 m; n = 15 , JFS 70 , 71 , 73 , 74 , 76 , 80 to 82 , 87 , 88 , 95 , 97 , 98 , 100 , and 106 ) , Bonny Reservoir , Yuma County , Colorado ( 1 , 158 m; n = 15 , JFS 107 , 114 , 117 , 122 to 124 , 132 , 134 to 137 , and 140 to 143 ) , and Fort Larned National Monument , Pawnee County , Kansas ( 620 m; n = 11 , NK 53239 , 53245 , 53247 to 53249 , 53251 , 53252 , 53256 , 53265 , 53266 , and 53268 ) . After each mouse was killed , whole blood was collected by cardiac puncture ( in the case of mice collected from Mt . Evans and Yuma County ) and liver tissue was frozen in liquid nitrogen as a source of genomic DNA . Specimens were deposited in the vertebrate collection at the University of Nebraska State Museum and the Museum of Southwestern Biology , University of New Mexico . DNA was extracted from frozen liver of each mouse using DNeasy kits ( Qiagen , http://www . qiagen . com ) . Adult α-globin genes are duplicated in nearly all mammals , including Peromyscus [8 , 31 , 46] . Thus , before conducting surveys of population-level variation at individual α-globin genes , it was first necessary to characterize the genomic structure of the α-globin gene family in order to design locus-specific PCR primers that would not coamplify paralogous gene duplicates or pseudogenes . The use of locus-specific primers is critical because it allows us to distinguish between allelic variation segregating at a single gene versus variation between duplicated genes . To isolate and characterize the α-globin genes of P . maniculatus we screened a bacteriophage-λ genomic library using the lambda FIX II/XhoI partial fill-in vector kit ( Stratagene , http://www . stratagene . com ) . We successfully cloned one of the two α-globin paralogs ( 3′ α-globin ) in a single fragment encompassing approximately 16 . 5 kb of Peromyscus Chromosome 13 , which is syntenic to Mus Chromosome 11 . We then used degenerate primers from the 5′ and 3′ flanking regions to amplify and sequence the other paralog ( 5′ α-globin ) . The design of locus-specific primers allowed us to amplify and sequence approximately 900-bp fragments that contain the entire coding sequence of each of the separate α-globin paralogs . In order to resolve the haplotype phase of each α-globin paralog ( i . e . , to associate nucleotides at multiple heterozygous sites ) , we adopted an experimental protocol that involved cloning both alleles of diploid PCR products in all individuals . After cloning diploid PCR products into pCR4-TOPO vector ( Invitrogen , http://www . invitrogen . com ) , we sequenced the complete coding region and intervening introns of at least 16 cloned alleles per mouse ( at least eight cloned alleles per paralog ) . This labor-intensive strategy offered two important advantages: ( 1 ) we recovered diploid genotypes for the complete coding region of both α-globin paralogs , and ( 2 ) we recovered the exact haplotype phase of all heterozygous sites . We also obtained a representative sequence of the β-globin gene of P . maniculatus in order to construct an accurate homology-based structural model of the hemoglobin tetramer . Primers for β-globin were designed using an alignment of orthologous sequences from human , Rattus , and Mus , in addition to published sequence from part of the β-globin coding region in Peromyscus [47] . We then used a genome-walking approach to characterize the 5′ and 3′ flanking sequence , and this enabled us to design primers that amplified the complete coding region . The globin genes were PCR-amplified using Ampli-taq Gold chemistry ( Applied Biosystems , http://www . appliedbiosystems . com ) under the following cycling parameters: 94 °C ( 120 s ) initial denaturing , [94 °C ( 30 s ) , 58 °C ( 30 s ) , 72 °C ( 60 s ) ] 30 times , and a final extension at 72 °C ( 120 s ) . Primer sequences are listed in Table S2 . The same PCR primers were also used for sequencing reactions , although annealing temperature was increased to 60 °C . M13 primers were used to amplify cloned products ( 55 °C annealing temperature ) and internal T7/T3 primers were used for sequencing . All sequencing reactions and samples were run on an ABI 3730 capillary sequencer using Dye Terminator chemistry ( Applied Biosystems ) . For each of the two α-globin gene duplicates , we used orthologous sequence from a related species , P . leucopus , to estimate divergence and to infer the ancestral and derived variants at each segregating site in P . maniculatus . For the purpose of comparison with the α-globin genes , we used a sample of 30 mice from Mt . Evans and Yuma County to survey DNA sequence variation at five additional unlinked nuclear loci: β-fibrinogen ( 614 bp ) , vimentin ( 785 bp ) , LCAT ( 456 bp ) , RAG1 ( 1 , 183 bp ) , and AP5 ( 385 bp ) . Each of the five loci were PCR-amplified as described above and were then sequenced directly on the ABI 3730 . Primers and sequenced gene regions for each marker locus are listed in Table S2 . After first obtaining direct sequence data for the complete sample of 30 mice , we cloned both alleles from greater than 80% of individuals that were heterozygous at multiple sites . We then used the program PHASE [48 , 49] to infer haplotype phase for the remaining individuals . We found that by experimentally verifying haplotype phase for large numbers of multiple heterozygotes , the PHASE program was able to resolve the remaining haplotypes with a high level of confidence . The original experiments that documented the association between physiological phenotype and α-globin genotype in P . maniculatus were based on gel electrophoresis and thin-layer IEF analysis of protein variation [8–13] . In order to relate the results of our DNA sequence analysis to this previously published work , we conducted an immobilized gradient , thin-layer IEF analysis to obtain electrophoretic protein genotypes for the same mice that we used in the analysis of DNA sequence variation . By matching deduced amino acid sequences of each allele to the observed IEF genotypes , we identified the specific amino acid changes that underlie differences in isoelectric point between the protein alleles of 5′ α-globin ( the Hba0 and Hba1 alleles of Snyder et al . [21] ) . Hemolysates and globin samples were prepared following Ferrand [50 , 51] , and thin-layer IEF was carried out in polyacrylamide gels ( pH 7 . 1 to 8 . 1 ) . The primary structures of the α- and β-globin polypeptides of P . maniculatus were deduced from translated DNA sequences ( Figure 1 ) . To construct a homology-based model of deer mouse hemoglobin , we used the P . maniculatus amino acid sequences to search structural templates on the 3D-JIGSAW [52] and SWISS-MODEL [53] Web servers . The resulting homology models were evaluated using the Procheck protein crystallography program from the CCP4 suite [54] . All structural mining analyses , including superimpositions and calculation of interatomic distances , were performed using Swiss-Pdb Viewer [55] . Calculations of local binding energy minimization were conducted using AutoDock [56] . All graphical representations of molecular structures were prepared using PyMOL ( DeLano Scientific , http://www . delanoscientific . com ) . DNA sequences were aligned and contigs were assembled using the Sequencher program ( Gene Codes , http://www . genecodes . com ) . All sequences were verified by visual inspection of chromatograms . To detect evidence of gene conversion ( nonreciprocal recombination ) between the two α-globin paralogs , we used a maximum likelihood algorithm to estimate ψ , the per-site probability of detecting a gene conversion event between the two paralogous sequences [57] . To detect evidence of recombination within each of the two α-globin paralogs , we used the four-gamete test of Hudson and Kaplan [58] to estimate RM , the minimum number of recombination events in the history of the sample , and we used coalescent simulations to obtain 95% confidence intervals for the estimates . For each pairwise combination of nonsynonymous nucleotide polymorphisms , we tested the null hypothesis that genotypes at one site were independent of genotypes at a second site by performing exact tests on contingency tables of diploid genotypes . These tests were performed for site-by-site comparisons between the two α-globin paralogs . To obtain a summary measure of two-locus linkage disequilibrium between the two α-globin paralogs , we used the index of association , IA [59 , 60] , and tested for significant associations using the randomization test of Agapow and Burt [61] . In the randomization tests , each of the two α-globin paralogs were treated as separate linkage groups and permutations of diploid genotypes were restricted to each group . Thus , excess linkage disequilibrium in the observed data relative to the randomized data can be attributed to nonrandom associations between the two paralogs . Likewise , permutations of genotypes within each linkage group were restricted to each of the separate population samples . Thus , excess linkage disequilibrium in the actual data relative to the randomized data cannot be solely attributed to genetic differentiation between the high- and low-altitude samples . Finally , the identified gene conversion tracts were treated as missing data and were fixed in place for the randomization tests , thereby ensuring that the actual distribution of missing data was preserved in the randomized datasets . Summary statistics of nucleotide polymorphism and divergence were computed with the programs SITES [62] and DnaSP v4 . 0 [63] . To assess whether the observed levels of intralocus linkage disequilibrium deviated from neutral expectations , we conducted neutrality tests based on Kelly's Zns statistic [34] . To obtain critical values for this test statistic , we generated a null distribution of Zns values by using coalescent simulations to generate 10 , 000 neutral genealogies that were conditioned on estimates of ρ ( = 4Nc , where N is the effective population size and c is the rate of crossing over between adjacent sites ) [64] . To measure levels of genetic differentiation between high- and low-altitude samples , we calculated Weir and Cockerham's [65] estimator of FST for each single nucleotide polymorphism , and we used the estimator of Hudson et al . [66] to compute an analogous measure of differentiation for complete sequence haplotypes . To determine whether amino acid polymorphisms in the α-globin genes exhibit unusually high levels of altitudinal differentiation that may be attributable to diversifying selection , we used coalescent simulations to generate null distributions of site-specific FST values under a neutral model of population structure . Specifically , we used observed levels of differentiation at silent-sites to parameterize a 100-deme island model of population structure in which the simulated migration rate between each pair of sampled demes was set equal to the value that produced the observed mean FST value . For each α-globin gene , we used coalescent simulations to generate 10 , 000 neutral genealogies that were conditioned on the actual sample size and estimates of θ ( = 4Nμ , where N is the effective population size and μ is the mutation rate ) [67] . Separate distributions of FST values were generated for a sliding window of 50 bp that was positioned at 10-bp increments along each of the two α-globin genes .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for all sequences discussed in this paper are EF369525–EF370032 .
|
A major goal in evolutionary biology is to identify the specific genetic mechanisms that have enabled organisms to adapt to their environments . Variation in deer mouse hemoglobin represents an especially promising system for examining the molecular underpinnings of adaptation because it has been possible to establish a mechanistic link between allelic variation in protein function and fitness-related variation in physiological performance . Specifically , adaptive variation in blood biochemistry and aerobic metabolism among mice from different elevations is associated with allelic variation at two closely linked gene duplicates that encode the α-chain subunits of adult hemoglobin . In this study , we report an analysis of DNA sequence variation in the two α-globin gene duplicates of deer mice in order to identify the specific mutations that underlie adaptation to high-altitude hypoxia . The study revealed that allelic differences in hemoglobin-oxygen affinity are attributable to the independent or joint effects of substitutions in five exterior amino acid residues that line the opening of the heme pocket . Additionally , patterns of DNA sequence variation indicate that functionally distinct α-globin alleles are maintained by natural selection that favors different genotypes in different elevational zones .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"evolutionary",
"biology",
"genetics",
"and",
"genomics",
"mammals"
] |
2007
|
The Molecular Basis of High-Altitude Adaptation in Deer Mice
|
It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli . However , it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory , where maintenance of a percept when the stimulus is absent is required . This challenges the universality of the basic canonical cortical circuit . Here , we show that by including an overlooked realistic small nonspecific background neural activity , the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena . The background activity induces a mutual-inhibition mechanism for short-term memory , which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary . We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion , which is a prototypical intermittent rivalry stimulus .
Perceptual rivalry is the subjective experience of alternations between competing percepts when an individual is presented with an ambiguous stimulus . It has been suggested that rivalry between neurons may be a ubiquitous property and competition between neural representations occurs throughout the brain [1] . Evidence for this includes: 1 ) neural correlates of visual rivalry found at multiple regions of visual processing ( e . g . , V1 , V4 , MT , IT ) [2–5] , 2 ) most sensory modalities exhibit rivalry suggesting similar biophysical processes [6] , 3 ) brain regions that do not receive specific sensory inputs , such as the inferior frontal and parietal lobe , exhibit activity that is correlated with perceptual switches [7] , and 4 ) independent rivalry can occur at different spatial locations , with different senses , and under different modalities [6 , 8–10] . Rivalry provides a unique window into cortical function because it directly accesses an internal computation elicited by but distinct from an external sensory input . Many experiments on rivalry utilize stimuli that are always in view although possibly moving . We refer to these situations as static rivalry . In intermittent rivalry , the stimulus presentation is periodically removed while the perception alternates with a longer period . Rivalry memory refers to the return of the last dominant percept after an extended time duration without stimulus . Computational and mathematical work has shown that a neurophysiologically-constrained cortical model whose primary features are mutual inhibition between pools of neurons and a fatigue process can reproduce the basic experimental properties of static rivalry , winner-take-all behavior , normalization , and decaying oscillations induced by distractors under different input conditions [11–14] . These findings support the universality of a simple neuronal model that can be used to explain a myriad of basic cortical behaviors and as such is a candidate canonical circuit for simple cognitive functions ( e . g . see [15–17] for discussion of such functions ) . A canonical circuit is universal in the sense that it does not imply a single function but can exhibit multiple operating regimes under a change in parameter values ( including just a change in the type of stimulus ) . The biophysical dynamics can be inferred from behavioral observations such as rivalry , decision-making , working memory , and contrast-sensitivity [1 , 6 , 13 , 18] making them potential clinical endophenotypes ( simple biomarkers associated with and possibly underlying a more complex trait of interest ) . As such , rivalry has been suggested as a diagnostic tool for probing cognitive dysfunction such as in autism , bipolar disorder , and major depressive disorder [19–21] . The canonical circuit and similar models have been validated against a set of nontrivial observations in static rivalry including Levelt’s propositions [11 , 22–26] . Although mutual inhibition acting as a positive feedback has been proposed as a memory mechanism in other contexts [27] , it has been argued [22 , 28] that a circuit with mutual inhibition and fatigue cannot reproduce intermittent rivalry nor rivalry memory because presumably there is no mutual inhibition when the stimulus is absent and the fatigue process would give the opposite result of what is observed , favoring the suppressed percept ( i . e . masking not priming ) . The question then is whether a single circuit model can account for all of the aforementioned behaviors ( including non-rivalry dynamics ) or whether independent or additional circuits are required to explain all the phenomena . Here , we show that the same cortical circuit model can also be applied to time-varying inputs in intermittent rivalry and rivalry memory . We explore various possible mechanisms and show that just the inclusion of a previously neglected phenomenon—namely low , nonspecific , background activity—is sufficient to provide a unified account of static rivalry , intermittent rivalry , rivalry memory and all previously accounted for phenomena . The background activity at arbitrarily low values can provide sufficient drive to the neurons such that the memory can be held by mutual inhibition alone and withstand the effects of fatigue . The memory persists over a wide range of parameter values , is robust to noise , and does not require the inclusion of another process . The memory is topological in the sense that the total number of memory states is invariant to a range of changes to the input and network properties . Importantly this memory preserves all of the previously known behaviors of the basic model since the background activity does not play a role under the static conditions . This is in contrast to recurrent excitation models of short-term memory where neural activity is bistable between an active and inactive state , which is generally antagonistic to rivalry [29] , may conflict with local balanced states [30] , and relies on fine-tuning [31] . The circuit model can reproduce two main features of rivalry memory and intermittent rivalry that we observed in the ambiguous quartet illusion: 1 ) a dynamic Levelt’s fourth proposition ( dynamic L4 ) , where the dominance duration increases with increasing stimuli presentation intervals and 2 ) habituation , where the initial percept durations ( epochs ) decrease upon repeated stimulus presentations until a steady state is reached after several epochs . Dynamic L4 has been shown in prior work with other intermittent paradigms [32] . Habituation has also been reported for rivalry; although , not necessarily intermittent rivalry [33–35] . We provide a theoretical explanation for these behaviors . The theory suggests that for the observed habituation to occur , local fatigue such as spike-frequency adaptation or recurrent synaptic depression is the dominant form of fatigue rather than synaptic depression between pools , and that the interval between pulses is shorter than the fatigue time constant .
We informed our model with experiments on the ambiguous quartet illusion , which is a prototypical example of intermittent rivalry and has the advantage of naturally incorporating a periodic stimulus ( see Fig 1 ) . The inputs are time dependent and the perceived motion only occurs when the frame presentation ( input ) is switched from one parity ( e . g . dots in upper-right and lower-left corners ( UR-LL ) ) to the other ( e . g . lower-right and upper-left corners ( LR-UL ) ) . The motion is ambiguous because the transition has two possible interpretations . For example , in the transition from UR-LL to LR-UL , the dot located at UR could be interpreted as “sliding” vertically down to LR , while the dot at LL simultaneously slides up to UL . The alternative interpretation is that the dot at UR slides horizontally leftwards to UL , while the other dot at LL slides horizontally rightward to LR . If one imagines a bar connecting the dots then the vertical interpretation corresponds to a bar rotating clockwise from 45° to –45° ( like a seesaw ) while the horizontal interpretation corresponds to the stick rotating counterclockwise from 45° to 135° . Hence , the perceived motion in the quartet illusion is characterized by two degrees of freedom—orientation and direction . Orientation refers to whether the dot motion is aligned along the vertical ( V ) or horizontal ( H ) axis and direction refers to whether the motion is clockwise ( + ) or counterclockwise ( - ) ( see Fig 2a ) . We presented the illusion to subjects with different frame presentation intervals ( Tframe ) . We examined dynamic L4 by testing if the perception dominance duration ( TD ) ( inverse of the percept alternation rate ) was correlated with Tframe . In our first experiment we tested two subjects across a wide range of Tframe inputs . The subjects showed a dramatic response to the stimulus parameter , which occurred at different ranges for each subject ( different sensitivities ) . In Fig 3a and 3b we plotted the TD distributions to give the qualitative picture . We also observed some evidence of a habituation effect ( Fig 3c ) , where the dominance durations decreased to a steady state value . To verify the phenomenon we recorded dominance durations on a group of 16 hypothesis-naive subjects . We probed the system over a range of Tframe: 300 , 350 , 400 , and 450 milliseconds ( minimum chosen to avoid photosensitive epilepsy ) . Individuals were told to report on any change of motion and were trained on biased H and V motion stimuli prior to the quartet stimulus ( see Methods ) . From our post-task survey we found that all individuals observed oscillating V motion ( seesaw ) and H motion ( seesaw rotated by 90 degrees ) . For a majority of subjects , these were the only reported percepts but a subset occasionally observed rotation ( H , V , H , … ) . There were also infrequent reports of the dots disappearing or changing size and the dots moving toward and away from the subject ( three-dimensional motion ) . The stimulus used in the experiments can be seen in S1 Movie . To test for the presence of dynamic L4 , we analyzed the pooled data across subjects and across percept-types ( H , V , rotation ) . We observed a weak effect across subjects . The qualitative results are shown by the TD distributions and their means ( see Fig 4a and 4b ) . We quantified the relationship with a Cox proportional hazards mixed effects model . The statistical model treats the dominance time as the survival time for a percept and evaluates the effects of factors on the survival probability ( see Fig 4c ) . There was a significant but weak positive relationship of TD with Tframe with P = 2 × 10−4 ( Wald test using 1 , 322 samples and 1 , 266 recorded switch events ) and hazard ratio of 0 . 996 ( s . e . 0 . 001 ) . We used these preliminary findings together with previously reported effects for another rivalrous stimulus [32] as support for the dynamic L4 constraint . For the hypothesis-naive experiment we discovered a larger habituation effect of the percept durations over percept epochs . The initial percept epochs had longer TD as shown by Fig 5 , and we estimated a hazard ratio of 1 . 024 ( s . e . = 0 . 008 ) ( P = 2 × 10−3; Wald test using 1 , 322 samples and 1 , 266 recorded switch events ) . We used this habituation effect as a second constraint for the model .
We examined several intermittent rivalry and rivalry memory mechanisms in a canonical cortical circuit model and concluded that the inclusion of experimentally observed low-level , nonspecific background neuronal activity that induces a mutual-inhibition memory is the most plausible . The mechanism preserves all the phenomena of static rivalry as shown in previous studies [11 , 12 , 22] . Mutual inhibition has been proposed before as a mechanism for short-term memory [27] . However , it was not considered for rivalry since it was assumed that the circuit could not maintain the memory in the absence of a stimulus and the presence of fatigue would favor the suppressed percept . Even if neurons are not completely inactive during the off-states , it is not a priori obvious that an asymmetric state would exist and persist indefinitely in the presence of fatigue . We show that for reasonable conditions , this form of memory is topological ( holds for a wide range of parameters ) and stable for arbitrarily low input . Topological mutual-inhibition memory can exist as long as inhibition is strong enough and the gain function ( i . e . , frequency-input curve ) has a non-increasing slope , which is experimentally observed [36 , 49] . Thus , it may not be limited to rivalry and could be the mechanism for other forms of memory in the brain . The model explains the two intermittent rivalry phenomena of dynamic L4 and habituation in the quartet illusion . Dynamic L4 is similar to what Leopold et al . ( 2002 ) found for the rotating sphere ( RS ) stimulus using manually inserted blank periods . A habituation effect similar to what we found for the quartet has also been noted for other stimuli ( e . g . , static images , motion ) and sensory domains ( e . g . , visual , auditory ) [33–35] . However , binocular rivalry shows the opposite effect where the switch rate decreases over epochs . This has been attributed to contrast adaptation [50] and can be accounted for in the rivalry model by reducing input over time . The dynamics of our model are geometrically similar to the model proposed by Noest et al . 2007[28] . However , instead of requiring a subthreshold current with shunting adaptation , we show that the activity itself acts as the memory with the property of persisting indefinitely . There are many possible mechanisms for the background activity and perhaps the most interesting is that zero-mean noise is sufficient . While either local ( e . g . , spike-frequency adaptation , recurrent synaptic depression ) or nonlocal ( e . g . , cross-pool synaptic depression ) is sufficient to explain intermittent rivalry , local fatigue in particular reproduces habituation . Prior experimental designs of intermittent rivalry introduced the off-states by either manually removing a static stimulus or by utilizing a more complex protocol such as combining motion-induced blindness with a static stimulus , or by instructing the subject to independently attend to mixed “static , intermittent” epochs[10 , 32] . It is possible that some of these more complex paradigms involve higher order processing that makes replication difficult . We propose that the quartet is a robust self-contained system for the study of intermittent rivalry since periodicity in the quartet invokes natural motion processing . In retrospect , our experimental design was suboptimal for detecting dynamic L4 , but the model provides guidance for future experiments . Further study is necessary to determine whether different fatigue modes are important for different rivalry stimuli . This could be predicted with the model and tested experimentally by behavioral and electrophysiological measurements , perhaps combined with pharmacological manipulation of K+-channels that modulate adaptation effects [51] . Finally , the role of background activity in the theory predicts that perturbing or suppressing the activity bias will extinguish rivalry memory .
Ethics approval for this study was granted by the NIH Combined Neuroscience Institutional Review Board under protocol number 10-M-0027 ( ClinicalTrials . gov ID NCT01031407 ) . The naive-subject study consisted of 16 adult male participants from the National Institutes of Health campus and DC metropolitan region . The data were screened such that at least two percept switches were reported in a test block . One subject did not have adequate data and was removed , leaving 15 subjects . Author data was collected on SV and PT for the quartet . Naive-subjects were tested on the standard quartet task . The standard quartet animation consists of two alternating still frames where a single frame consists of one set of dots located at opposing corners of an upright square as shown in Fig 1 . The task was administered in a dark room with maximum screen and keyboard brightness ( approximately 2 lux ) on a 15 inch 2010 MacBook Pro with 60 Hz frame rate . Our stimulus consisted of three nested quartets centered on the viewing screen as shown in Fig 1 ( also see S1 Movie ) ; chosen to mitigate the Troxler effect and to induce motion perception over a large receptive field . The dots were outlined with the RGB color = 46 , 55 , 254 and set on a black background . For a typical viewing distance of 24 inches , although this was not rigidly enforced , the square length visual angle ( dot diameter visual angle ) of each nested quartet were 5 . 5 ( 1 ) , 3 . 5 ( 0 . 5 ) , 1 . 5 ( 0 . 25 ) degrees . There was a fixed orange , central dot with RGB color = 255 , 127 , 0 . This was the instructed focal point for the subject . These parameters were arbitrarily chosen . During the task , subjects were instructed to maintain fixation on this central dot and report a change in motion by pressing the Return-key . To give feedback that a switch was recorded , the central dot briefly changed color from orange to green ( RGB = 0 , 255 , 0 ) to orange when the subject reported a change . The task consisted of 7 blocks: a tutorial , a task-comprehension block , and 5 frame-period ( Tframe ) blocks with a two second black screen period between blocks . Participants were told that the task would test their ability to detect changes in motion and were not informed that the motion was ambiguous . They were also shown examples of the types of motions to expect: horizontal or vertical , consisting of unbiased apparent motion animations , where a set of three frames was repeatedly presented such that the dots progressed from corner , to center , to corner , to center . This was done for vertical and horizontal orientations of motion . The task-comprehension block used this same biased motion stimulus and the direction was changed at fixed intervals that were known to the experimenter . The first frame-period block was set to 300 millisecond frame intervals and lasted six minutes ( we refer to this as the practice block ) . This was followed by four blocks with frame periods ( and block durations ) of 300 ( 5 ) , 350 ( 5 ) , 400 ( 6 ) , and 450 ( 8 ) ms ( minutes ) . The order of the last four blocks was shuffled between participants . Author-data was collected on the quartet task . The quartet task was similar to the above but without the tutorial , task-comprehension , or practice blocks . These sessions consisted of randomized Tframe with 2–3 sampled frame periods and the duration was based on reporting 11 switches ( some Tframe were repeated in separate sessions ) . Data from the naive-subject group was analyzed using a Cox proportional hazards model ( see [52 , 53] for review ) . It is a semi-parametric model that is robust to the distribution of the data , which was non-normal for our case . The analysis calculates statistics across all time points and accounts for right-censored data . We chose this approach since it accounts for percept duration that may extend beyond the test block , including those that may span the majority of a block . The frequency of percepts that remained dominant after TD dotted frame presentations were fit to a baseline survival model for each test condition ( e . g . , Tframe ) and normalized ( the non-parametric component of the model ) . The hazard ratio is the exponential adjustment to the baseline survival for the conditioned effects , which is the parametric component . An increased hazard ratio means there was an increased probability of percept extinction over a time period as the parameter was increased . To be considered a valid model fit , there were two statistical tests: 1 ) test whether the estimated coefficient for the effect is non-zero , and 2 ) that the distribution of the residuals has zero slope across time ( is flat ) . We used the coxme mixed effects R function to estimate the hazard ratios . The mixed effects model was used so we could account for subject- or Tframe- specific deviations . A Wald test was used to estimate a P-value for the hazard ratio . The coxme library does not have a test for the residuals . To check this we calculated a noise vector ( z ) from the coxme fit given by z = ŷ − θx , where ŷ is the linear model prediction , θ is the estimated coefficient , and x is the predictor vector . The noise vector z was used as an offset in the R function coxph and the residuals from this were checked with cox . zph . We used a criterion of no significant deviation of the residual slope from zero ( i . e . , P>0 . 05 ) , in order for the proportionality assumption to apply . Bifurcation analysis was performed in XPPAUT [54] . Rate models were simulated using Python . Conductance-based model simulations were performed with circuit of two mutually inhibiting neuron pools with a calcium activated spike frequency adaptation-like current and synaptic depression . This model was simulated in XPPAUT and the output was analyzed in Python . All code used for simulations and numerical analysis including parameters are attached as S2 Text .
|
When the brain is presented with an ambiguous stimulus like the Necker cube or what is known as the quartet illusion , the perception will alternate or rival between the possible interpretations . There are neurons in the brain whose activity is correlated with the perception and not the stimulus . Hence , perceptual rivalry provides a unique probe of cortical function and could possibly serve as a diagnostic tool for cognitive disorders such as autism . A mathematical model based on the known biology of the brain has been developed to account for perceptual rivalry when the stimulus is static . The basic model also accounts for other neural responses to stimuli that do not elicit rivalry . However , these models cannot explain illusions where the stimulus is intermittently switched on and off and the same perception returns after an off period because there is no built-in mechanism to hold the memory . Here , we show that the inclusion of experimentally observed low-level background neural activity is sufficient to explain rivalry for static inputs , and rivalry for intermittent inputs . We validate the model with new experiments .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"material",
"fatigue",
"medicine",
"and",
"health",
"sciences",
"classical",
"mechanics",
"pathology",
"and",
"laboratory",
"medicine",
"fatigue",
"social",
"sciences",
"neuroscience",
"learning",
"and",
"memory",
"perception",
"cognitive",
"psychology",
"signs",
"and",
"symptoms",
"cognition",
"materials",
"science",
"memory",
"damage",
"mechanics",
"materials",
"physics",
"vision",
"inhibitions",
"animal",
"cells",
"behavior",
"physics",
"psychology",
"cellular",
"neuroscience",
"diagnostic",
"medicine",
"cell",
"biology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"sensory",
"perception",
"physical",
"sciences",
"cognitive",
"science"
] |
2016
|
Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory
|
Gap junctions between fine unmyelinated axons can electrically couple groups of brain neurons to synchronise firing and contribute to rhythmic activity . To explore the distribution and significance of electrical coupling , we modelled a well analysed , small population of brainstem neurons which drive swimming in young frog tadpoles . A passive network of 30 multicompartmental neurons with unmyelinated axons was used to infer that: axon-axon gap junctions close to the soma gave the best match to experimentally measured coupling coefficients; axon diameter had a strong influence on coupling; most neurons were coupled indirectly via the axons of other neurons . When active channels were added , gap junctions could make action potential propagation along the thin axons unreliable . Increased sodium and decreased potassium channel densities in the initial axon segment improved action potential propagation . Modelling suggested that the single spike firing to step current injection observed in whole-cell recordings is not a cellular property but a dynamic consequence of shunting resulting from electrical coupling . Without electrical coupling , firing of the population during depolarising current was unsynchronised; with coupling , the population showed synchronous recruitment and rhythmic firing . When activated instead by increasing levels of modelled sensory pathway input , the population without electrical coupling was recruited incrementally to unpatterned activity . However , when coupled , the population was recruited all-or-none at threshold into a rhythmic swimming pattern: the tadpole “decided” to swim . Modelling emphasises uncertainties about fine unmyelinated axon physiology but , when informed by biological data , makes general predictions about gap junctions: locations close to the soma; relatively small numbers; many indirect connections between neurons; cause of action potential propagation failure in fine axons; misleading alteration of intrinsic firing properties . Modelling also indicates that electrical coupling within a population can synchronize recruitment of neurons and their pacemaker firing during rhythmic activity .
Electrical synapses are widespread in nervous systems from motoneurons to the neocortex and have a long history of study [1–2] . In simpler systems , they have been proposed to reduce response latency , synchronise firing , and detect input coincidence [3–8] . Electrical coupling is found within populations of similar neurons in the mammal retina [9] and brain [1] and is sometimes mediated by axo-axonic connections [10–19] . The effects of electrical coupling may be diverse [20–21] , but one hypothesis is that it synchronizes activity over particular frequency ranges [22–25] . Electrical coupling is easy to demonstrate experimentally but its distribution and significance is difficult to define . Tracer dyes may reveal electrically coupled neurons and potential gap junction sites [26] but curiously , electrically coupled neurons often show no dye coupling [1] . Another difficulty is that the properties and responses of individual neurons cannot be isolated if they are electrically coupled . To investigate the effect of the gap junctions pharmacological blockers have been used but most are probably non-specific and have effects on other membrane channels [21 , 27–29] . Genetic knockouts are another possibility [30] but there are likely to be side-effects [31] . One approach to understanding is through modelling networks of electrically coupled neurons [32] . Our aim is to use network modelling to explore the significance of axo-axonic electrical coupling in populations of rhythmically active neurons . We have exploited the detailed evidence on frog tadpole reticulospinal descending interneurons ( dINs; [29 , 45] ) by building a model of part of the dIN population . The aim was to investigate the effects of different gap junction distributions , coupling and axon parameters , and experimental protocols . Specifically , we built a population of 30 model brainstem dIN neurons and asked: 1 ) can the possible locations of axonal gap junctions be inferred using passive models of their somata and axons; 2 ) what is the effect of axonal electrical coupling on the firing properties and axonal action potential propagation; 3 ) is their apparent single-spike firing a result of their electrical coupling; 4 ) what is the role of axonal electrical coupling in the rhythmic firing of the dIN population during swimming and their recruitment by sensory stimulation ? Our results have implications for understanding the nature of axon to axon electrical coupling in fine unmyelinated axons , its role in rhythmic networks , and some of the experimental difficulties in its experimental investigation .
The construction of the multicompartmental neuron model and networks is described in the Results . Each generated gap junction distribution scheme was evaluated by examining the distribution of coupling coefficients in a population of 30 passive neurons . The dIN axons were compartmentalised according to gap junction density , in order to maintain simulation speed ( compartment lengths: 5 μm in the hillock , 5 μm for the first 400 μm of the axons and 100 μm thereafter , electrotonic length in the axon is ~250 μm ) . In 50 simulations , hyperpolarising current was injected into a randomly chosen source neuron ( Nsrc ) and the steady state voltage deflections measured in all neurons . Coupling coefficients between Nsrc and all other neurons were then calculated . Membrane potential ( V ) evolves according to ( Eq 1 ) . Channel kinetics were based on voltage clamp data for the currents [35–37] . The sodium ( ina ) and potassium currents ( ikf , iks ) were modelled using a Hodgkin-Huxley type formulation [38]; ( Eq 2 ) and the calcium current ( ica ) was modelled using the Goldman-Hodgkin-Katz formulation [39–41] ( Eq 3: F = 96485 C/mol , R = 8 . 314J/ ( K mol ) , T = 300K ) . The ilk current is due to passive membrane leak conductance and iext is the current from a current injection protocol . Each voltage-gated channel is gated by one or more gating variables ( mna , hna , nkf , nks and mca ) . Eq 4 describes the dynamics of an arbitrary gating variable X , which is governed by the steady-state and characteristic time constant functions respectively ( Eqs 5 and 6 ) . These are dependent on functions which control the opening and closing of gates gof Eq 7 . The values for parameters A , B , C , D and E are given in Table 1 for each channel type . ( Note that the beta-rate equation for calcium takes one of two sets of parameters depending on the membrane potential [35 , 36] . Initial values for channel conductances g^X ( where X is the channel type ) were estimated based on input resistance and current densities measured in voltage clamp-recordings from Central Pattern Generator ( CPG ) neurons ( Table 2 ) . The small number of channel types meant that parameter sweeps could be used to investigate the effects of channel densities on the firing properties of the neurons and the robustness of the neurons to noise . For each channel type , a set of scaling factors ( MX ) was chosen ( Table 2 ) , resulting in a parameter space of 288 possible combinations . Since the experimental recordings of dINs are from in vivo recordings , in which the dINs are embedded in the electrically coupled network , the simulations were performed in the same scenario . For each parameter set in the parameter space to be investigated , a network of 30 electrically coupled dINs was created , in which each dIN had conductance densities for each channel given by gx=g^x×MX×N ( μ=1 . 0 , σ=0 . 05 ) . Normally distributed variability was introduced with a noise term ( N ) calculated separately for each model dIN , for each channel and for each simulation ( mean = 1 . 0 , standard-deviation = 0 . 05 ) . Note that the initial leak-conductance value was calculated based on measurements of input resistance of an electrically coupled dIN in vivo . When multi-compartment axons were modelled with gap junctions present , we examined the effects of non-uniform channel densities on the reliability of action potential propagation . To model the synaptic input which dINs receive following head skin stimulation , we used data from whole-cell recordings made in trigeminal interneurons ( tINs ) which are excited by head skin sensory neurons and in turn excite dINs [42] . In vivo these tINs fire between 0 and 5 spikes depending on the stimulus level , so a simple model was built that generated a set of spike times for a single tIN in response to graded skin stimuli . The stimulus strength , s , is normalised so that s = 100% corresponds to a head-skin stimulus at the threshold required to initiate swimming ( Experimentally , s is the normalised amplitude of current used for electrical head-skin stimulation ) . This model was used to drive EPSPs in the dINs to model excitation from a population of 20 tINs . In the tIN spike time model , the number of spikes fired , n , at given stimulus level , s , is generated from the probability distribution p ( N = n|S = s ) . This simple model was based on the observations that: a ) the mean threshold stimulus that leads tINs to fire a single spike is 95% ( 94 ± 6% ) , ( i . e . p ( N = 1|S = 95% ) = 0 . 5 ) ; b ) at 100% stimulus all tINs fire a single spike ( i . e . p ( N = 1|S = 100% ) = 1 . 0 ) ; c ) as stimulus strength increases above 100% , some tINs begin to fire multiply , but some always fire a single spike ( 10/34 ) ( p ( N = 1|S>120% ) = 0 . 3 ) ; and d ) the distributions for the number of spikes fired at high stimuli ( s>120% ) were estimated based on spike counts at higher stimulation levels [42] . Next , the number of spikes , n , fired by a model tIN is converted into timings , {t1 , t2 … tn} . The time of the kth spike , tk is generated from a normal distribution tk ~ N ( μ = μk , σ = σk ) , where μk and σk are the means and standard deviations of the k'th spike . The values of μk and σk were calculated from experimental data taken at all levels of stimulation [43] .
To investigate the effects and significance of electrical coupling in a relatively simpler network we have studied small columns of neurons in the brain of hatchling frog tadpoles . Two days after fertilization , tadpoles of Xenopus laevis are approximately 5 mm in length ( Fig 1A ) and respond to a brief touch stimulus by swimming for several seconds [44] . The locomotor circuits generating swimming rhythms consist of ~2000 neurons divided into <10 classes . These neurons form longitudinal columns on each side , starting in the hindbrain and descending into the spinal cord . Lesion studies have shown that a small region of the nervous system , 0 . 3 to 0 . 4 mm long in the hindbrain and rostral spinal cord ( Fig 1B ) , is sufficient to produce sustained swimming-like rhythms in response to stimulation [45] . One class of reticulospinal neuron , descending interneurons ( dINs ) , is present in this region with a population of about 30 on each side . These neurons play a central role in the generation of swimming rhythms and directly excite motoneurons [46] . Whole-cell recordings showed that the dINs are the first neurons to fire on each side on each cycle of swimming , and concluded that they are the neurons which drive swimming [47] . Recordings from pairs of these dINs up to 200 μm apart in the longitudinal column on one side have shown that they are electrically coupled ( Fig 1C and 1D; [29] ) . In some cases dIN dendrites extend far enough longitudinally to allow short range coupling ( Fig 1C neuron 2 ) but more usually their dendrites have very limited longitudinal extents ( < 30 μm; Fig 1C neuron 1 ) . This means that coupling between dINs from 50 to 200 μm apart is likely to be via contacts between their very thin ( <0 . 5μm ) , unmyelinated , descending longitudinal axons ( Fig 1C ) . After emerging from the soma these axons lie close to each other for a while before they diverge ( Fig 1E ) and could therefore make gap junctional connections . When gap junction blockers were applied , swimming episodes became shorter and dIN firing became unreliable , suggesting that the electrical coupling is important for reliable rhythm generation . Curiously , whole-cell recordings from dINs show they only fire once in response to step-current injection . This property may be important for network mechanisms of rhythm generation based on Post Inhibitory Rebound ( PIR; [48] ) . This single firing response seems at odds with the repetitive , 20 Hz pacemaker firing seen when the whole dIN population is excited by the experimental application of excitatory synaptic agonists like glutamate [49] . Glutamate activates NMDA-receptors ( NMDARs ) which play a fundamental role when dINs drive swimming [45] . The electrical coupling between reticulospinal dINs in the tadpole hindbrain is likely to be axo-axonic because only their axons are long enough to allow contacts at the longer distances between coupled somata [29] . It is possible that coupling could be axo-dendritic or axo-somatic or via ascending dIN axons when these are present but to simplify our analysis we have not considered these cases . The strength of coupling between two dINs , measured at their somata , will therefore be determined by the resistance of the gap junctions , their positions along the axons and the axon properties . The effects of neuronal morphology were taken into account by building multicompartmental models of dINs which included an axon . dINs have a soma with short dendrites , which together can be considered as a single isopotential compartment [50] , and a single thin axon ( <0 . 5μm ) which descends towards the tail for 280 to 2050 μm [51] . In some cases hindbrain dINs also have an ascending axon ( Fig 1C ) . The surface area of dIN soma plus dendrites was estimated as ~1000 μm2 from tracings of motoneurons with very similar size and morphology . This is a round number corresponding to a sphere of diameter ~17 . 5 μm and a small taper into the axon . ( It lies within the range of summed soma and dendrite surface areas measured for filled motoneurons: 754 to 1594 μm2; n = 5 ) A multicompartmental model was built with four sections to represent dIN morphology: one for the soma-dendrites , two for the hillock and one for the axon ( Fig 2A ) . The shortness of the dendrites means that they are equipotential with the soma and can be modelled as a single compartment ( see: [50] ) . The axon compartment was further compartmentalised during simulations ( see below ) . These smaller compartments are electrically connected together via resistances , which are calculated based on the intracellular resistivity , Ri , of the neuron , and the surface area that connects the two compartments ( mathematics described further in [34] ) . Initially , the model dINs were given a uniform leak conductance ( glk ) over their surface , to match the dIN input resistance measured experimentally , an intracellular resistivity Ri = 80 Ω cm and a specific capacitance C = 1 . 0 μF/cm2 [41] . The somata of the dINs driving swimming form a longitudinal column . The axons of dINs grow into the marginal zone of axons surrounding the spinal cord then grow towards the tail ( Fig 1C and 1E; and Fig 2B ) , so they could contact dendrites or axons of other dINs . Based on experimental measurements of cell distributions and reconstructions from fills with neurobiotin [45 , 51] , we created a column of 30 model dINs in the hindbrain and rostral spinal cord , with somata spaced at 10 μm intervals . In the tadpole , current injections during paired whole-cell patch-clamp recordings have shown electrical coupling between dINs with somata up to nearly 200 μm apart [29] . The coupling coefficient is defined as the change in potential of a coupled neuron expressed as a percentage of the change in potential of a neuron into which hyperpolarising current is injected . The coupling coefficient was found to decrease with distance between the two neurons , from ~10–15% ( at 0–50 μm ) to ~5% ( at 150–200 μm; Figs 3 and 4D ) . In paired recordings from dINs in normal saline , we injected depolarising current to evoke an action potential . In no case did such action potentials lead to firing in unstimulated dINs or to motoneuron excitation [45] . The location of the gap junctions responsible for coupling was not established experimentally but detailed dIN anatomy is available from single neurons fills [51] . If the dINs are very close to each other ( say <50 μm ) , gap junctions could be dendro-dendritic . If they are further apart the main possibility is axo-dendritic or axo-axonic gap junctions . Since the dendrites are electrotonically compact , both dendro-dendritic and axo-dendritic coupling can be seen as a special case in which the distance along the axon from the soma-dendrite compartment was 0 μm . To simplify our study and avoid further assumptions , we only considered axo-axonic coupling between dINs with descending axons . An advantage of using the dINs in the Xenopus tadpole to study axo-axonic coupling is that the layout is constrained to a single longitudinal dimension; the location of a gap junction coupling two dINs can be expressed simply as the distance of the gap junction from the more caudal dIN in the column ( dgj in Fig 2B ) . We investigated how the numbers , strengths and locations of gap junctions with simple values of the gap junction resistance ( RGJ ) would affect the electrical coupling measured between the dINs within the model column ( Fig 2B ) . Anatomical studies indicate that dIN axons intermingle in the marginal zone for a few hundred microns after emerging from the soma . Where axons overlapped , model gap junctions therefore had the same probability of forming ( per unit length ) between any two axons . The layout of the network leads to more gap junctions forming caudally , because the axons descend . For a bundle of parallel , cylindrical axons of equal diameter , one axon can be in contact with a maximum of six others at any point . Therefore at each point of each axon , we limited the possible connectivity to a maximum of six neighbouring axons but as new axons add to the column different axons will be able to make contacts with each other . The simplest layout scheme of gap junctions between pairs of overlapping axons which is consistent with the anatomy is for the axon of a more rostral dIN to form junctions with the axon of a more caudal neuron at a fixed position from the caudal soma with a certain probability ( Fig 2C ) . However , this type of layout led to local clustering of gap junctions with consequent large shunting which would cause axonal action potential propagation failure ( see below ) . We therefore used the next most simple layout with a fixed gap junction probability density over a defined region of the axon of the more caudal dIN ( Fig 2D ) . Gap junction distribution schemes were evaluated by examining the distribution of coupling coefficients in a population of 30 passive neurons . The dIN axons were compartmentalised according to gap junction density in order to maintain simulation speed ( compartment lengths: 5 μm in the hillock , 5 μm for the first 400 μm of the axons and 100 μm thereafter; electrotonic length in the axon is ~250 μm ) . In 50 simulations , hyperpolarizing current was injected into a randomly chosen source neuron ( Nsrc ) and the steady state voltage deflections measured in all neurons . Coupling coefficients between Nsrc and all other neurons were calculated and plotted against the distance between somata ( Figs 3 and 4D ) . The small number of parameters in the system meant that each layout strategy could be tested using a parameter sweep through different values of glk ( 0 . 1 to 0 . 5 mS/cm2; [48] ) , Ri ( 40 to 150 Ωcm; [41] ) , axon diameter ( 0 . 1 to 0 . 6 μm; [52] ) , min-dist ( 0 to 100 μm ) , max-dist ( 0 to 100 μm ) , and RGJ , ( 50 to 2000 MΩ; [23] ) . Model networks were evaluated by visually comparing the distribution of their coupling coefficients as a function of distance with those found experimentally [29] . Using the fixed probability density layout for gap junctions ( Fig 2D ) , we explored the parameter space influencing the distribution of coupling coefficients to find areas where there is a good fit to the experimental results ( Fig 3 ) . Some areas produced distributions that were clearly inappropriate . For example , raising the gap junctional resistance weakened coupling at all distances ( Fig 3A ) . When the diameter of the axons was 0 . 3 μm or less , the fit of the model to experiments was poor and large areas of parameter space produced low coupling coefficients between distant neurons compared to experimental observations ( Fig 3B ) . To get good coupling over distance , gap junctions needed to be less than 50 μm from the soma of the more caudal neuron ( Fig 3C ) . Interestingly , when the coupling was too close ( a substantial proportion of the gap junctions within 10 μm of the caudal soma ) , neurons closer together showed stronger coupling but the strengths of coupling over long distances were also reduced . Some care must be taken interpreting these results . In general , changing parameters in one way , ( for example moving gap junctions proximally , or decreasing their resistance ) leads to stronger coupling in the network , while others , ( such as reducing the numbers of gap junctions ) weakens coupling—often changing one parameter can be offset by changing another . Further exploration of gap junction distribution in coupled networks was based on results from our parameter sweeps . These showed that a gap junction layout scheme with a fixed probability density over a defined region , ( Fig 2D ) gave coupling coefficients similar to those observed experimentally , and was biologically plausible . Gap junctions were therefore generated between overlapping axons with a fixed probability density of 0 . 015 μm−1 over a region of the caudal axon starting at the minimal distance ( min-dist ) and finishing at the maximal distance ( max-dist; Fig 2D ) . To implement this the first 50 μm region of each axon was divided into 1 μm bins and for each bin , six other dINs with axons at the same position along the rostrocaudal axis were picked at random and given a fixed 0 . 015 probability of forming a gap junction . A gap junction resistance of 600 MΩ gave coupling coefficients similar to those measured experimentally . This method generated 80–120 gap junctions in the network of 30 dINs , which means each axon has about 5 to 8 gap-junction connections ( Fig 4 ) . The small number of connections on each axon resulted in only 25% of neuron pairs being directly coupled ( Fig 4B; blue dots ) . Most pairs were indirectly coupled via the axon of one other neuron ( Fig 4B; yellow dots ) . We found that when a dIN was electrically coupled in the network , its input resistance , calculated by injecting hyperpolarising step current , dropped by ~50% . To restore the model dIN input resistance to match values measured physiologically in dINs coupled to their neighbours [29] , the leak conductance ( glk ) of the neuron was decreased by 50% to 0 . 125 mS/cm2 . To build active models of the network of dINs we needed to match their unusual properties . The dINs play a critical role in driving the firing of other neuron types on the same side of the CNS during swimming as they are the first neurons to fire on each side on each cycle [47] . Whole-cell recordings have shown that dINs have an unusual response to a step current injection and only fire a single action potential , even at levels of injected current up to twice threshold [45] . Furthermore , it has been proposed that dIN action potentials during swimming are partly the result of PIR following inhibition from the opposite side of the spinal cord [45 , 53] . Rebound firing is not seen when dINs are hyperpolarized from rest but only during swimming when they are depolarised [44] . In line with this , while dINs are depolarised by injected current , short hyperpolarizations or Inhibitory Postsynaptic Potentials ( IPSPs ) can lead to rebound firing [47] . The densities and kinetics of the voltage-gated channels play an important role in determining the firing properties of neurons [54] . In Xenopus tadpole spinal neurons , voltage clamp experiments on dissociated neurons [36] and neurons in situ [55] have suggested the presence of about 8 types of voltage-gated ion channels . In dINs , the majority of the voltage-gated currents are thought to be carried through sodium channels ( Na ) , fast and slow potassium channels ( Kf , Ks ) and high-voltage-activated calcium channels ( Ca ) . The kinetics of these channels are described in the Methods section . The responses of model dINs and their axons to current injection were evaluated in the electrically coupled population using the layout scheme and parameters found above . For each channel type ( X ) we used an estimate for its conductance density ( g^x ) and created a set of possible open-channel conductances: gx=g^x×MX , where values of MX were a chosen set of multipliers for each channel type ( e . g . Mlk = ( 0 . 5 , 1 . 0 , 1 . 5 ) ; further details are given in the Methods section ) . We used a parameter sweep ( details in the Methods section ) over these channel densities and evaluated three responses at every point in the parameter space: a ) whether the model neuron reliably fired only a single action potential in response to step current injections of 50 , 100 , 200 and 300 pA; b ) whether an action potential initiated in the soma would propagate along the axon; and c ) whether the model neuron would ‘fire-on-rebound’ to short , hyperpolarizing current injections given during step depolarizing current injections . These are all typical and identifying features of dINs [45] . Since variability is introduced into model parameters ( see below ) , 10 neurons were evaluated at each point . In the first parameter sweep , channels were applied uniformly to the neurons and axons . In general increasing Na and Ca channel density and reducing K channels increased the excitability of the neurons ( Fig 5 ) . With fewer Na and Ca channels but more Kf and Ks channels dINs robustly fired only a single action potential ( Fig 5B ) but there was a problem . Action potential propagation along the axon ( Fig 5C ) only occurred in < 10% of cases . With more Na and Ca channels but fewer Kf and Ks channels , dINs fired once at threshold ( rheobase ) but fired repetitively as current was increased ( Fig 5D ) . Action potential propagation occurred ( Fig 5E ) but only in 20% to 40% of tests . At high densities of Na and Ca channels , rhythmic firing could continue after the current injection or in some cases even without any stimulation . For most sets of parameters , some form of rebound firing occurred when sufficient hyperpolarising current was injected while the dIN was depolarised ( Fig 5F and 5G ) . Can changes in gap junction resistance or the distribution of voltage-gated channels in the axon make action potential propagation more reliable ? When gap junction resistance was increased from 600 MΩ to 2000 MΩ , action potentials were always able to propagate , suggesting that the failure to propagate was due to the shunting of current through the gap junctions . In other fine , unmyelinated axons , densities of sodium channels up to 50 times those found elsewhere have been observed in the initial segment of the axon close to the soma [56–59] . To increase the proportion of action potentials propagating over regions with 600 MΩ gap junctions , without increasing the overall excitability of the model dINs , we carried out a parameter sweep in which channels were not distributed evenly over the neuron . Initially , we changed the density of Na and K channels only in the axon . Although increasing Na channel density by 200% and reducing K by 50% improved action potential propagation to ~40% , it also produced networks that were unstable , and fired repetitively without any input . The next step was to adjust channel densities only in the initial region of the axon between 20 and 70 μm from the soma . Increasing Na channel density in this region by 5 to 10 times made action potential propagation more reliable without the network becoming unstable . Increasing the Na channel density by a factor of 5 improved action potential propagation rates to over 50% . In vivo , dINs are usually active together so the current leak to other dINs will be less than when a single dIN is active alone and propagation should be reliable ( see below ) . However , recordings from pairs of neurons in vivo , showed that stimulating a single dIN to fire reliably produced an excitatory post-synaptic potential in the other neuron which implies that dIN axonal action potential propagation was reliable [45] . There is therefore a mismatch with the model . The parameters for the final dIN model were selected so its properties were as close as possible to those observed in whole-cell recordings of real dINs ( Fig 6; cf [45 , 47 , 48] ) : input resistance 300 MΩ; firing threshold 80 pA; action potentials with broadly similar rise times and durations; single firing in response to large step current injections ( > 300 pA; Fig 6A ) ; and firing on rebound in response to short hyperpolarising current injections during a long depolarising step current injection ( Fig 6B ) . The rise time and duration of the model action potential to current injection were shorter than those seen in recordings . The rise-time could be increased by the addition of an A-type potassium channel [54] but since its introduction had little effect on network dynamics , it was not included . Action potentials propagated more slowly over regions of gap junctions ( ~0 . 16 m/s ) . When gap junctions were removed the conduction velocity was ~0 . 32 m/s . These values are similar to conduction velocity estimates of 0 . 12± 0 . 27 m/s for the central axons of excitatory spinal neurons in Xenopus which may be dINs [60] . We would expect that properties determined using whole-cell recording electrodes are influenced by neurons being electrically coupled to other neurons . We therefore investigated whether the limited excitability and single spike firing of tadpole dINs was a result of the current sink effects of electrical coupling . We compared the response of a model dIN to step current injections in an electrically coupled network , and in isolation ( Fig 7 ) . When electrically coupled within the network , dINs fired only once to all levels of injected current ( Fig 7A ) . Removing the coupling between dINs had two effects . The input resistance of individual dINs recorded at the soma increased ( from ~300MΩ to ~600MΩ ) and the same step current injections now caused them to fire repetitively ( Fig 7B ) . The simplest interpretation of this effect is that dIN excitability is normally depressed by current flow through gap junctions from the stimulated neuron to its neighbours which sit at a more negative membrane potential , closer to rest . These results have significant implications for the interpretation of recordings from electrically coupled neurons and for conclusions based on the use of pharmacological agents to try to block electrical coupling ( see #sec_discussion ) . Our modelling led to the hypothesis that the single spiking response seen physiologically to step current injection into a single recorded dIN is caused by shunting of current into nearby electrically coupled dINs whose membrane voltages remain close to rest . To test this hypothesis , we injected a 200 ms step current pulse simultaneously into all 30 dINs in the electrically coupled network . At threshold , the dINs did not fire singly but the whole dIN population fired nearly synchronously and repetitively at ~30 Hz during the current injection ( Fig 8A ) . As the current was increased , the firing frequency increased to ~80 Hz , before repetitive firing failed at higher current levels ( Fig 8E ) . This peak frequency is considerably higher than the mean firing frequencies seen when dINs in vivo are perfused with glutamate or NMDA ( ~17 Hz; [49] ) . The effect that the gap junctions played in synchronizing network firing was investigated by reducing the number of gap junctions in the network to 0 , 25 , 50 or 75% . As the level of coupling was reduced , firing became less synchronised revealing the variability in the dIN population ( Fig 8C and 8D ) . When the whole model network fired synchronously , action potentials in each dIN propagated reliably for 1500 μm along their thin axons ( Fig 8F ) . The evidence from whole-cell recordings suggests that tadpole reticulospinal dINs have to be recruited as a population before swimming can start [42 , 47] . Does their electrical coupling affect the way in which this dIN recruitment happens ? To investigate this question we modelled the pathway for excitation of dINs following head skin stimulation [42] . The trigeminal sensory neurons innervating the head skin on one side directly excite a small population of approximately 20 trigeminal interneurons ( tINs ) which , in turn , directly excite dINs at glutamatergic synapses . As the stimulus to the skin increases , more tINs are recruited , they fire more action potentials and their EPSPs sum in the dIN population . We have defined this pattern of summation of tIN EPSPs in dINs as a function of skin stimulus strength ( see Methods ) . This means that we can now use it to study how the 30 dINs in our simple model population are recruited as stimulus strength to the head skin increases ( Fig 9 ) . Without electrical coupling a few dINs are recruited at low stimulus strengths and the number gradually increases with the stimulus but only slowly approaches 100% firing at higher stimulus strengths ( Fig 9B ) . In contrast , when the dINs are electrically coupled , a higher stimulus strength is required to recruit the first dIN but , above this level , the whole population is recruited together ( Fig 9C ) . Electrical coupling therefore makes the dIN population act like a switch which is either off ( at rest ) or fully on ( during swimming ) .
To date , electrical coupling has been described between diverse neurons types in the mammal brain but the evidence is not extensive and sometimes contentious ( see: [1 , 2 , 21] ) . This coupling is often fairly local , within distances of ~200 μm , but determining how many gap junctions are made by individual neurons and how their geometry is organised has proven difficult especially where networks are 3-dimensional . We have shown here how modelling can help . Fortunately , the tadpole dIN neurons which we have studied form a relatively simple longitudinal column in the hindbrain . Since their reliable electrical coupling acts over distances up to 200 μm and they usually have very short dendrites , it is most probably via gap junctions between their descending longitudinal axons [29] . Only detailed anatomical study with immune-labelling will resolve the true location of the junctions . Our modelling suggests firstly , that gap junctions need to be within 30 μm of the soma to get a realistic distribution of coupling coefficients as a function of distance between neurons that matches measured values , and secondly , that about 80% of coupling is indirect ( Fig 4B ) . Paradoxically , adding gap junctions did not necessarily increase coupling , particularly if a more proximal gap junction already existed between a pair of axons . Realistic coupling was achieved using around 100 gap junctions in the network of 30 dIN neurons . This implies , firstly , that many neuron pairs are coupled indirectly via the axon of a third . Secondly , each axon makes rather few gap junctions ( 5 to 8 gap junctions per axon ) . These numbers are similar to rat cerebellar basket cells where Alcami and Marty [64] used capacitative current measurements and paired recordings to estimate that each neuron was directly coupled to ~4 neighbours . Neurons with very fine , unmyelinated axons ( < 0 . 5 μm in diameter ) are widespread in the adult peripheral ( pain pathways ) and central nervous systems ( e . g . hippocampal mossy fibres , parallel fibres of granule cells , olfactory receptor axons ) but are also found in developing nervous systems like that of the tadpole . The function of such fine axons is not well understood because it is so difficult to obtain recordings from them [57 , 65 , 66] . The minimum diameter required to house their ‘molecular machinery’ is quite small but axons narrower than 0 . 1 μm may be prone to spontaneous initiation or failure of action potentials due to the stochastic nature of ion channel opening and closing [67 , 68] . In our modelling of the electrically coupled network of hindbrain dINs , axon diameter was found to be critical to achieving realistic coupling . Passive coupling between dINs could not be achieved with an axon diameter of less than 0 . 3 μm regardless of the gap junction layout and parameters . Measurements are not available for dINs but transmission electron microscope sections show that inhibitory neurons in the Xenopus tadpole have axon diameters of 0 . 2 to 0 . 9 μm with a mean of 0 . 4 μm [52] . The modelling therefore suggests there is a limit on minimum axon diameter if neurons are to be coupled electrically via their axons . However , we must remember that our model is not based on good data on the properties of fine unmyelinated axons which is just not available . Conclusions therefore have to be viewed with caution . The effects of the densities and spatial distributions of membrane channels has been investigated in axons from many systems [57 , 69–72] . Modelling the population of tadpole dINs as a passive network suggested that most of them are coupled indirectly via the axons of other dINs . When the neurons were given active properties we found failures of action potential propagation over regions of axons with gap junctions; these failures were reduced if gap junction resistance or membrane excitability was increased . However , such failures are not compatible with physiological recordings which show that action potentials initiated in one dIN propagate reliably to cause synaptic potentials in more caudal neurons [45] . Simply increasing the density of sodium channels uniformly in the whole neuron or over the entire axon produced networks that were unstable and could fire repetitively even without stimulation . We have shown here how increasing the density of sodium channels and decreasing the density of potassium channels in the initial segment of the axon close to the soma radically increased the reliability of action potential propagation . Higher densities of sodium channels have been observed experimentally in the axon hillock and initial axon segment in other neuron types [57 , 69–72] . Action potential propagation failure near gap junctions may be reduced if there is localised clustering of sodium channels close to the gap junctions . Such clustering has been studied in models of invertebrate unmyelinated axons [73] and has been seen at the nodes of Ranvier in mammal myelinated axons [74] . Another possibility is the existence of two types of sodium channel: those near the gap junctions having a higher voltage activation threshold which allows them to conduct action potentials reliably , without initiating them spontaneously . Finally , in other systems it is known that long duration action potentials may improve the reliability of action potential propagation in unmyelinated axons [75] . The duration of action potential in dINs is long compared to other neuron types in the tadpole [48] . Unfortunately , direct evidence on the membrane channels and currents underlying this long duration is not yet available [35 , 37 , 76] and a comparison of dIN recordings with our model dIN ( Fig 6B ) shows that model action potentials are shorter . Efforts were made to obtain a better match in our model dIN . Adding A-type potassium channels is already mentioned in the text . Increasing Ca channel density also lengthened the AP but led to depolarisation block when the dIN was depolarised . To avoid increased complexity which was not evidence based we accepted an action potential which is too short . Problems of action potential propagation may not be so significant in life if coupled neurons tend to receive similar input and fire synchronously , as is the case with tadpole dINs . This would reduce the voltage drop across the gap junctions and should make action potential propagation more reliable . An unexpected outcome of modelling the tadpole dIN population was the demonstration that electrical coupling can have a misleading effect on neuron firing properties determined experimentally using whole-cell recordings . In vivo , in an unexcited network , step current injection into a single dIN always leads to a single action potential [45 , 48 , 77] . However , the modelling showed that electrical coupling can transform neurons that fire multiple action potentials in isolation so that they too only fire a single action potential at the onset of a step current injection ( Fig 7 ) . Our interpretation , based on results from the model , is that this restriction to a single spike when current is injected into a single dIN is caused by a combination of: 1 ) sodium channel inactivation and activation of a slow potassium channel; and 2 ) current flowing through the gap junctions which acts like a leak to counter depolarizing currents and prevent further firing . Both of these mechanisms are needed to prevent successive action potentials; individually they are not sufficient . We infer this because just prior to the first spike in a single depolarized dIN , currents will flow across gap junctions , but there is not yet any sodium inactivation or slow potassium channels activation and so the neuron is able to spike . On the other hand , when current is injected to depolarise all the dINs , current will not flow across gap junctions because the dINs will be roughly isopotential as they approach spike threshold , and the sodium inactivation and potassium currents are unable to prevent repetitive spiking ( Fig 8 ) . This would explain why individual dINs only ever fire a single spike in response to step current injection during whole-cell recordings . At present , it is impossible in experiments to inject current simultaneously into many dINs . However , it is possible to depolarise many dINs at the same time by perfusion of NMDA and this leads to repetitive firing [49] just as we have found with current injection into the whole model dIN population . These results illustrate how whole-cell recordings from electrically coupled neurons may give misleading evidence about their firing properties by significantly reducing their excitability [76] . Another experimental approach to the study of electrical coupling is the use of pharmacological blockers . In the model , removal of the coupling within a network increases individual dIN input resistances by ~50% . This is rather more than the 16 or 18% increases produced experimentally by gap junction blockers ( flufenamic acid and 18-β-glycyrrhetinic acid respectively ) despite their producing a "near-complete" block of electrical coupling [29] . This suggests that the action of the pharmacological blockers is not equivalent to the simple removal of gap junctions from the model , as would be the case for an ideal gap junction blocker . This reinforces the view that gap junction blockers are not specific and , as well as blocking electrical coupling , are likely to have more complex effects , such as on other membrane channels [21 , 28 , 78] . As in most rhythmic neuronal networks , two proposals have been made about the fundamental mechanisms producing rhythmic swimming-type activity in the tadpole . The first is a pacemaker mechanism [35 , 49 , 79] . It has been shown that when NMDA is applied to an isolated single side of the hindbrain , motor nerves and the dINs ( which drive the swimming rhythm , [45 , 47] ) fire rhythmically at frequencies similar to those seen during swimming , and that this does not depend on inhibition [49 , 80] . The evidence suggests that the excitatory dINs driving swimming have membrane properties which limit their repetitive firing , when they are excited , to within the normal swimming frequency range . The second proposal is a network mechanism [48 , 81 , 82] where reciprocal inhibitory synaptic connections between the two sides of the CNS play an important role in rhythm generation by producing PIR firing in dINs , when these are held depolarised by their own mutual NMDAR-mediated excitation . The most recent evidence suggests that the two mechanisms play complementary roles in generating a reliable and coordinated swimming rhythm [83] and ensuring that one side does not generate rhythmic motor activity on its own . In both mechanisms the dIN electrical coupling ensures that activity within each side in synchronised . A long-standing paradox was that experimentally recorded dINs only fired a single spike to a depolarising current step ( but see [76] ) . They would therefore not be able to fire rhythmically when depolarised during swimming unless PIR allowed them to recover their excitability . However , our modelling shows that the restricted firing is a consequence of the electrical coupling , and that when the whole coupled dIN population is excited , the dINs are able to fire rhythmically as pacemakers . This suggests that PIR is not essential for rhythmic dIN firing during swimming , though it is still required to ensure antiphase coupling between the two sides . In an electrically coupled network , the current flow between neurons at rest has the effect of reducing the excitability of the individual neurons . This has another possible significance for the response of tadpole dINs to synaptic input . The whole dIN population on one side of the CNS is normally recruited to fire together , following sensory stimulation of the skin , to elicit swimming [42] . We have shown in our model that electrical coupling can ensure step-wise whole-population recruitment . It could also work like the simple “coincidence detection” mechanism described for electrically coupled amacrine cells in the retina [9] . The result of the coupling would be that neurons are less likely to fire on their own and more likely to fire together in response to simultaneous synaptic excitation . Our modelling of axo-axonic gap junction coupling in the linear population of tadpole reticulospinal neurons generates a number of predictions that we expect to be relevant to other networks of coupled neurons with fine , unmyelinated axons . It predicts that axonal gap junctions will be located close to the neuron soma of the more caudal neuron and occur in low numbers ( <8 hemichannels per neuron ) . Electrical coupling between neurons is therefore often indirect via the axons of other neurons . It may be possible to use tracers other than neurobiotin to locate gap junction contact points using dye filling of individual dINs to confirm that they are axo-axonic . It might also demonstrate indirect connections via other dINs . Neurobiotin injected with whole-cell electrodes does not appear to show dye coupling in the tadpole [29] although it has been used in other animals [84] . Our model showed that shunting through gap junctions can cause action potential propagation failure unless axonal membrane excitability is increased by changing the densities of sodium and potassium channels . In very small axons <0 . 3 μm in diameter propagation failure could not even be rescued in this way . The main , conventional role for electrical coupling in the tadpole reticulospinal population is to synchronize pacemaker firing during rhythmic activity and in this way coordinate motorneuron firing underlying swimming . However , electrical coupling also has dramatic effects on neuron firing properties measured experimentally using whole-cell recordings . Shunting can transform individual neurons able to fire repetitively into ones which only fire once to depolarising current when coupled . We plan to make voltage-clamp recordings to investigate the currents underlying the resting and active properties of dINs ( cf . [37] ) . During such experiments it may be possible to physically isolate individual dINs by pulling them away with the whole-cell electrode and then testing if they fire repetitively to current injection when uncoupled from their neighbours . Our modelling emphasises how little is known about the physiology of fine unmyelinated axons in any part of any nervous system .
|
Some groups of nerve cells in the brain are connected to each other electrically where their processes make contact and form specialized “gap” junctions . The simplest function of electrical connections is to make activity propagate faster by avoiding the delays resulting from chemical messengers at synaptic connections . In other cases , especially in higher brain regions where more spread out nerve cells may be connected by their axons , the function of electrical coupling is less clear . To understand this type of electrical connection better we have built computer models of a group of electrically coupled nerve cells in the brain which control swimming in very young frog tadpoles . We show that the coupling can be indirect , via other members of the group , and can profoundly influence the properties of the nerve cells which would be recorded during real experiments . The main role of the coupling is to synchronise the firing of the group so they are all recruited together when the tadpole is stimulated and then fire in a rhythm suitable to drive swimming movements . The results from this simple animal raise issues which will help to understand coupling in more complex brains .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Modelling the Effects of Electrical Coupling between Unmyelinated Axons of Brainstem Neurons Controlling Rhythmic Activity
|
The flagellar pocket ( FP ) of the pathogen Trypanosoma brucei is an important single copy structure that is formed by the invagination of the pellicular membrane . It is the unique site of endo- and exocytosis and is required for parasite pathogenicity . The FP consists of distinct structural sub-domains with the least explored being the annulus/horseshoe shaped flagellar pocket collar ( FPC ) . To date the only known component of the FPC is the protein BILBO1 , a cytoskeleton protein that has a N-terminus that contains an ubiquitin-like fold , two EF-hand domains , plus a large C-terminal coiled-coil domain . BILBO1 has been shown to bind calcium , but in this work we demonstrate that mutating either or both calcium-binding domains prevents calcium binding . The expression of deletion or mutated forms of BILBO1 in trypanosomes and mammalian cells demonstrate that the coiled-coil domain is necessary and sufficient for the formation of BILBO1 polymers . This is supported by Yeast two-hybrid analysis . Expression of full-length BILBO1 in mammalian cells induces the formation of linear polymers with comma and globular shaped termini , whereas mutation of the canonical calcium-binding domain resulted in the formation of helical polymers and mutation in both EF-hand domains prevented the formation of linear polymers . We also demonstrate that in T . brucei the coiled-coil domain is able to target BILBO1 to the FPC and to form polymers whilst the EF-hand domains influence polymers shape . This data indicates that BILBO1 has intrinsic polymer forming properties and that binding calcium can modulate the form of these polymers . We discuss whether these properties can influence the formation of the FPC .
Trypanosoma brucei is an important parasitic protozoan that is the etiological agent of sleeping sickness in sub-Saharan Africa . Related parasites are responsible for Chagas disease and Leishmaniasis in South America and many tropical countries [1 , 2 , 3] . At the G1 stage of the T . brucei cell cycle a single flagellum exits the cell through the flagellar pocket ( FP ) , a structure that is located in the posterior end of the cell . The FP functions as the exclusive site for endo- and exocytosis , and has been shown to be an essential component of membrane trafficking and recycling [4 , 5 , 6] . In these roles the FP is essential for parasite virulence , because T . brucei must survive within both the gut and salivary glands of the tsetse fly as well as in the bloodstream of the mammalian host . Thus the FP is also most likely a functional design to sequester important parasite surface receptors away from detection by the host’s innate immune system [5 , 7] . The tight coupling between the FP , the flagellum , and the cytoplasmic membranes has been well established in recent studies where work on the T . brucei FP and associated cytoskeleton suggest that new FP biogenesis is precisely timed to coordinate with flagellum duplication and segregation [6 , 8] . Electron microscopic imaging and tomography clearly illustrate that a cytoskeletal structure called the flagellar pocket collar ( FPC ) , a horse-shoe/annular structure , of approximately 500–800 nm in diameter , in T . brucei , is present at the exit point of the flagellum [4 , 6 , 9] . The FPC surrounds the flagellum and is also attached to the sub-pellicular microtubule cytoskeleton [6 , 8] , but it is not known how it is attached , nor is it apparent how the FPC always forms its characteristic shape around a newly formed flagellum . Recently , an important structure called the bilobe has been identified as being closely associated with the FPC . The bilobe is considered to be a Golgi-linked structure that contains Centrin 2 , and numerous other proteins [9 , 10 , 11] . The intimate relationship between the FP-flagellum , the bilobe and Golgi [12] suggests that at least some of these structures are physically linked [11] . As we demonstrated previously , BILBO1 is syntenic and essential for biogenesis of the FPC [8] . RNAi knockdown of BILBO1 in T . brucei disrupts the formation of the FPC , inhibits the biogenesis of important cytoskeleton structures , induces severe perturbation of the endo-membrane system , cell cycle arrest , and is ultimately lethal . BILBO1 is the first , and to date the only , FPC molecular component identified that is required for FPC and FP biogenesis , which makes it a potentially important target for intervention against kinetoplastids [8] . Recently , the three-dimensional structure of BILBO1 N-terminal domain was solved and revealed that it contains an unexpected ubiquitin-like fold with a conserved surface patch [13 , 14] . Mutation of the patch was lethal when expressed in T . brucei procyclic forms suggesting that there are important interactions between the patch and other BILBO1 protein partners [13 , 14] . Using electron microscopy Vidilaseris et al . , demonstrated that the EF-hand domains of BILBO1 change their conformation upon calcium binding , and the coiled-coil domain can form anti-parallel dimers , which can then form linear polymers via the C-terminal leucine zipper . Further , they demonstrated that these filaments can condense into fibers through lateral interactions [15] . In this study , we turn to an analysis of BILBO1 protein as an essential candidate of the FPC scaffold . Our overall objective was to identify the molecular role of BILBO1 in FPC formation . The primary and secondary structures of BILBO1 do not predict a specific function , and the protein does not appear to have any obvious membrane-targeting domains , but it does possess two predicted EF-hand calcium-binding domains ( aa 185–213 and aa 221–249 ) . It also has a large coiled-coil ( CC ) domain ( aa 263–566 ) , which is involved in protein-protein interactions [15] . Based on our hypothesis that BILBO1 is the FPC scaffold , we decided to determine 1 ) if BILBO1 can form polymers in vivo , 2 ) what domain ( s ) of the protein is ( are ) involved in polymer formation . We approached these questions with the following experiments; 1 ) Identification of functional domains involved in BILBO1-BILBO1 interaction by yeast-two hybrid analysis 2 ) , test for intrinsic polymer formation properties of BILBO1 using a heterologous mammalian expression system and 3 ) , characterization of these properties in the parasite . We demonstrate in this work that BILBO1 can form polymers in vivo and propose that these may have important implications for the formation of the annulus/horseshoe of the FPC . The results we report here point to a substantial role for BILBO1 in forming the structural scaffold for FPC biogenesis and maintenance .
Yeast-Two-Hybrid ( Y2H ) analysis has been used to test interactions between soluble proteins , but also between polymer forming proteins [16 , 17 , 18] . We used this technique to test if BILBO1 could form homo-polymers and , if yes , identify the domains that are involved in this interaction . For this study several BILBO1 truncations were constructed and were named as follows; T1 for the N-terminal domain ( aa 1–170 ) , T2 for the N-terminal domain including both EF-hand calcium-binding domains ( aa 1–250 ) , T3 for the CC domain including the two EF-hand domains up to the C-terminus ( aa 171–587 ) , and T4 for the CC domain up to the C-terminus ( aa 251–587 ) ( Fig . 1A ) . BILBO1 and its truncations tested negative for toxicity and auto-activation in these Y2H experiments . Interactions were visualised using two auxotrophic assays with similar results ( minus Adenine is shown in Fig . 1B ) . As yeast growth was observed when full-length BILBO1 construct was tested ( Fig . 1B ) , our assays show that there is a BILBO1 x BILBO1 interaction ( full-length x full-length ) . Further , they show that the coiled-coil domain is required for the interaction ( full-length x T3 , or full-length x T4 ) , and that neither the N-terminal domain ( T1 and T2 ) nor the EF-hand domains 1 and 2 are required in this interaction per se ( Fig . 1B ) . BILBO1 has two putative calcium-binding EF-hand domains that are located between the N-terminus and the coiled-coil domain . In silico analysis of these EF-hand domains indicate that domain1 is non-canonical ( 12-residue loop—aa 194–205 , but contains a lysine at position +Y , whereas this is typically aspartic acid or asparagine ) , but domain 2 is canonical ( 12-residue loop—aa 230–241 ) [19 , 20 , 21] . The 3D structure of the N-terminus of BILBO1 has been solved , but the domain analyzed does not contain the EF-hands [14] . To characterize further the EF-hand domains , we tested the wild-type and mutated forms for calcium binding properties using isothermal calorimetry ( ITC ) [22] . The DNA sequence encoding the EF-hand domains ( amino acid residues 177–250 ) of wild-type BILBO1 were cloned to incorporate a N-terminal maltose-binding tag plus a 10 × histidine tag ( MBP-His10 ) . This construct was used as a template to make the mutated forms of the EF-hand domains . For the mutant forms the following amino acid substitutions were created: Mutated EF-hand 1 ( mEFH1: D194A , N198A , D202A , and D205A ) , and mutated EF-hand 2 ( mEFH2: D230A , N232A , and E241A ) , or both mutated EF-hands ( mEFH1+2 ) . The amino acids selected for mutation were based on published analysis of EF-hand function by Gifford et al . , [19] . A schematic of BILBO1 is shown in Fig . 2A , whilst Fig . 2B shows the purified proteins on an SDS-PAGE with Coomassie blue staining . The minor bands present under the 50kDa main bands are degradation products . Fig . 2C illustrates a superimposition of BILBO1-EF-hand domains onto the modeling template of the human calmodulin-like protein hCLP ( 1ggz . pdb ) [23] . The two proteins share 30% identity and 47% similarity in their primary sequences , which gave rise a very similar conformation with a root-mean-square deviation ( r . m . s . d ) of 0 . 82 Å over 70 aligned residues of the two structures . A ribbon diagram of the BILBO1-EF-hand domains derived from homology-based modeling , together with the two bound calcium ions from the template structure ( 1ggz . pdb ) , is shown in Fig . 2D . ITC demonstrated that the wild-type BILBO1 EF-hand domains do bind 2 calcium ions ( Fig . 2E , N = 2 . 11 , Kd = 3 . 46 μM ) , whereas mutation of either or both EF-hand domains caused loss of calcium binding ( Fig . 2F-H ) . As a negative control , no calcium binding was observed for the MBP fusion tag alone ( Fig . 2 I ) . To explore the possibility that BILBO1 can form polymers , it was expressed in an in vivo heterologous system in the absence of any other parasite-specific proteins . Since BILBO1 has no known mammalian orthologues , we can investigate the polymers formed in U-2 OS cells in detail albeit out of context of the FPC . By transient transfection of U-2 OS cells , we expressed full-length , untagged BILBO1 protein or BILBO1:GFP and analyzed the polymers formed . We acknowledge that there may be other proteins interacting with BILBO1 when expressed in these cells and these can contribute to polymer formation . Nevertheless , direct GFP fluorescence , immuno-labelling of untagged BILBO1 with the anti-BILBO1 monoclonal antibody 5F2B3 or electron microscopy illustrated that expression from six to 24 hours resulted in the formation of long fibrous polymers ( Fig . 3A—D also refer to S1 Fig . ) . Thus , confirming our hypothesis that BILBO1 can indeed form polymers in vivo . We noticed the formation of numerous isolated annular structures when BILBO1:GFP was expressed in U-2 OS cells , but since this appears to be a GFP-tag induced artefact , these structures were not analyzed further ( Fig . 3A and 3G , and S2 Fig . ) . Polymers that started or terminated with globular , or annular/comma , shaped structures , were observed when untagged BILBO1 was expressed ( Fig . 3B-F ) . When viewed by transmission electron microscopy these polymers had transversal striations that were observed after negative staining to have a mean periodicity of 46 . 9 nm ( n = 1067 , SE± 0 . 4nm ) indicating the formation of highly ordered polymers , ( Fig . 3C-F ) . Interestingly , this is similar to the inter N-termini distance of anti-parallel BILBO1 proteins ( 40–45nm ) observed by Vidilaseris et al . , 2014 [15] , suggesting a similar assembly arrangement of polymers . A similar periodicity was also observed with the BILBO:GFP construct , but since GFP induces artefacts these striations were not analyzed in detail ( Fig . 3G ) . We have categorized the linear polymers observed by immunofluorescence as “simple”’ or “complex” . Simple polymers have no distinguishable features at their ends , whereas complex polymers have comma or globular structures at one or both ends . To facilitate nomenclature , these structures , from here onwards , will be referred to collectively as “termini” . Fig . 3C-F illustrates termini when visualised by electron microscopy and show that the shapes of these termini are varied . Termini that looked like “spheres” using immunofluorescence are actually dense globular structures when viewed by electron microscopy ( Fig . 3E ) . Formation of these globules maybe due to non-specific aggregation but it is not apparent how comma/annuli are formed , but they appear to be shaped when termini curl back upon themselves . Polymers could be observed by immunofluorescence microscopy whereas the cell extraction procedure required for electron microscopy removed most of the polymers making them difficult to observe , despite numerous attempts to do so . We therefore measured the width of the BILBO1 fibres and the diameter of the termini using immunofluorescence microscopy . Globules and commas were sometimes difficult to distinguish by immunofluorescence and were therefore grouped together and counted as termini . We also counted the number and type of polymers with complex termini per cell after six and 24 hours of untagged BILBO1 post transfection , ( please refer to Table 1 and Fig 3H and 3J ) . Measurements of BILBO1 fibre width and termini diameter was done using immunofluorescence microscopy . Measurements were made after six and 12 hours , whereas complex termini were measured after six and 24 hours . Fibre width and the number of fibres with termini at both ends increased over time . From this data and the electron microscopy results we conclude that BILBO1 has the intrinsic capacity to polymerize into organized high order polymers in mammalian cells ( in the absence of any other parasite-specific proteins ) and these polymers have a tendency to form complex termini at their extremities . Reiterating the results from Fig . 3I this data suggests that the polymerization state of BILBO1 is likely to be highly dependent on the expression level of the protein or local protein concentration . In addition to these measurements we measured the total fluorescence emitted per cell , as arbitrary units , after a fixed time of acquisition . We used the fluorescence intensity produced by each cell as a marker for protein concentration per cell . We then divided the population into two groups—cells producing less than 50% of the maximum fluorescence recorded , and cells producing more than 50% of the maximum fluorescence recorded . After six hours of expression of untagged BILBO1 81% of cells produced 50% or less of the maximum fluorescence recorded/cell , and 37 . 2% of these cells contained polymers with only simple termini , whilst 11 . 6% contained polymers with complex termini and 51 . 2% contained both types of polymer . 19% of cells produced more than 50% of the maximum fluorescence recorded/cell , and 20% of these cells contained polymers with simple termini , whilst 30% contained complex polymers and 50% contained both types of polymer ( Fig . 3I ) . After 24 hours of expression of untagged BILBO1 , 81 . 7% of cells produced 50% or less of the maximum fluorescence recorded/cell and 70% these cells contained polymers with simple termini , whilst 10% contained polymers with complex termini and 20% contained both types of polymer . 18 . 3% of cells produced more than 50% of the maximum fluorescence recorded/cell and 11 . 1% these cells contained polymers with only simple termini , whilst 44 . 4% contained polymers with only complex termini and 44 . 5% contained both types of polymer ( Fig . 3I , 24 hours post-transfection ) . Taken together this data reiterates the hypothesis that the polymerization state of BILBO is likely to be highly dependent on the expression level of the protein . To investigate whether the BILBO1 polymers were associated with pre-existing or newly formed cytoskeleton structures , U-2 OS cells expressing BILBO1:GFP were additionally labelled for F-actin ( phalloidin ) , intermediate filaments ( anti-vimentin ) , microtubules ( anti-tubulin ) , endoplasmic reticulum Golgi ( anti-giantin ) and ( anti-calnexin ) . The results presented in S2 Fig . demonstrate that BILBO1:GFP does not co-localize with any of these structures . We conclude that the polymers formed by BILBO1 are not promoted by the interaction with the ER , Golgi or the cytoskeletal structures tested . Phosphorylation or dephosphorylation can induce conformational or interaction changes in proteins . Our LC-MS/MS analysis on procyclic form ( PCF ) whole cell extracts identified one phosphorylated residue ( S163 ) as already described in T . cruzi bloodstream forms and T . brucei procyclic forms BILBO1 [24 , 25] . We postulated that phosphorylation and/or dephosphorylation of serine 163 could induce a conformational change leading to polymer shape changes . Mutation of BILBO1 serine 163 to non-phosphorylatable alanine , or to phosphomimetic aspartic acid , did not influence the type of polymers formed when expressed in U-2 OS cells suggesting that the phosphorylation of serine 163 is not regulating the conformation of BILBO1 or at least polymer formation . Since phosphorylation of serine 163 did not influence polymer formation we interrogated BILBO1 in detail to identify the domain ( s ) that permit polymer formation . Thus , we expressed truncated forms of untagged BILBO1 in U-2 OS cells and localized the truncations using immunofluorescence with the monoclonal antibody specific to the CC domain ( anti-BILBO1 , 5F2B3 ) [8] , or a polyclonal antibody specific to the N-terminal domain of BILBO1 ( amino acids 1–110 , anti-NTD ) [9] . The truncation T1 did not produce polymers , and was uniformly distributed throughout the cytoplasm ( Fig . 4A ) . Additionally , T1 labelling could be extracted by mild detergent treatment and is found in the soluble fraction ( S ) by western-blot ( WB ) whilst full-length BILBO1 is found in the insoluble fraction ( P ) ( Fig . 4E ) . T2 was also extracted with detergent treatment , and indicates that T2 is soluble , but can form small , insoluble , punctate aggregates ( Fig . 4B , and 4E ) . The data also suggests that aggregation is likely to be highly dependent on expression level of the protein . Confirming the Y2H interaction results described earlier , T3 and T4 formed linear polymers ( T3 ) and spindle shaped polymers ( T4 ) , but no termini were observed on these fibres ( Fig . 4C-D ) . Similar to full-length BILBO1 these polymers are insoluble and were only found in the pellet fraction by WB ( Fig . 4E ) . Taken together , these results demonstrate that neither the N-terminal domain ( corresponding to T1 and T2 ) nor the EF-hands form linear polymers , but they can influence the type of polymer formed by the CC domain . We then turned to T3 and T4 and measured the width of induced polymers after six and 24 hours post transfection , ( please refer to Table 2 and Fig . 4F ) . From these measurements we noted an increase in T3 polymer width over time compared to T4 , which suggests easier lateral binding of T4 to polymers , and/or may reflect differences in protein expression levels . Since we have established that both BILBO1 EF-hand domains bind calcium and that BILBO1 can form polymers in vivo , we wanted to test the potential role of the EF-hand domains in modulating polymers formed in U-2 OS cells . We therefore expressed mutated forms of EF-hand domain 1 ( mEFH1 ) , EF-hand domain 2 ( mEFH2 ) , or both mutated EF-hand domains ( mEFH1+2 ) in U-2 OS cells ( Fig . 5 ) . Mutation of EF-hand domain 1 had a substantial effect on polymerization because the formation of long polymers was abolished , and only small insoluble punctate aggregates were observed ( Fig . 5A ) . Similar aggregates were observed when both EF-hand domains 1 and 2 were mutated ( mEFH1+2 ) ( Fig . 5C ) . Surprisingly , six-hour expression of mEFH2 resulted in the formation of insoluble helical-like , comma and annular polymers ( Fig . 5B , D ) . Attempts to visualize these by electron microscopy failed due possibly to loss of sample upon cell extraction and/or poor visualization because of the presence of cell debris . Nevertheless , using immunofluorescence we classified them and measured their dimensions six hours post transfection , ( please refer to Table 3 and Fig . 5E ) . The results of these data suggest that EF-hand 2 can radically influence the type of polymer formed , but it also illustrates that these structures have similar diameters even though they can be a helix , comma or annulus in shape ( Fig . 5E ) . Interestingly , the FPC diameter in wild-type T . brucei cells is 842 nm , which is a diameter comparable to those of the mEFH2-induced structures ( please refer to Table 3 ) . Western blots of untagged and mutated BILBO1 proteins , that were expressed in U-2 OS cells , were probed with anti-NTD , and show that all proteins were present in the pellet fraction of extracted cells ( ∼70 kDa band ) and that anti-NTD recognises the full-length proteins ( Fig . 5F upper panel ) . The mouse monoclonal 5F2B3 recognizes the C-terminus of the CC domain of these proteins and also recognises the full-length proteins , whereas anti-NTD recognises full-length proteins and the N-terminus . 5F2B3 labelling also revealed a lower band of ∼ 60kDa indicating probable N-terminal degradation ( Fig . 5F lower panel ) . This data led us to question the stability of mEFH1+2 and we therefore analyzed cells after 24 hours expression followed by a six-hour treatment with the proteosome inhibitor MG132 [26] . Cells were then probed with anti-NTD ( S3A Fig . ) and counted for positive mEFH1+2 signal ( S3B Fig . ) . We also tested mEFH1+2 levels by western blotting using 5F2B3 ( S3C Fig . ) . The counts illustrated that 3 . 9% ( n = 633 , SE± 0 . 29% ) of the population were positive for mEFH1+2 signal in the absence of MG132 treatment , whereas 23 . 3% ( n = 559 , SE± 3 . 5% ) were positive after MG132 treatment ( S3A , B Fig . ) . Quantification of western blots of mEFH1+2 probed with 5F2B3 , and normalization with anti-tubulin loading control , indicate that there is 1 . 5 x fold more mEFH1+2 protein in cells after MG132 treatment ( S3C Fig . ) . Oddly , we did not observe the ∼ 60kDa band noted previously in Fig . 5F in these untreated cells and are uncertain why , but it may signify that degradation of mEFH1+2 is not consistent and that degradation rates and origin on mEFH1+2 can vary . Importantly , this data indicates that there is up to 40% degradation of mEFH1+2 when expressed in U-2OS cells , but more significantly no linear , helical or annular polymers were detected in cells in the absence or presence of MG132 treatment . Our previous work has shown that a brief ectopic expression of a full-length tagged version of BILBO1 protein ( GFP-BILBO1 ) in trypanosomes results in targeting and assembly into the FPC [8] . We accept the caveat that the expression of truncated , mutated or full-length forms of BILBO1 in trypanosomes will clearly involve the binding and/or interaction of other FPC proteins , which may influence the structures produced . However , we wanted to observe whether ectopic expression of these proteins would indeed form polymers in a trypanosome context . To avoid potential steric hindrance due to GFP and to discriminate between the endogenous BILBO1 , we created C-terminus myc-tagged forms of the protein that are identical to those expressed in U-2 OS cells ( for clarity , described here as T1:myc—T4:myc ) and expressed them in procyclic T . brucei cells ( Fig . 6 ) , using the previously described tetracycline inducible expression system [8 , 27] . T . brucei cells expressing different truncations were detergent extracted to make cytoskeletons ( CK ) and simultaneously probed with the following antibodies anti-NTD ( which labels the endogenous BILBO1 and BILBO1:myc , T1:myc , T2:myc , but not T3:myc or T4:myc truncations , in which case 5F2B3 was used ) , and anti-myc , which labels only myc tagged ectopic proteins . After six hours of induction we observed BILBO1:myc at the FPC thus demonstrating that the myc tag does not affect the localization of the protein ( Fig . 6A a ) . By western blotting we noted an additional band present under the main band ( Fig . 6C ) after six or 24 hours of expression and suggest that this is a degradation product . It is weaker after 24 hour expression compared to six hours suggesting more complete degradation to small peptides , much less degradation or degradation during or after sample preparation ( for example when making cytoskeletons ) ( Fig . 6C ) . T1:myc or T2:myc truncations were observed in the cytoplasm by WB ( Fig . 6C ) , but no signal was present in detergent extracted cytoskeletons by immunofluorescence or WB ( Fig . 6A b-c , 6B b-c , 6C ) . The solubility of T1:myc and T2:myc truncations is in agreement with the soluble forms observed when expressed in U-2 OS cells and , with respect to trypanosomes , it indicates that neither the N-terminus nor the EF-hand domains are sufficient for targeting or binding to the FPC . Extensive expression of T1:myc or T2:myc truncations ( >24 hours ) did not dramatically affect cell morphology or cell growth ( Fig . 6B , and D ) indicating that neither of these domains induce dominant negative effects . When probed with anti-NTD antibody endogenous BILBO1 localization at the FPC was not impaired or modified during T1:myc or T2:myc expression because the NTD-labelling was unmodified . By probing the samples with NTD , and an anti-tubulin loading control , on western blots we were able to measure the native levels of BILBO1 expression in cells expressing T1:myc or T2:myc truncations for six or 24hours and these were both shown to be 1 . 4 x higher than wild-type levels . After 24 hours induction these levels remained at 1 . 4 x higher than wild-type levels ( S4 A Fig ) . Immunofluorescence on procyclic trypanosome cytoskeletons and western blot analysis of whole cells ( WC ) or cytoskeletons ( CK ) demonstrated that T3:myc truncation is insoluble and is associated with the cytoskeleton ( Fig . 6A d and 6B d ) . T3:myc targets primarily to the FPC , but also forms a subset of short fibres that below the FPC ( Fig . 6A d ) . Surprisingly , these fibres were myc positive , but NTD negative , implying that they were formed predominately by T3:myc truncation . In contrast to T1:myc and T2:myc , longer expression of the T3:myc truncation resulted in targeting and binding to the FPC with deleterious effects; endogenous BILBO1 ( labelled with anti-NTD ) and T3:myc co-localized to a single FPC structure from which both flagella ( old and new ) emerged ( Fig . 6B d ) . Moreover , in these T3:myc induced cells , the new flagella were detached from the length of the cell body . This phenotype was characterised by flagellum attachment at the basal body region , but detachment along the length of the cell . For simplicity we have called this a “detached flagellum” phenotype . These cells died within 24 hours of T3:myc induction ( Fig . 6D ) . Interestingly , detached flagella and cell death are phenotypes observed in the induced BILBO1 RNAi cell line [8] . Expression of T4:myc for six hours produced long fibre-like polymers that were observed within the cytoplasm ( Fig . 6A , e ) . This implies that the CC domain , in the absence of the N-terminal domain or the EF-hand domains , is able to form polymers in T . brucei as well as in U-2 OS cells . Similarly to T3:myc , expression of >24 hours of T4:myc produced polymer structures that did not contain endogenous BILBO1 signal ( Fig . 6B , e ) . Notably , cells expressing T4:myc died within 24 hours of induction ( Fig . 6D ) and , as with T3:myc expression , these cells had a detached new flagella phenotype ( Fig . 6B e ) . As with T1:myc and T2:myc , we probed the T3:myc and T4:myc protein samples by western blot with NTD , anti-myc and anti-tubulin and measured the native levels of BILBO1 expression . After 6 hours of induction , endogenous level of BILBO1 appeared to be 6 . 6 x fold and 5 . 1 x fold higher in T3:myc and T4:myc truncation expressing cells respectively . After 24 hours of expression these levels were shown to be 6 . 3 x and 4 . 2 x fold higher respectively compared to wild-type levels , ( S4 B Fig . ) . Given that the expression of T3:myc or T4:myc truncations in trypanosomes is lethal and induced detached flagellum phenotypes we counted the percentage of cells exhibited this phenotype after six or 24 hours of expression and compared this to T1:myc , T2:myc and BILBO1:myc expression . Fig . 7E shows that wild-type cells do not have a detached flagella phenotype , and cells expressing T1:myc and T2:myc truncations produced less than 2% detachment . However , after a 24 hour induction T3 cells showed a 36 x fold increase in detached flagella phenotypes , T4 cells showed 15 . 2 x fold increase and BILBO1:myc showed a 6 . 3 x fold increase ( please refer to Table 4 ) . This data suggest that the high number of detached flagella phenotypes induced by BILBO1:myc , T3:myc and T4:myc expression is likely to be a dominant negative effect and the secondary effects of this have influenced the formation or function of the flagella attachment zone ( FAZ ) [6] resulting in detached flagella . The results obtained by Y2H analysis for T1-T4 correspond with the results obtained using the same truncations expressed in U-2 OS cells and in trypanosomes . Essentially , For T1 and T2 there is no BILBO1 interaction by Y2H , also no polymer formation was observed when these truncations were expressed , but positive protein-protein interaction and polymer formation was observed with expression of full-length BILBO1 or T3 or T4 truncations . These data indicate that the CC domain is required for polymerization and targeting/binding to the FPC . It also illustrates that over-expression ( 24 hours ) of the CC domain alone ( T4:myc ) is sufficient to induce the detached flagellum phenotypes . To analyse in detail the role of the BILBO1 EF-hand domains in trypanosomes we expressed mEFH1:myc , mEFH2:myc , and mEFH1+2:myc in T . brucei procyclic cells ( Fig . 7 ) . Expression of mEFH1:myc for six hours demonstrated targeting to the FPC , but in addition we observed the formation of several helical structures independent of the FPC ( Fig . 7A a ) . The diameter of the helices and comma/annuli structures after six hours of expression was 747 nm ( n = 67 , SE± 20nm ) and 883 nm ( n = 39 , SE± 27nm ) respectively ( Fig . 7F ) . As with T1—T4 experiments , we probed the proteins from cells expressing BILBO1:myc and mutated EF-hand:myc by western blot and compared them to the native levels of BILBO1 . Notably , we observed degradation bands at ∼ 60kDa , which is probably due to the same degradation related reasons we have specified earlier for the expression of BILBO1:myc ( Fig . 6A a ) . Nevertheless , after six hours of expressing BILBO1:myc , mEFH1:myc , mEFH2:myc , or mEFH1+2:myc , wild-type BILBO1 protein levels were shown to be 1 . 1 x , 0 . 9 x 1 . 3 x and 0 . 9 x fold higher or lower respectively than the expressed myc tagged levels , ( S4 , C Fig . ) . After 24 hours of expression these levels were 2 . 5 x , 1 . 3 x , 2 . 9 x , and 1 . 0 x higher respectively than myc tagged protein levels . Because mEFH1:myc formed helices when expressed in trypanosomes , we wanted to compare helix dimensions with that of the FPC . We therefore measured the diameter of the FPC ( major axis diameter ) in wild-type ( endogenous BILBO1 ) , BILBO1:myc and mEFH1-myc expressing cells ( Fig . 7F ) . We measured the FPC diameter of wild type G1 procyclic cells after immunolabelling with anti-NTD antibody and the FPC of BILBO1:myc and mEFH1-Myc expressing cells , for six hours , using anti-myc antibody . The diameter of wild-type FPC was 842 nm ( as noted in Table 3 ) whilst in the BILBO1:myc expressing cells the diameter was measured as 866 nm ( n = 48 , SE± 20nm ) and was 923 nm ( n = 88 , SE± 20nm ) in mEFH1:myc expressing cells ( Fig . 7F ) . The fact that mEFH1:myc structures were helical indicates that mutation of EF-hand domain 1 influences the type of polymer formed , in the context of the trypanosome ( Fig . 7A a ) . Expression of mEFH1:myc ( >24 hours ) induced the formation of myc and NTD positive , globular , insoluble structures followed by cell death occurring between 24–48 hours of induction ( Fig . 7B , D ) . Noticeably , as previously observed in T3 and T4 expressing cells , and in BILBO1 RNAi knockdown cells [4 , 8] , expression of mEFH1:myc induced detached flagella phenotypes ( Fig . 7B a ) ( see below for quantification ) . We observed the formation of a single insoluble BILBO1 positive structure within the length of the new detached flagellum . The presence of this structure along the flagellum suggests the mis-targeting of this mEFH1:myc form of BILBO1 ( Fig . 7B a , asterisk ) . Similar to observations made with BILBO1:myc , the mEFH2:myc targets to the FPC after a six hour induction , suggesting that EF-hand domain 2 function is not required for targeting to the FPC ( Fig . 7A b ) . Expression >24 hours resulted in cells with new flagella detached phenotypes ( see below for counts ) and the formation of large insoluble structures that were not associated with the old or new flagella . Cell death occurred between 24 and 48 hours ( Fig . 7B b and 7 D ) . The majority of mEFH1+2:myc protein when expressed in trypanosomes for six hours was soluble , as observed by WB ( Fig . 7C ) . As in the expression of mEFH1+2 in U-2 OS cells we cannot rule out the possibility of rapid degradation since degradation bands were observed after six hours , and to a much lesser extent 24 hours , of expression . Indeed mEFH1+2:myc expressing cells treated with MG132 had 2 . 2 x fold higher protein levels when compared to non-treated cells suggesting mEFH1+2:myc degradation via the proteasome ( S4D Fig . ) . A weak mEFH1+2:myc signal could be detected on cytoskeletons probed by immunofluorescence and western-blot ( Fig . 7A , B , C ) and extensive expression of mEFH1+2:myc ( >24 hours ) resulted in a very weak mEFH1+2:myc immunofluorescence signal close to both old and new FPC structures . When probed by WB a weak signal was also observed in cytoskeleton samples that had been expressing mEFH1+2:myc for 24 hours ( Fig . 7 C ) . Cells died after >24 hours expression of mEFH1+2:myc ( Fig . 7B , D ) . Long induction also induced the production detached flagella phenotypes ( see Table 5 for quantification ) . In all cases where mutated mEF-hand proteins were expressed we noticed that after 24 hours of expression little or no anti-BILBO1 signal ( endogenous or myc-tagged ) was detected at the base of the new flagellum suggesting sequestration or degradation of wild-type BILBO1 ( Fig . 7B a-c ) . Since the expression of mEFH1:myc , mEFH2:myc or mEFH1+2:myc in trypanosomes induced detached flagella phenotypes , we investigated the extent of their appearance . We observed a significant difference between a six hour expression and 24h expression of mEFH1:myc because the number of detached flagella phenotypes increased by 17 x fold . For mEFH2:myc expression we observed a 6 x fold increase in detached flagella and for mEFH1+2:myc there was a 20 x fold increase in detached flagella phenotypes , ( please refer to Table 5 and Fig . 7E ) .
The results in this study support the concept that BILBO1 has self-assembly properties that are dependent on the CC region of the protein , and that can be influenced by the EF-hand domains at its N-terminus . We hypothesize that BILBO1 is well suited to play a role in the formation of the FPC annulus . The results reported here establish that BILBO1 can , autonomously , support the formation of various polymers including helical polymers . We suggest that these polymer-forming properties are important in building a FPC . We demonstrate here that the two tandem EF-hand domains of BILBO1 bind calcium in vitro and that mutation of these domains either together or independently prevented calcium binding . The observation that mutation of one EF-hand domain prevented the second from binding calcium is not unprecedented . In fact , the basic unit for a functional EF-hand protein is a pair of EF-hand domains that stably form a four-helix bundle [28 , 29 , 30] . Previous studies have also shown that the four-helix bundle should be treated as a single global structure because the two EF-hand domains bind calcium cooperatively [30] . Indeed , our ITC titration fit curve for wild-type TbBILBO1-EF-hand domains indicate that the molar ratio between Ca2+ and the protein was approximately 2 ( Fig . 2E ) , suggesting that both EF-hand domains bind calcium . The expression in mammalian cells or trypanosomes of full-length BILBO1 with mutations on either or both EF-hand domains produced different types of polymers ( see below ) , which would also suggest that both hands bind calcium . The data obtained from Y2H analysis , U-2 OS and trypanosome studies strongly indicate that BILBO1 x BILBO1 interactions exist , and in many cases , are targeted to the FPC of trypanosomes . We have shown that long-term over-expression of full-length BILBO1:myc in T . brucei procyclic cells is lethal . When expressed in T . brucei procyclic cells the T1 and T2 truncated forms of BILBO1 are soluble , but neither can interact with full-length BILBO1 nor with the CC domain , and do not form polymers . We can thus consider that the N-terminal domain of BILBO1 is not directly involved in BILBO1 polymerization . On the other hand , the N-terminal domain can regulate the shape of the polymer . Expression of mutated EF-hand proteins in trypanosomes did not prevent targeting to the FPC indicating that they bind to native BILBO1 . mEF-hand 1+2 location to the FPC was very weak when detected by immunofluorescence , but it was also mostly degraded under these conditions . Further , we carried out yeast two-hybrid analysis to test whether full-length BILBO1 interacts with full-length BILBO1 , or deltaEF-H1+2 ( a deleted EF-hand form of BILBO1 where the N-terminal domain is retained ) and full-length BILBO1 versus mEF-hand1+2 . We also tested mEF-hand1+2 versus T4 truncation , or mEF-hand1+2 versus T3 truncation . In all cases BILBO1 interacted with these modified proteins indicating that the EF-hand domains are not required for this interaction . Interestingly , the double EF-hand mutant mEF-hand1+2 also bound to BILBO1 indicating that neither lack of calcium binding nor the conformational change induced by calcium binding prevents BILBO1-BILBO1 interactions ( S5 A Fig . ) . Recently , nuclear magnetic resonance ( NMR ) and X-ray crystallographic techniques were used to solve the 3D structure of the N-terminal domain of BILBO1 . That work involved experiments demonstrating that the N-terminal domain has an exposed surface patch within an ubiquitin-like fold . Expression in trypanosomes of full-length-BILBO1 mutated on this patch is lethal suggesting that this region has important functional roles which may include interaction with other FPC proteins [14] . Additionally , the authors also showed that EF-hand domains of BILBO1 changes its conformation upon calcium binding , and the CC domain forms anti-parallel dimers and linear polymers and these filaments can condense into fibres through lateral interactions [15] . When expressed in T . brucei procyclic cells , T3:myc and T4:myc fragments localised to the FPC , and produced lethal effects . It is also possible that they induced dominant negative effects because detached flagella phenotypes were observed after expression of these proteins , indicating a probable interruption of flagellum attachment zone ( FAZ ) function or biogenesis . When expressed in U-2 OS cells , T1 and T2 truncation were soluble , and T3 and T4 truncations formed polymers . Interestingly , expression of the T4 truncation formed exclusively linear spindle-like polymers in these cells . From these results we conclude that the CC domain of BILBO1 can spontaneously form homo-polymers in vivo and these are linear due to the absence of the N-terminus , which functions to modulate the type of polymer formed . It is not clear how BILBO1 polymers are formed in U-2 OS cells and although trypanosome FPC proteins are absent there may be other proteins interacting with BILBO1 contributing to the formation of polymers . Since the FPC has been established in earlier published studies to be an annulus/horseshoe in vivo we suggest that , in the parasite , BILBO1 could form the template for the FPC , which then associates with other FPC proteins perhaps under the modulation of calcium to form the final annulus/horseshoe [4 , 8] . Further evidence for BILBO1 calcium binding and polymer forming properties was provided by Vidilaseris et al . , 2014 [15] . Using in vitro structural dissections of BILBO1 they showed that the EF-hand domains change conformation upon calcium binding , and the central CC can form anti-parallel dimers . They demonstrated that a C-terminal leucine zipper is present and appears to contain targeting information so that inter-dimer interactions can form between adjacent leucine zippers of the CC domain , which allow BILBO1 to form extended filaments . Finally , they showed that these filaments could condense into fibers through lateral interactions [15] . From these observations it is suggested that two BILBO1 molecules can form an anti-parallel dimer via their CC domains , which assemble into a filament through the interactions between the C-terminal leucine zippers . BILBO1 forms polymers spontaneously in vitro with no apparent need for nucleotide hydrolysis [15] , but spontaneous polymerization is not unprecedented for cytoskeleton proteins and has been observed for intermediate filaments and bacterial flagellins [31 , 32 , 33 , 34] . Within the parasite BILBO1 is unlikely to function alone and must interact with other proteins to form the scaffold of the FPC . Based on Y2H analysis we have identified numerous BILBO1 binding proteins , two of which bind to the N-terminus of BILBO1 . One of these proteins , FPC5 , is kinetoplastid specific , but it is feasible that other BILBO1 binders are structural and may influence BILBO1 conformation in vivo . Indeed it is also possible there is interplay between calcium and protein binding to BILBO1 and this may regulate how BILBO1 functions or perhaps even form polymers . Data to support this in presented in S5B Fig . where we use Y2H assays to illustrate that the BILBO1 binding domain of FPC5 interacts with full-length BILBO1 as bait or prey , but does not interact with BILBO1 if the EF-hand domains have been deleted or mutated to prevent calcium binding . In order to test the effects of calcium chelation on polymers we treated U-2 OS cells , which have expressed untagged BILBO1 for six or 24 hours , with the membrane permeable calcium chelator BAPTA-AM . We did not observe any difference between treated and untreated cells ( S6A Fig . ) suggesting that once polymerization occurs the protein is somewhat stable to calcium chelation . We also treated cytoskeletons derived from cells that had expressed BILBO1myc or the EF-hand mutants , with 50mM EGTA , but the chelation treatment did not disrupt the FPC nor solubilize the polymers formed by mEF-hand 1 domains under these conditions ( S6B Fig . ) . The former results suggests that these polymers may have very strong and/or irreversible inter-molecule interactions , and the latter result is probably due to both strong interactions and the stabilizing properties of other FPC proteins that prevent depolymerisation . Such stabilization is not unprecedented and a good example of this is alphaB-crystallin , which has been demonstrated to stabilize microtubules against calcium depolymerization [35 , 36] . The results observed from the ITC studies and the effects of the mutation of the EF-hand domains in BILBO1 on polymer assembly in vivo demonstrates that BILBO1 does binds calcium . The expression of mEFH1 in U-2 OS cells produced small aggregates , whereas expression of mEFH2 in these cells induced the formation of helical polymers . When expressed in trypanosomes mEFH1 formed helical polymers whereas mEFH2 did not . We do not have a clear explanation for this difference between mammalian cells and trypanosomes , but one can speculate that it may reflect a change in three-dimensional structure when the protein in question interacts with , or binds to , parasite specific proteins . It is apparent from our studies and confirmed by other work that mutating calcium-binding domains can also influence BILBO1 structure and this may influence if and how polymers are formed [15] . The fact that BILBO1 can form different shapes such a linear or annular/comma shaped polymers is especially interesting when we consider the helices formed when either EF-hand domain was mutated . Therefore it is possible that these mutations prevent calcium binding and could stop the domain folding correctly , which could result in alternative BILBO1 polymerization . In trypanosome procyclic cells mEFH1:myc targeted to the FPC , but also within the new detached flagellum of these cells . It is unclear why these mis-targeted mEFH1 “spots” are present within the length of the flagellum , but it may be due to the limited ability to bind to partners for FPC retention or may be due to modified access to the intraflagellar transport system . Mutated EFH1+2:myc was apparently predominantly soluble when expressed in trypanosomes , but in this context it is not unusual , because the mutation of a single amino acid has been noted in some cases to modify protein solubility such as maltose binding protein and haemoglobin [37 , 38 , 39] . We also show that EFH1+2:myc is mostly soluble , but can form some small punctate or aggregates in U-2 OS cells and is predominantly degraded when expressed . Longer expression of EFH1+2:myc in trypanosomes induced flagella detachment phenoptypes suggesting perturbation of the FAZ . Low levels appear to be very toxic to cells perhaps by recruiting inappropriate proteins or preventing the correct binding of partner proteins such as FPC5 . In the trypanosome expression experiments the mutant mEF-hand 1 and 2 proteins are only recognized by the anti-myc antibody and show that they do target to the FPC . This provides some evidence to suggest that in a six-hour induction they do not sequester all native BILBO1 from targeting to the collar . After 24 hours of expression this is not the case and native BILBO1 does appear to be degraded , not targeted to , and/or sequestered from the FPC as seen in Fig . 7 , B a and b . The sequestering of wild-type BILBO1 may also be the cause of flagella detachment and cell death . The diameter of mEFH2-induced helices in U-2 OS cells was 730 nm and comma diameter was 787 nm whilst annuli diameter was 771 nm . The diameter of the helices formed by procyclic cells expressing mEFH1:myc is 750nm , whilst the diameter of the wild-type FPC was 842 nm . The similarity between the diameters of these polymers compared to the FPC is probably not coincidental and could be envisaged to be associated with the intrinsic properties of BILBO1 . Based on our observations , we propose a model in which BILBO1 is a structural scaffold of the FPC and assembles into polymers via the CC domain and leucine zipper as proposed by Vidilaseris , et al . , 2014 [15] . This polymer forms an elliptical or annular structure , the exact state depending on the calcium-binding functionality of the EF-hand domain and the presence or absence of partner proteins . In this model the role of the EF-hand domain is vital and is involved in the plasticity of the structure , but clearly in the trypanosome there must be other proteins that bind to or regulate the structure of the FPC . If we consider a hypothesis where the FPC changes dimensions to accommodate a new flagellum during the cell cycle then one hypothetical interpretation could be that the interplay with binding partners is important during the parasite cell cycle , wherein an annular/horseshoe shaped FPC is required in early cell cycle stages , whilst a more elliptical FPC is required during emergence of the new flagellum during S/G2 or later . Nevertheless , it is unclear how precisely a new FPC is formed . If a new FPC is formed de novo then we would expect much less need for dramatic changes in its shape and this interpretation provides a role for BILBO1 whereby it forms a FPC as the new flagellum exits the FP . The primary and motile cilia and the flagella of differentiating spermatids have a ciliary pocket ( CP ) [40 , 41 , 42] , which is physically associated with clathrin-coated endocytotic vesicles [40 , 41] and shares a conspicuously similar structure to the FP . Surprisingly , it is not known if the equivalent of the FPC , the CP collar ( CPC ) actually exists . Therefore it is unknown if the CP indeed requires a BILBO1-like protein or if the CP is constructed differently to the FP . The importance of a precisely constructed primary cilia cytoskeleton is revealed by studies on ciliopathies; defective primary cilia in humans lead to polycystic kidney disease and a variety of other illnesses [43 , 44 , 45 , 46 , 47] . Clearly , a thorough understanding of how the FP and/or the CP are formed will provide important insights into both parasite biology and human ciliopathies . The data we have reported here have elucidated in vivo , and in a tractable system , some interesting properties of BILBO1 and these have advanced our understanding of how the FP is constructed . The ongoing search for the identification and characterization of additional FPC proteins will add to our understanding of the ways in which the FPC is organized and maintained . We anticipate that this data will be useful to obtain a more general understanding of the assembly of kinetoplastid FPC complexes and provide important clues on how to inhibit FPC biogenesis .
U-2 OS cells ( human bone osteosarcoma epithelial cells , ATCC Number: HTB-96 [48] were grown in D-MEM Glutamax ( Gibco ) supplemented with final concentrations of 10% fetal calf serum ( Invitrogen ) , 100 units . mL-1 of Penicillin ( Invitrogen ) , and 100 μg . mL-1 of Streptomycin ( Invitrogen ) at 37°C plus 5% CO2 . Exponentially growing U-2 OS cells in 24 well plate with glass coverslips were lipotransfected as in Dacheux et al . , [49] with 0 . 5–2 μg DNA using Lipofectamine 2000 in OPTIMEM ( Invitrogen ) according to the manufacturer’s instructions and processed for IF six to 24 hours post-transfection . The BILBO1 ORF and truncations were amplified by PCR from T . brucei TREU927/4 GUTat10 . 1 genomic DNA [50] . The work described in this study uses the parental procyclic form ( PCF ) T . brucei 427 29–13 cell-line , co-expressing the T7 RNA polymerase and tetracycline repressor , named for the purposes of this study as wild-type ( WT ) [51] . WT cells were transfected with NotI linearized plasmids as in [52] and cloned . Expression of recombinant proteins was induced with 1 μg . mL-1 tetracycline . Growth curves were done by using a mallassez cell counter every 24 hours and by diluting the cells back to 3 . 106 cells/ml . Growth curves in Figs . 6 and 7 represent the cumulative cell number . Mammalian expression vectors . The BILBO1 ORF and truncations were cloned into the pcDNA3 between HindIII-XbaI sites for BILBO1 full length and EcoRI-XhoI for the truncations or into pcDNA3 . 1 CT-GFP TOPO ( Invitrogen ) . Mutations of the EF-hand domain 1 ( D194A ( GAT/GcT ) ; N198A ( AAC/gcC ) ; D202A ( GAC/GcC ) ; D205A ( GAC/GcC ) , the EF-hand domain 2 ( D230A ( GAC/GcC ) ; N232A ( AAC/gcC ) ; E241A ( GAA/GcA ) , and the serine 163 mutations ( S163D ( TCG/gat ) and S163A ( TCG/gCG ) were done by site-directed mutagenesis following the instructions from the Agilent QuickChange Site-directed Mutagenesis kit . Trypanosome expression vector . The pLew100X-3myc has been modified in the laboratory from pLew100 [49] . BILBO1 and truncations 1 , 2 , 3 , 4 , and mutated EF-hands versions of BILBO1 were cloned into pLew100X-3myc between the HindIII-XbaI sites . Yeast two-hybrid vectors . Open reading frames were amplified by PCR from T . brucei PCF genomic DNA and cloned in the prey ( pGADT7-AD , Clontech ) and bait ( pGBKT7 , Clontech ) vectors between the EcoRI-BamHI sites . In trypanosomes . For cytoskeleton preparations , cells were washed in PBS , loaded on poly-l-lysine coated glass slides , and extracted with 1% or 0 . 25% NP40 in Pipes buffer ( 100 mM Pipes pH6 . 9 , 1 mM MgCl2 ) for 5 minutes then washed twice in Pipes buffer . Cytoskeletons were fixed in -20°C methanol or 3% paraformaldehyde ( PFA ) in PBS . After PFA fixation , cells were neutralized 10 min in glycine ( 100 mM in PBS ) . After 3 washes in PBS , samples were incubated with the primary antibodies for 1 hour at room temperature in a moist chamber: anti-BILBO1 ( mouse monoclonal 5F2B3 , which recognizes the CC domain [8] , 1:10 dilution ) or rabbit anti-NTD ( which recognises the first 110 aa of BILBO1 , [9] ) diluted 1:50 in PBS . After two PBS washes , cells were incubated for 1 hour with the secondary antibodies anti-mouse-IgG ( H+L ) conjugated to Alexa 594 ( Molecular Probes A21201 , 1:400 dilution ) or FITC ( Sigma F-2012 , 1:100 dilution ) , or anti-rabbit IgG ( H+L ) from goat conjugated to FITC ( Sigma #F-9887 , 1:100 dilution ) . The nuclei and kinetoplasts were labeled with DAPI ( 10 μg . mL-1 in PBS for 5 minutes ) , washed twice in PBS for 5 minutes . Slides were mounted with Slowfade Gold ( Molecular Probes S-36936 ) . In U-2 OS cells . For observation of whole cells , transfected U-2 OS cells were fixed in 3% PFA in PBS for 15 minutes ( at RT or at 37°C ) . When indicated , transfected cells were incubated with the membrane permeable calcium chelator BAPTA-AM ( Sigma A1076 , 25μg/ml final concentration ) for three hours or with the proteasome inhibitor MG132 ( Sigma C2211 , 20–50 μM final concentration ) for six hours before fixation . To remove soluble proteins , cells were briefly extracted for 2 min with 30 μl of EMT , TX-100 0 . 5% , glycerol 10% then fixed in PFA 3% ( at 37°C , 15 min ) . After fixation , cells were neutralized 10 min in glycine ( 100 mM in PBS ) . After two washes in PBS , cells were incubated in permeabilization buffer PB ( PBS , 10% foetal calf serum , 0 . 1% saponin ) for 10–30 minutes . Primary antibodies anti-BILBO1 ( mouse monoclonal 5F2B3 ) , [8] 1:10 dilution , anti-NTD BILBO1 ( which binds to aa 1–110 , rabbit polyclonal , 1:50 dilution , [9] ) , anti-alpha-tubulin DM1A ( Sigma T9026 , 1:500 dilution ) or TAT1 ( 1:100 dilution , [53] ) , anti-calnexin ( rabbit polyclonal , 1:500 dilution ) , anti-giantin ( rabbit polyclonal , 1:750 dilution ) , anti-Vimentin V9 ( Interchim NB200-622 , 1:250 dilution ) were added and the slides were incubated for 1 hour in a dark , moist chamber . After two PBS washes , cells were incubated for 1 hour with the secondary antibodies anti-mouse-IgG ( H+L ) conjugated to Alexa-594 ( Molecular Probes A21201 , 1:400 dilution ) , or to FITC ( Sigma F-2012 , 1:100–1:400 dilution ) , or to anti-rabbit Texas-Red-conjugated ( Molecular Probes T-6391 , 1:400 dilution ) , or to anti-rabbit FITC-conjugated ( Sigma F-9887 , 1:100–1:400 dilution ) . For the F-actin labelling , Texas-red-conjugated phalloidin ( Molecular Probes A12380 , 1:160 dilution ) was incubated with the secondary antibody . The nuclei were stained with DAPI ( 0 . 25 μg . mL-1 in PBS for 5 minutes ) and cells were washed and mounted with Prolong ( Molecular Probes S-36930 ) . Images were acquired on a Zeiss Axioplan2 or a Zeiss Imager Z1 microscope , using a Photometrics Coolsnap HQ2 camera , with Zeiss 100x or 63x objectives ( NA 1 . 4 ) using Metamorph software ( Molecular Devices ) , and processed with ImageJ . Polymer dimensions were measured using ImageJ . Total fluorescence intensities in U-2 OS cells were quantified from Z-stack acquisitions and using ImageJ on SUM intensity Z project , after background subtraction , and selection of each cell as region of interest . The measurement of polymers was done using fluorescence or immunofluorescence based images . The dimensions were measured from at least three separate experiments and were measured by hand using Image J software . Protein expression and purification . TbBILBO1 wild-type and mutated EF-hands proteins ( residues 177–250; WT , mEFH1 , mEFH2 , and mEFH1+2 ) , were cloned into the custom vector MalpET as described previously [15] . All recombinant proteins , each carrying an N-terminal MBP-His10 tag , were expressed in E . coli BL21 ( DE3 ) . Bacteria transformed with the cloned constructs were grown at 37°C to an A600 of ∼0 . 6–0 . 8 and then subjected to cold shock ( ice , 30 min ) . Protein expression was induced by addition of 0 . 25 mM isopropylthio-β-D-galactoside , and protein production was continued for 20–22 hours at 16°C . Cells were harvested by centrifugation ( 4 , 000×g , 20 min ) and resuspended in cold lysis buffer ( 20 mM Tris-HCl pH 8 . 0 , 300 mM NaCl , 20 mM imidazole , 5% ( v/v ) glycerol ) . The cells were broken open with an EmulsiFlex-C3 homogenizer ( Avestin ) and the lysate was cleared by centrifugation ( 16 , 000×g , 45 min; 4°C ) to remove cell debris . The supernatant was filtered ( 0 . 45-μm pore size ) and loaded onto a Ni-HiTrap column ( GE Healthcare ) pre-equilibrated with the same lysis buffer in order to capture the expressed proteins . The column was washed with 5 × column volume of lysis buffer , and bound protein was eluted by a linear gradient concentration of imidazole ( 20–600 mM , 10× column volume ) in the lysis buffer . Target proteins were further purified on a Superdex S-200 16/60 column ( GE Healthcare ) pre-equilibrated with 20 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 5mM DTT and 5% ( v/v ) glycerol . Fractions containing target proteins were pooled and concentrated according to requirements for subsequent experiments . Isothermal titration calorimetry ( ITC ) . For all ITC experiments , ultrapure water ( milli-Q apparatus , Millipore ) was used . All plastic materials were washed with 1 mM EDTA ( pH 8 . 0 ) and then rinsed with milli-Q water to minimize Ca2+ contamination . ITC measurements were carried out using an iTC200 microcalorimeter ( MicroCal ) at 25°C in ITC buffer ( 20 mM Tris-HCl pH 8 . 0 , 100 mM NaCl ) . Potentially pre-bound calcium was removed from the TbBILBO1-EFh by incubating the protein with 50 mM EDTA ( pH 8 . 0 ) for 1 hour at RT . EDTA was subsequently removed by dialyzing the protein sample against 3L of ITC buffer 5 times over 36 hours at 4°C . Before each ITC experiment , the sample cell of the microcalorimeter was washed several times with 1 mM EDTA ( pH 8 . 0 ) and then rinsed with milli-Q water . The sample cell was loaded with 200 μl of 30μM protein in ITC buffer . The reference cell contained only milli-Q water . Titration was carried out using a 40-μl syringe filled with 600μM CaCl2 prepared in ITC buffer under continuous stirring at 1 , 000 × rpm . Injections were started after baseline stabilization . Each titration experiment consisted of an initial 0 . 4-μl injection followed by 19 consecutive injections of 2 μl each with duration of 0 . 8 s . The interval between each two injections was 150 seconds . The heat of dilution was measured by injecting CaCl2 into the sample buffer without protein . The enthalpy change for each injection was calculated by integrating the area under the peaks for the recorded time course of power change , and then subtracting the control titration . Data were analyzed using the MicroCal Origin software and fitted to obtain thermodynamic parameters of calcium binding to the protein using a model with one set of sites . Electron microscopy . Transfected U-2OS cells were harvested by scraping and then pelleted at 800 x g for 10 min at room temperature . They were then resuspended in 250μL PBS ( plus protease inhibitors ) , for 30 min at 4°C to depolymerize the sub-pellicular microtubules . 10μL of cell suspension was placed on freshly charged formvar/carbon coated G200 nickel electron EM grids at ( 4°C ) . After the cells had adhered the grids were inverted onto extraction buffer ( 500μL PBS , 1% Nonidet P40 plus benzonase and protease inhibitors ) and extracted for 15 minutes in at R/T . Grids were then washed ( 1 x 5 minutes ) by floating on 500μL PBS and fixed 5 minutes in 500μL of 2 . 5% glutaraldehyde in PBS . Grids were then washed in 500μL water , 1 x 5 minutes and negatively stained with a 10μL drop/grid of 50:50 mix of NanoVan:NanoW . For striation measurements , digital images were taken from grids of at least three different experiments . Filaments were measured and striations were counted by hand on all filaments identified using Image J software . The pGADT7-AD ( prey ) and pGBKT7 ( bait ) based plasmid constructs were transformed in the yeast cell lines Y187 and Y2HGold respectively . After production of diploids cells , interaction tests were done using the drop test technique according to the manufacturer’s instructions ( Matchmaker Gold Yeast Two-Hybrid System , Clontech ) . Haploid and diploid strains were grown in SC medium ( YNB ( w/o ammonium sulfate 1 . 7 g . L-1 ( BD , #233520 ) , Ammonium sulfate 5 g . L-1 ( Euromedex , #2019 ) , CSM ( -Leu , -His , -Trp , -Ade , -Ura ) 0 . 59 g . L-1 ( MP , #4550–122 ) , Dextrose ( D+Glucose ) 0 . 59 g . L-1 ( Euromedex , #UG3050 ) , Uracil ( 0 . 02 g . L-1 ) and complemented with Leucine ( 1 g . L-1 ) , Tryptophan ( 0 . 05 g . L-1 ) , Histidine ( 0 . 02 g . L-1 ) , or Adenine ( 0 . 04 g . L-1 ) as required . Absence of auto-activation for each pGBKT7 bait construct was tested on SC-Tryptophan-Histidine medium . Absence of toxicity for each pGADT7-AD and pGBKT7 construct was tested on SC-Leucine and SC-Tryptophan respectively . Diploid yeasts were selected on SC-Leucine-Tryptophan medium ( SC-L-W ) . Interaction tests were done on SC-L-W-Histidine media . All interactions were tested in both prey and bait configuration . Interaction using T2 as bait could not be tested because of auto-activation on SC-W-H medium . The two EF-hand domains were predicted by InterProScan [54] and Smart [55] software , and the CC domain by the Coils software [56] . Trypanosome cells . 2 . 5 . 107 non-induced and induced cells ( six or 24 hours ) PCF were split in two flasks for whole cells ( WC ) and cytoskeleton ( CK ) samples . For WC samples , cells were spun at 1 , 000 x g for 10 minutes , washed once and resuspended at 1 . 106 cells/μL-1 in PBS . An equivalent volume of 2x sample buffer and 25U of benzonase ( Sigma , E1014 ) was added before boiling 3 minutes . For CK samples , cells were spun at 1 , 000 g for 10 minutes and washed once in PBS , EDTA 10mM and resuspended at 1 . 106 cells/μL-1 in 100 mM PIPES pH6 . 8 , 2 mM MgCl2 , 0 . 25% NP-40 , Protease inhibitor ( Calbiochem , 1:10 , 000 dilution ) and 25U of benzonase . After 10 minutes incubation on ice , cytoskeletons were pelleted at 1 , 000 x g for 30 minutes then washed in 1 mL 100 mM PIPES pH6 . 8 , 2 mM MgCl2 and resuspended in the same buffer ( 1 . 106 cells/μL-1 final ) . An equivalent volume of 2x sample buffer was added before boiling for 3 minutes . 2 . 106 cells ( or cytoskeleton ) were loaded on 10 or 12% SDS-PAGE , semi-dry transferred on PVDF membrane or nitrocellulose membrane . U-2 OS cells . Exponentially growing U-2 OS cells in T-25 flask were lipotransfected with 12 . 5 μg DNA using Lipofectamine 2000 in OPTIMEM ( Invitrogen ) according to the manufacturer’s instructions and processed for western-blot for 6 hours post-transfection ( or 24 hours post-transfection for the mEFH1+2 sample ) . Cells were collected by; scraping the bottom of the respective culture flasks and transferring the detached cells into ice cold PBS . Cells were then centrifuged 5 min , 1 , 000 x g and resuspended in 125μL of buffer ( PIPES 60 mM , HEPES 25 mM , EGTA 10 mM , MgCl2 10 mM adjusted to pH6 . 9 with KOH , glycerol 10% , protease inhibitors ( Calbiochem Cocktail set III , 1:10 , 000 dilution and 1mM PMSF ) then lysed by adding 125μL of buffer supplemented with 0 . 2% TX-100 . The supernatant was collected after a 5 minute centrifugation at 1 , 500 x g and 62 . 5μL of sample buffer 4x was added . Boiling 5 for minutes denatured the sample and then benzonase ( 5U ) was added . The pellet was resuspended in 250 μL of buffer with 0 . 1% TX-100 , 62 . 5μL sample buffer 4x was added and the sample was denatured by boiling for 5 minute before adding 7 . 5U of benzonase . Protein concentrations were assayed using the Pierce 660 nm Protein Assay ( #22660 ) with the ionic detergent compatibility reagent ( #22663 ) kit according to the manufacturer’s instructions . 13–20μg of protein from supernatant samples , and a corresponding volume of pellet samples , were separated by SDS-PAGE ( 10% ) and semi-dry transferred onto PVDF membrane . Membranes were blocked in Tris-buffered saline ( TBS ) , 0 . 2% Tween-20 , 5% skimmed milk powder for 1 hour then incubated overnight at 4°C with the primary antibodies diluted in blocking solution: rabbit polyclonal anti-NTD diluted at 1:200 , mouse monoclonal 5F2B3 undiluted , anti-myc monoclonal 9E10 ( A kind gift from K . Ersfeld , University of Bayreuth , Germany ) at 1:200 , anti-alpha-Tubulin TAT1 monoclonal antibody ( a kind gift from K . Gull , Sir William Dunn School of Pathology , University of Oxford , England , U . K ) at 1:500 . After 3 washes ( 10 min ) in TBS , 0 . 2% Tween-20 , 1M NaCl , the membranes were incubated for 1 hour at room temperature with secondary antibodies diluted in blocking solution: anti-mouse HRP conjugated antibody ( Jackson 115-055-068 , 1:10 , 000 ) , ECL Plex anti-mouse Cy3 conjugated ( GE Healthcare #PA43009V , 1:2 , 500 ) , ECL Plex anti-rabbit Cy5 conjugated ( GE Healthcare #PA45011V , 1:2 , 500 ) . After washes in blocking solution , in TBS , 0 . 2% Tween-20 then TBS , membranes were revealed by ECL ( Clarity Biorad chemiluminescence kit # 170–5061 ) according to the manufacturer’s instructions ) or direct fluorescence detection on a LAS4010 ( GE Healthcare #28-9558-11 ) with R670 Cy5 filter , 575DF20 Cy3 filter , according to the manufacturer’s instructions . T . brucei brucei PCF 427 29–13 whole cell extracts was run on a 12% SDS-PAGE and stained with colloidal blue . After several H2O washes , a 60–80KDa band was excised and trypsin digested before LC-MS/MS analysis . Using Discoverer 1 . 3 ( PhosphoRS module ) , one phosphorylation was identified on serine 163 ( HAsFHGSTSNALVPR ) .
|
Trypanosoma brucei avoids destruction by , in part , changing its surface glycoprotein coat , which is trafficked onto the cell surface via an invagination of the cell surface called the flagellar pocket . The pocket is essential for pathogenicity . The distal membrane of the pocket is anchored to a cytoskeleton structure called the flagellar pocket collar ( FPC ) . The FPC is a ring/horseshoe shaped structure , which itself is attached to the single copy flagellum of the parasite . How the “ring” shape of the collar is formed is not understood . Moreover , the only known protein component of the FPC is the protein BILBO1 . BILBO1 is modular and has a distinct N-terminal domain , two EF-hand calcium-binding domains and a large C-terminal coiled-coil domain . Here we demonstrate that mutating the EF hand domains prevent calcium binding and that the coiled-coil domain is not only sufficient to target to the collar , but can also form polymers in mammalian cells . Mutating either or both calcium-binding domains of BILBO1 influences polymer formation and type when expressed in mammalian and trypanosome cells . Our premise is that BILBO1 has intrinsic polymer forming properties that are essential for the flagellar pocket collar making the pocket a target for intervention .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
BILBO1 Is a Scaffold Protein of the Flagellar Pocket Collar in the Pathogen Trypanosoma brucei
|
Ribosomal RNA synthesis is controlled by nutrient signaling through the mechanistic target of rapamycin complex 1 ( mTORC1 ) pathway . mTORC1 regulates ribosomal RNA expression by affecting RNA Polymerase I ( Pol I ) -dependent transcription of the ribosomal DNA ( rDNA ) but the mechanisms involved remain obscure . This study provides evidence that the Ccr4-Not complex , which regulates RNA Polymerase II ( Pol II ) transcription , also functions downstream of mTORC1 to control Pol I activity . Ccr4-Not localizes to the rDNA and physically associates with the Pol I holoenzyme while Ccr4-Not disruption perturbs rDNA binding of multiple Pol I transcriptional regulators including core factor , the high mobility group protein Hmo1 , and the SSU processome . Under nutrient rich conditions , Ccr4-Not suppresses Pol I initiation by regulating interactions with the essential transcription factor Rrn3 . Additionally , Ccr4-Not disruption prevents reduced Pol I transcription when mTORC1 is inhibited suggesting Ccr4-Not bridges mTORC1 signaling with Pol I regulation . Analysis of the non-essential Pol I subunits demonstrated that the A34 . 5 subunit promotes , while the A12 . 2 and A14 subunits repress , Ccr4-Not interactions with Pol I . Furthermore , ccr4Δ is synthetically sick when paired with rpa12Δ and the double mutant has enhanced sensitivity to transcription elongation inhibition suggesting that Ccr4-Not functions to promote Pol I elongation . Intriguingly , while low concentrations of mTORC1 inhibitors completely inhibit growth of ccr4Δ , a ccr4Δ rpa12Δ rescues this growth defect suggesting that the sensitivity of Ccr4-Not mutants to mTORC1 inhibition is at least partially due to Pol I deregulation . Collectively , these data demonstrate a novel role for Ccr4-Not in Pol I transcriptional regulation that is required for bridging mTORC1 signaling to ribosomal RNA synthesis .
Eukaryotic cells alter gene expression programs in response to changes in their environment , including nutrient availability and the presence of stress , by transmitting this information through nutrient-responsive signaling cascades to the transcriptional machinery [1] . This process is critically important for regulating rDNA transcription and ribosomal RNA ( rRNA ) biogenesis . Over 60% of cellular transcription in rapidly growing cells is mediated by RNA polymerase I ( Pol I ) , the sole RNA polymerase responsible for the production of three ( the 18S , 5 . 8S , and 25S in budding yeast ) of the four rRNAs [2] . Transcription of the 5S rRNA , tRNAs and specific snRNA and snoRNAs is mediated by RNA polymerase III ( Pol III ) , while RNA polymerase II ( Pol II ) transcribes all ribosomal protein ( RP ) genes and the ribosome biogenesis ( Ribi ) genes coding for the ancillary factors necessary to produce and assemble ribosomes [3] . Coordinating ribosomal transcription by these three distinct polymerases to produce ribosomal components in the appropriate stochiometries , and in proportion to nutrient availability , is critical . Dysregulation of this process may result in the formation of partial or non-functional ribosomes that could have deleterious effects on cell fitness . Promoting ribosomal biogenesis in nutrient poor environments may also suppress the ability of cells to enter into survival states , such as autophagy , which could reduce viability [3] . The yeast rDNA exists as a multicopy array on chromosome XII with the individual 35S and 5S rRNA genes organized such that they are divergently transcribed and separated by non-transcribed sequences with only approximately half of the ~100–200 rDNA repeats expressed in a given cell [3] . The 35S rDNA is transcribed by Pol I as a polycistronic RNA transcript consisting of the 5´ external transcribed sequence ( ETS1 ) , the 18S , the internally transcribed sequence 1 ( ITS1 ) , the 5 . 8S , the internally transcribed sequence 2 ( ITS2 ) , the 25S , and the 3´ external transcribed sequence ( ETS2 ) [3 , 4] . This polycistronic RNA is co-transcriptionally and post-transcriptionally processed through complex endonucleolytic and exonucleolytic mechanisms to form the mature 18S , 5 . 8S and 25S rRNAs that assemble with ribosomal proteins to form functional ribosomes [3 , 5 , 6] . The factors necessary to achieve Pol I-specific transcription initiation at the rDNA promoter have been extensively characterized in yeast . These factors include the UAF complex ( consisting of Rrn5 , Rrn9 , Rrn10 , Uaf30 , and histones H3 and H4 ) , TATA-binding protein ( TBP ) , and core factor ( composed of Rrn6 , Rrn7 , and Rrn11 ) ( reviewed in [4] ) . UAF , TBP , and core factor bind within the rDNA promoter to nucleate Pol I recruitment and initiate transcription . Additionally , the high mobility group ( HMG ) protein , Hmo1 , binds to both the promoter and the rDNA gene body to create a specialized chromatin state devoid of canonical nucleosomal structure that facilitates Pol I transcription [7–9] . Another essential Pol I regulator is the transcription factor Rrn3 ( TIF-IA/Rrn3 in mammals ) which is one of the few Pol I transcriptional regulators conserved at the amino acid level from yeast to mammals [10 , 11] . Rrn3 interacts with phosphorylated Pol I holoenzyme , via the A43 subunit , to form transcriptionally-competent Pol I initiation complexes [12–14] . Approximately 2% of Pol I is associated with Rrn3 to form initiation complexes suggesting that control of Rrn3-Pol I interactions is rate-limiting for Pol I transcription initiation [13] . Rrn3-Pol I interactions occur specifically at the rDNA promoter where Rrn3 directly contacts the Rrn6 subunit of core factor , and as Pol I begins to elongate it dissociates from Rrn3 [14 , 15] . A particularly important signaling input controlling Rrn3-Pol I dynamics is relayed through mTORC1 which is crucial for coordinating Pol I transcription with environmental nutrient status [16 , 17] . The pharmacological agent rapamycin selectively inhibits mTORC1 to suppress anabolic and cell proliferative processes , including rRNA synthesis . In mammals , rapamycin treatment induces hypophosphorylation of Rrn3 serine 44 and hyperphosphorylation of serine 199 to block Rrn3-Pol I complex formation and rapidly decrease rRNA synthesis [18] . While yeast Rrn3 shares conserved serine residues with mammalian Rrn3 that are important for Rrn3 function , whether mTORC1-regulated phosphorylation of these serines occurs in vivo is unclear [19] . Recent studies in yeast have demonstrated that decreased mTORC1 signaling disrupts Rrn3-Pol I interactions through an alternative mechanism that involves reduced RRN3 mRNA translation [16] . Decreased translation rapidly lowers Rrn3 protein expression since Rrn3 is constitutively ubiquitylated and degraded via the proteasome [16] . Although RRN3 is an essential gene required for Pol I transcription , this requirement can be bypassed if a chimera construct expressing Rrn3 fused to the A43 Pol I subunit is substituted [20] . Cells expressing Rrn3-A43 fusions grow normally in nutrient rich media; however , they are hypersensitive to mTORC1 inhibition [20 , 21] . These studies highlight the critical importance for appropriate mTORC1-dependent Pol I regulation when cells are exposed to conditions of nutrient stress . While Pol I transcription initiation mechanisms are well defined , those affecting Pol I elongation remain are unclear . Studies have demonstrated that integral components of the Pol I holoenzyme , including the A49 , A34 . 5 , A12 . 2 , and A14 subunits ( all of which are non-essential genes in yeast ) , are linked to Pol I transcription elongation [22–25] . Mutations within Pol I that affect transcription elongation also impair rRNA processing and lead to the generation of aberrant rRNA intermediates [26] . The mechanisms by which altered elongation causes these rRNA processing defects are not currently understood , but they are likely to be complex since ~70% of nascent rRNA undergoes significant co-transcriptional processing [6 , 27] . In particular , the small subunit ( SSU ) processome complex binds the rDNA to both stimulate Pol I transcription and co-transcriptionally cleave the nascent rRNA within ITS1 yet how the elongating Pol I complex interacts with SSU processome to properly process the nascent transcript is not understood [5 , 6] . Control of Pol II elongation is well-defined and a number of accessory factors act in this process by either stimulating the intrinsic elongation activity of Pol II or providing chromatin-modifying activities necessary for Pol II to transcribe through chromatin [28] . Recent studies have demonstrated many of these Pol II elongation factors also regulate Pol I elongation , yet their importance to rRNA biogenesis is only now being elucidated . Examples of such factors include the PAF complex , Spt4 and Spt5 , the FACT histone chaperone , the chromatin remodeling enzymes SWI/SNF and Chd1 , and the proteasome [29–34] . The Ccr4-Not complex is composed of nine distinct subunits , including the Ccr4 mRNA deadenylase , Caf1 , Caf40 , Caf130 , and Not1-5 , and has both cytoplasmic and nuclear functions important for cell growth and proliferation . In the cytoplasm , Ccr4-Not acts to degrade mRNAs via the Ccr4 subunit which is the main deadenylase activity in yeast [35] . In addition to this intrinsic deadenylase activity , the Not4 subunit contains a functional RING domain that utilizes either the Ubc4 or Ubc5 E2 enzymes to mediate ubiquitin transfer to a small number of substrates [35] . Not4-dependent substrate ubiquitylation results in either proteasome-dependent degradation or altered substrate function [36–38] . Ccr4-Not also interacts with the proteasome and Not4 loss causes defects in both proteasome integrity and function . These defects lead to accumulation of polyubiquitylated proteins and decreased free ubiquitin which is a phenotype not shared with ccr4Δ [38 , 39] . Nuclear Ccr4-Not regulates Pol II transcription at both the initiation and elongation phases of the transcription cycle and is functionally connected to the mRNA export pathway as well [35 , 40] . How Ccr4-Not regulates Pol II transcription is only now beginning to be defined , particularly for the elongation phase of the transcription cycle . When Pol II transcription elongation reactions are assembled in vitro , Ccr4-Not directly stimulates stalled Pol II elongation complexes suggesting it acts directly to regulate Pol II elongation [40] . Furthermore , Ccr4-Not mutants have Pol II elongation defects in vivo [40–42] . Whether Ccr4-Not also contributes to transcriptional regulation by other RNA polymerases has not been addressed . In this study , we provide evidence that supports a novel role for Ccr4-Not as a Pol I transcriptional regulator that controls both Pol I initiation and elongation . Furthermore , we demonstrate that a functional Ccr4-Not is required to decrease Pol I transcription upon mTORC1 inhibition , suggesting that this complex integrates upstream nutrient signaling through mTORC1 to downstream control of rRNA biogenesis .
Specific Ccr4-Not mutants result in significant growth defects which prompted us to ask whether Ccr4-Not might contribute to Pol I transcription since rRNA biogenesis is rate limiting for cell growth and proliferation [35] . Utilizing yeast strains carrying C-terminal TAP epitope tags integrated at either the CCR4 , NOT1 , or NOT2 genomic loci , we cultured cells in nutrient rich media ( YPD ) and performed chromatin immunoprecipitation ( ChIP ) coupled with quantitative PCR utilizing primers to the rDNA locus as outlined ( Fig . 1A ) . All three Ccr4-Not subunits were significantly enriched above background suggesting that Ccr4-Not physically localizes to rDNA repeats ( Fig . 1B ) [43] . Next , we engineered strains to express either Rpa190-6XHA ( Pol I catalytic subunit ) , Ccr4-13XMyc , or both , and performed co-immunoprecipitation ( co-IP ) experiments to determine if Ccr4-Not associates with Pol I . Precipitation of Pol I readily co-precipitated Ccr4 , suggesting these complexes physically interact ( Fig . 1C ) . Collectively , these data are consistent with the possibility that Ccr4-Not directly regulates Pol I transcription . To address the role of Ccr4-Not in Pol I transcription , we performed Pol I-specific ChIP experiments in wild-type and ccr4Δ cells grown to log phase in YPD . Surprisingly , under these conditions we detected significantly elevated levels of Pol I throughout the rDNA suggesting Ccr4-Not normally acts to limit Pol I rDNA binding ( Fig . 2A ) . Deficiencies in Pol I regulators can also influence rDNA copy number so we next determined whether ccr4Δ resulted in significant changes to rDNA copy number . Utilizing our ChIP samples , and ChIP samples from yeast strains containing either 42 rDNA copies ( 42C ) or 143 copies ( 143C ) [44] , we performed quantitative PCR with primers to the 18S and also to the essential single-copy gene , SPT15 , to generate an 18S/SPT15 ratio . As expected , analysis of the 42C strain gave a low ratio whereas the analysis of the 143C strain resulted in a higher ratio ( Fig . 2B ) . Interestingly , the ccr4Δ resulted in an 18S/SPT15 ratio higher than that detected in wild-type and the 143C strain ( Fig . 2B ) . Because our Pol I ChIP signals are normalized to input DNA , this increase in rDNA copy number is accounted for in the analysis and thus cannot explain the increased Pol I ChIP signal . Therefore , these data suggest that ccr4Δ causes increased Pol I transcription as well as an increase in rDNA copy number . The increased Pol I bound to the rDNA suggested the possibility that ccr4 Δ increases overall rRNA transcription . Nascent rRNA synthesis is typically measured through incorporation of radiolabelled methionine or uridine via pulse-chase experiments of cells grown in nutrient defined media . However , both Pol I transcriptional activity , as well as the biological functions of Ccr4-Not , are downstream targets of nutrient regulated signaling pathways that may be differentially affected by growth under nutrient rich versus nutrient defined conditions [17 , 45 , 46] . Therefore , measuring rRNA synthesis via this approach could mask biological effects that occurring during growth in nutrient rich conditions ( see below ) . Therefore , we elected to quantify differences in nascent rRNA levels by measuring the expression of ETS1 and ITS1 . These sequences exist transiently in the nascent rRNA since ~70% of nascent rRNA is co-transcriptionally processed in these regions , and their presence has been utilized previously as a general proxy for Pol I transcriptional activity [27 , 47 , 48] . Initially , we confirmed that we could detect significant alterations in nascent rRNA levels via qPCR by synthesizing randomly-primed cDNA from total RNA isolated from wild-type and rpa49 Δ mutant cells . These samples were analyzed with primers specific to either ETS1 or ITS1 and normalized to the expression of the SPT15 housekeeping gene . RPA49 encodes the non-essential Pol I A49 subunit whose loss significantly impairs Pol I transcription [25] . Under these conditions , significant reductions in both ETS1 and ITS1 were detected in rpa49 Δ compared to wild-type ( Fig . 2C ) suggesting this approach could be utilized to detect significant perturbations in nascent rRNA synthesis for cells grown under nutrient rich conditions . Utilizing this approach , we determined the effect that loss of either enzymatic activity associated with Ccr4-Not , specifically the Ccr4 deadenylase or Not4 ubiquitin ligase subunits , had on rRNA synthesis . Intriguingly , both ccr4 Δ and not4 Δ resulted in elevated expression of ETS1 and ITS1 relative to wild-type ( Fig . 2D ) . Additionally , the not4 Δ also exhibited a subtle , but reproducibly significant , increase in 18S and 25S rRNA levels as well ( Fig . 2D ) . Since this approach does not delineate between differences in nascent 18S and 25S rRNA synthesis versus changes in their overall stability , how not4 Δ increases 18S and 25S rRNA expression is currently unknown . Defects in Pol I transcription elongation generate aberrantly processed rRNA intermediates that are subsequently cleared through the collective actions of both the TRAMP polyadenylation complex and the nuclear exosome . As a consequence , mutations in TRAMP or exosome can result in accumulation of these non-processed rRNA intermediates [26 , 49] . Ccr4-Not interacts with both TRAMP and the exosome and has been suggested to regulate the functions of both complexes [50] . Therefore , the elevated ETS1 and ITS1 expression in ccr4 Δ or not4 Δ could be explained either by increased Pol I transcription or by the enhanced stability of these nascent sequences due to TRAMP and/or exosome defects . To rule out this latter possibility , we analyzed ETS1 and ITS1 expression from wild-type , trf4 Δ ( a TRAMP mutant ) , and rrp6 Δ ( nuclear exosome mutant ) backgrounds . Additionally , we also examined ETS1 and ITS1 expression in a catalytic mutant ( pan2 Δ ) of the Pan2-Pan3 complex which is an additional mRNA deadenylase complex that is partially redundant with Ccr4-Not [51] . None of these mutants significantly increased expression of either ETS1 or ITS1 compared to wild-type ( Fig . 2E ) . These data demonstrate that the effect Ccr4-Not disruption has on rRNA synthesis is specific to Ccr4-Not and is not due to a generalized disruption in rRNA processing . Instead , when combined with the ChIP data from above , the results are most consistent with Ccr4-Not mutation causing an increase in Pol I-dependent rRNA synthesis . The expression of 5S rRNA , which is transcribed by Pol III , was next assessed to determine the extent of the role Ccr4-Not has in rRNA biogenesis . Although ccr4 Δ did not affect 5S expression , not4Δ severely impaired 5S rRNA levels ( Fig . 2F ) . Collectively , these data implicate Ccr4-Not as a negative regulator of 35S synthesis while the Not4 subunit appears to also have a positive role in 5S rRNA expression . As not4 Δ is considerably more growth impaired than ccr4Δ and has additional effects that might confound our analysis , we chose to focus predominantly on the role by which the Ccr4 subunit contributes to Pol I transcription . To further understand the mechanism by which ccr4Δ causes increased Pol I transcription , we next examined components of the Pol I transcription initiation machinery bound at the promoter . The UAF complex stably binds both transcriptionally active and inactive rDNA promoters and serves to facilitate high level Pol I transcription initiation [4 , 17 , 52] . We epitope tagged the RRN5 ( UAF subunit ) locus with wild-type and ccr4Δ cells and then monitored Rrn5 promoter association by ChIP . Rrn5 binding was not significantly increased in ccr4Δ suggesting that Ccr4-Not disruption did not augment Pol I transcription by elevating UAF promoter interactions ( Fig . 3A ) . Next , we genomically epitope-tagged the RRN6 ( core factor subunit ) genomic locus in wild-type and ccr4Δ cells and monitored Rrn6 binding . In wild-type cells , we detected a modest , but statistically significant , enrichment of Rrn6 over background which is likely due to the very low expression levels of core factor in cells ( Fig . 3B ) [53] . Intriguingly , the ccr4Δ resulted in enhanced Rrn6 enrichment suggesting that ccr4Δ either increased core factor binding or that Rrn6 was made more accessible for immunoprecipitation in this mutant . This same experimental approach was then applied to the analysis of two additional Pol I transcriptional regulators , specifically the high mobility group protein Hmo1 and the SSU processome complex via the Utp9 subunit . These experiments demonstrated that ccr4Δ caused a modest decrease in Hmo1 binding that was restricted solely to the promoter ( Fig . 3C ) whereas the SSU processome was reduced throughout the transcribed rDNA ( Fig . 3D ) . Since the SSU processome mediates co-transcriptional cleavage of the nascent rRNA within ITS1 , reduced rDNA binding by this complex likely contributes to the increase in ITS1 levels identified in Ccr4-Not mutants ( Fig . 2D ) [5 , 6] . The differences in rDNA binding by these Pol I regulators cannot be explained by changes in their expression as ccr4Δ does not alter the protein levels of Rrn5 , Rrn6 or Utp9 and it modestly increases expression of Hmo1 ( Fig . 3E ) . Overall , these data demonstrate that ccr4Δ selectively affects Pol I transcriptional regulators and that the increase in core factor enrichment correlates well with the elevated Pol I transcription occurring in ccr4Δ . Pol I interaction with the transcription factor Rrn3 is essential for forming transcriptionally competent Pol I initiation complexes [12–14] . The Rrn6 subunit of core factor also contacts Rrn3 which contributes to Pol I transcription initiation by facilitating promoter recruitment of the Rrn3-Pol I initiation complex [14] . We next assessed if the increased Pol I transcription in ccr4Δ could be explained by changes in Rrn3-Pol I interactions . Wild-type and ccr4Δ cells expressing endogenous , genomically-tagged Rrn3-9XMyc and Rpa190-6XHA were grown in nutrient rich media and Rrn3 ChIP was performed . No difference in Rrn3 promoter binding was detected in ccr4Δ cells , demonstrating that Ccr4-Not disruption does not increase Rrn3 promoter association ( Fig . 4A ) . However , the amount of Rrn3 expressed in ccr4Δ , as well as the amount of Rrn3-Pol I complexes formed , was significantly enhanced in ccr4Δ ( Fig . 4B ) . Examination of mRNA expression for RRN3 , as well as RRN5 and HMO1 , identified a selective , approximately 2 . 5-fold increase in RRN3 expression in ccr4Δ ( Fig . 4C ) . These data suggest that Ccr4-Not regulates either RRN3 transcription or RRN3 mRNA stability to limit the total amount of Rrn3 available to form transcriptionally competent Rrn3-Pol I complexes . To further address the role of Ccr4-Not in Rrn3-Pol I complex formation , we transformed ccr4Δ cells with control vector or vectors overexpressing C-terminally FLAG-tagged wild-type CCR4 or catalytically inactive mutant ccr4-1 . We confirmed that expression of wild-type , but not mutant , Ccr4 rescued growth on caffeine since ccr4Δ cells are caffeine sensitive ( Fig . 4D ) [54] . Next , we cultured cells in nutrient defined ( SC ) media lacking uracil to select for plasmid maintenance and prepared whole cell extracts for α-Pol I precipitation . Surprisingly , and in contrast to what was detected when cells were grown in nutrient rich YPD , the Pol I precipitated under these conditions co-precipitated equivalent amounts of Rrn3 from cells transformed with control vector or vectors expressing wild-type or mutant Ccr4 ( Fig . 4E ) . Rrn3 protein expression was comparable between these strains as well ( Fig . 4E ) . This result was surprising since ccr4Δ cells cultured in YPD consistently expressed more Rrn3 protein and had greater Rrn3-Pol I complex formation ( Fig . 4B ) . Because this experiment was performed using vector-transformed cells grown in SC-uracil media to maintain plasmid selection , we speculated that the nutrient environment might alter the effect ccr4Δ has on Pol I transcription . To test this possibility , we cultured wild-type and ccr4Δ in complete SC media and analyzed ETS1 and ITS1 expression . Under these conditions , ccr4Δ did not increase rRNA expression ( Fig . 4F ) which contrasts with the result from cells cultured in YPD ( compare Fig . 4F and Fig . 2D ) . These data demonstrate that under defined , nutrient limiting conditions loss of Ccr4-Not function neither increases Rrn3-Pol I complex formation nor elevates rRNA synthesis . Rrn3 is constitutively ubiquitylated and degraded via the proteasome [16] . A not4Δ impairs proteasome assembly and function which results in an accumulation of polyubiquitylated proteins [38 , 39] . We speculated that in not4Δ , polyubiquitylated Rrn3 may accumulate due to dysfunctional proteasome regulation and that this stabilized Rrn3 may contribute to the increased rRNA synthesis detected in the not4Δ background ( Fig . 2D ) . To test this possibility , we integrated a 1XMyc/7X-histidine tag at the RRN3 genomic locus in wild-type and not4Δ cells . Duplicate wild-type and not4Δ cultures , as well as the corresponding no tag control , were grown to log phase in YPD , lysed in denaturing buffer , and the Rrn3 from these samples was purified using nickel-conjugated agarose resin . The purified Rrn3 was then either immunoblotted with α-ubiquitin or α-Myc . In these experiments , the not4 Δ caused a notable increase in total cellular polyubiquitylated proteins relative to wild-type cells which is consistent with previous reports ( Fig . 4G ) [38 , 39] . In particular , not4Δ caused a pronounced increase in polyubiquitylated Rrn3 suggesting that the ubiquitylated protein was not being efficiently degraded in these cells ( Fig . 4G ) . Since Rrn3 is still ubiquitylated in not4Δ , Not4 cannot be the sole E3 ligase responsible for Rrn3 ubiquitylation and subsequent proteasome-dependent turnover . However , these data do suggest that not4Δ loss stabilizes the pool of Rrn3 indirectly by disrupting proteasome function which prevents efficient Rrn3 degradation . Taken together , these data suggest Ccr4-Not negatively regulates Rrn3-Pol I complex formation and , consequently , Pol I transcription initiation . However , this role is influenced by the cellular nutrient environment as the increase in Rrn3-Pol I complex formation and elevated rRNA synthesis caused by ccr4Δ did not occur when the mutant was grown in nutrient defined media . The differential effects on rRNA synthesis detected in ccr4Δ cells grown in YPD versus SC suggest nutrient signaling pathways may affect the role of Ccr4-Not in Pol I transcriptional control . The mTORC1 and Ras/PKA pathways are the two main signaling pathways that couple nutrient availability to cell growth control with mTORC1 specifically regulating Pol I transcription by controlling Rrn3-Pol I complex formation [1 , 16] . We initially addressed whether Ccr4-Not might participate in mTORC1-dependent Pol I regulation . Deletion of specific Ccr4-Not subunits ( ccr4Δ , caf1Δ , not4Δ and not5Δ ) causes profound rapamycin sensitivity , consistent with previous studies linking Ccr4-Not to the mTORC1 pathway ( Fig . 5A ) [55] . Next , we cultured wild-type cells in YPD in the absence or presence of an inhibitory ( 200 nM ) concentration of the specific mTORC1 inhibitor rapamycin for 30 minutes to confirm suppression of Pol I transcription . mTORC1 inhibition resulted in a significant reduction in nascent rRNA synthesis by approximately 60% which is consistent with previous studies ( Fig . 5B ) [16 , 17] . Surprisingly , under these conditions Ccr4-Not association with Pol I is not disrupted even though mTORC1 inhibition was previously demonstrated to cause Pol I nucleolar delocalization ( Fig . 5C ) [46] . To determine the consequence mTORC1 inhibition has on Rrn3-Pol I interaction in wild-type and ccr4Δ cells , this experiment was repeated and the relative amounts of Rrn3-Pol I complexes were assessed . We detected fewer Rrn3-Pol I complexes in rapamycin-treated wild-type cells compared to mock-treated controls , a result which is consistent with the reduction in Rrn3 protein that occurs under these conditions ( Fig . 5D ) [16] . Surprisingly , mTORC1 inhibition in ccr4Δ did not significantly impair Rrn3-Pol I complex formation even though overall levels of Rrn3 were modestly decreased in the mTORC1-inhibited samples ( Fig . 5D ) . More Pol I remained bound to the rDNA in ccr4Δ compared to wild-type under these conditions as well ( Fig . 5E ) . To address whether Ras/PKA signaling may affect Ccr4-Not interactions with Pol I , we overexpressed the high affinity cyclic AMP ( cAMP ) phosphodiesterase , PDE2 . Increased Pde2 expression facilitates cAMP breakdown , thus reducing PKA signaling and slowing cell growth and proliferation ( Fig . 5F ) [1] . Pol I immunoprecipitated from control and PDE2 overexpressing cell extracts retained equivalent levels of associated Ccr4 , demonstrating that Ccr4-Not interactions with Pol I are insensitive to upstream Ras/PKA activity and to conditions that result in generalized slow growth ( Fig . 5G ) . In total , these data demonstrate that Ccr4-Not remains tightly associated with Pol I even under conditions that suppress the activity of nutrient signaling pathways controlling Pol I transcription ( mTORC1 ) or regulate other aspects of cell growth and proliferation ( Ras/PKA ) . Furthermore , Ccr4-Not disruption uncouples Pol I from mTORC1 control by both maintaining Rrn3-Pol I complex formation as well as retaining Pol I bound to the rDNA when mTORC1 signaling is suppressed . The data presented above suggests Ccr4-Not functions in Pol I transcription initiation by affecting core factor function and controlling the formation of Rrn3-Pol I complexes in a nutrient-dependent fashion . However , the recruitment of Ccr4-Not along the entire rDNA , its co-association with Pol I , and the fact that Ccr4-Not directly regulates Pol II transcription elongation all suggest it may function in Pol I elongation as well . To pursue this possibility , we determined if any of the four non-essential Pol I subunits were required for Ccr4-Not to interact with Pol I by engineering wild-type and individual Pol I deletion mutants to express Ccr4-9XMyc combined with Rpa190-6XHA . Ccr4-9XMyc was then immunoprecipitated from extracts prepared from these strains grown under nutrient rich conditions . Precipitation of Ccr4 specifically co-precipitated Pol I , consistent with our previous results ( Fig . 6A and 1C ) . While deletion of the Pol I A49 subunit ( rpa49Δ ) did not alter Ccr4-Not co-association , deletion of the A34 . 5 ( rpa34Δ ) subunit reduced Ccr4-Not association to background levels ( Fig . 6A and 6B ) . These results were unexpected since A49 binds A34 . 5 to form a heterodimeric subcomplex within Pol I , and A49 loss has been reported to release A34 . 5 from the remainder of Pol I [23 , 25] . Surprisingly , deletion of either the A12 . 2 ( rpa12Δ ) or the A14 ( rpa14Δ ) subunits increased Ccr4-Not association , suggesting these subunits normally limit Ccr4-Not interactions with Pol I ( Fig . 6A and 6B ) . When Ccr4 was examined from cells lacking the A12 . 2 subunit , we also consistently detected that it migrated more slowly by SDS-PAGE relative to Ccr4 from wild-type or the other Pol I deletion mutants ( Fig . 6A ) . The cause of this slower migrating form of Ccr4 is currently unknown . To further delineate the functional relationship between Ccr4-Not and Pol I on rRNA expression , we combined a ccr4Δ with rpa12Δ and measured ETS1 and ITS1 rRNA synthesis in wild-type , the individual and double mutants cultured in nutrient rich media . Consistent with the results from Fig . 2B , ccr4Δ increased expression of both ETS1 and ITS1 , while rpa12Δ modestly reduced ITS1 expression ( Fig . 6C ) . The ccr4Δ rpa12Δ significantly reduced expression of ETS1 relative to both wild-type and ccr4Δ cells while marginally ( p = 0 . 047 , Student’s t-test ) affecting ETS1 expression compared to rpa12Δ alone . Interestingly , while the ccr4Δ rpa12Δ reduced both ETS1 and ITS1 expression relative to ccr4Δ alone , ccr4Δ rpa12Δ restored the decreased ITS1 expression caused by the rpa12Δ mutation ( Fig . 6C ) . This result is most likely explained by the decreased co-transcriptional rRNA processing caused by reduced SSU processome rDNA binding occurring in ccr4Δ cells ( Fig . 3C ) . To determine the consequences these mutations have on Pol I rDNA binding , Pol I ChIP was performed . Consistent with the effects on ETS1 and ITS1 expression , ccr4Δ resulted in significantly increased Pol I rDNA binding compared to both wild-type and rpa12Δ ( Fig . 6D ) . While the levels of rDNA bound Pol I in rpa12Δ relative to ccr4Δ rpa12Δ were not different , the double mutant significantly reduced Pol I binding compared to ccr4Δ ( Fig . 6D ) . These results are also consistent with the reduced ETS1 and ITS1 expression detected in ccr4Δ rpa12Δ compared to ccr4Δ ( Fig . 6C ) . Analysis of the 18S/SPT15 ratios from the ccr4Δ , rpa12Δ and ccr4Δ rpa12 mutants show an increase in total rDNA copy number relative to wild-type , suggesting that all three mutants destabilize the rDNA ( Fig . 6E ) . Importantly , the decrease in Pol I rDNA binding in the ccr4Δ rpa12Δ compared to ccr4Δ cannot be explained simply by a changes in rDNA copy number as the 18S/SPT15 ratios between these strains are not different . However , the 18S/SPT15 ratio is increased in ccr4Δ rpa12Δ relative to rpa12Δ alone , suggesting that impairment of both Ccr4-Not and the Pol I holoenzyme simultaneously enhances rDNA instability ( Fig . 6E ) . These data suggest that Rpa12 may normally limit Ccr4-Not association with the Pol I holoenzyme and that the absence of this Pol I subunit during transcription may be compensated for by increasing interactions between Ccr4-Not and Pol I . Although Rpa12 is genetically linked to Pol I elongation [24] , we did not detect a significant defect in Pol I distribution in the rpa12Δ background compared to wild-type ( Fig . 6D ) . As these experiments were performed under steady-state conditions , underlying defects in Pol I elongation are likely masked . Therefore , we analyzed further the impact of the ccr4Δ , rpa12Δ and ccr4Δ rpa12Δ mutants on Pol I elongation by assaying their growth on media containing the transcription elongation inhibitor 6-azauracil ( 6AU ) . Because 6AU depletes nucleotide pools , mutants deficient in either Pol I or Pol II elongation become hypersensitive to this compound while wild-type cells eventually adapt by upregulating IMP dehydrogenase expression [26 , 56] . Previous studies have demonstrated that Ccr4-Not mutants exhibit 6AU sensitivity which has been attributed to the function of Ccr4-Not as a Pol II elongation factor [57] . Consistent with these previous reports , ccr4Δ is already 6AU sensitive in the presence of 15 μg/mL 6AU ( Fig . 6F ) . While Rpa12 promotes Pol I elongation in vivo [24] , the rpa12Δ mutant does not exhibit 6AU sensitivity under these conditions which suggests that redundant elongation factors exist in vivo to compensate for Rpa12 loss . Surprisingly , the ccr4Δ rpa12Δ mutant exhibits a mild synthetic sick phenotype on control media and was completely growth impaired in the presence of 6AU ( Fig . 6F ) . We next ascertained the consequences of these mutations on Pol I rDNA binding in vivo . These strains were cultured in nutrient rich media and an acute 6AU treatment , utilizing the same 6AU concentration ( 15 μg/mL ) as above , was performed for 20 minutes before cross-linking and performing α-Pol I ChIP . Under these conditions , Pol I rDNA binding in wild-type cells was reduced to background across the entire rDNA locus , demonstrating that acute 6AU treatment temporarily prevents transcription reinitiation after the clearance of Pol I complexes already engaged in elongation ( Fig . 6G ) . However , the ccr4Δ , rpa12Δ and ccr4Δ rpa12Δ mutants all retained detectable Pol I throughout the rDNA which indicates a reduced ability of Pol I to elongate through the rDNA . Surprisingly , rpa12Δ exhibited significantly lower Pol I levels relative to ccr4Δ , thus suggesting loss of Ccr4-Not function has a more severe effect on Pol I elongation ( Fig . 6G ) . While we detected significant synthetic 6AU sensitivity in the ccr4Δ rpa12Δ relative to either ccr4Δ or rpa12Δ ( Fig . 6G ) , this enhanced phenotype cannot be explained solely by effects on Pol I elongation as there was not increased Pol I retention in ccr4Δ rpa12Δ compared to the individual mutants . These data suggest that Ccr4-Not and Rpa12 likely function in at least partially parallel pathways to regulate Pol I elongation . This role for Ccr4-Not would be consistent with previous high throughput genetic analyses demonstrating synthetic sick and/or lethal phenotypes when Ccr4-Not mutants are paired with deletions of the non-essential Pol I subunits [58 , 59] . The above experiments demonstrate a role for Ccr4-Not in regulating both Pol I transcription initiation and elongation and suggest that Ccr4-Not is required for mTORC1-dependent Pol I regulation . To further delineate how Ccr4-Not functions in mTORC1-dependent Pol I regulation , we spotted wild-type , ccr4Δ , rpa12Δ and ccr4Δ rpa12Δ cultures to control plates or to plates containing low levels of the mTORC1 inhibitors rapamycin ( 3 nM ) or caffeine ( 8 mM ) to reduce mTORC1 signaling and mimic nutrient stress . These same cultures were also spotted to plates containing the DNA replication inhibitor hydroxyurea ( 100 mM ) as well as to control plates that were incubated at 37°C to assay their sensitivity to heat stress . After three days growth , the effects of the various stress conditions on the individual mutants were assessed . While the ccr4Δ exhibited severe sensitivity to even modest mTORC1 inhibition or DNA replication stress , the rpa12Δ was not sensitive to any of these agents ( Fig . 6H ) . Surprisingly , while mTORC1 inhibitors profoundly suppressed growth of ccr4Δ , the ccr4Δ rpa12Δ rescued this phenotype ( Fig . 6H ) . This effect was not due to a simple increase in their general stress resistance as ccr4Δ rpa12Δ did not grow better than ccr4Δ in the presence of DNA replication stress , and the ccr4Δ rpa12Δ was actually ore sensitive to heat stress ( Fig . 6H ) . Combined with the results from above demonstrating that the ccr4Δ rpa12Δ inhibits the increase in Pol I transcription that occurs in a ccr4Δ ( Fig . 6H ) , these data support the conclusion that the severe sensitivity of Ccr4-Not mutants to mTORC1 inhibitors is largely due to deregulation of Pol I transcription .
The Ccr4-Not complex is an established Pol II transcriptional regulator that acts to regulate both transcription initiation and directly stimulate Pol II elongation . Whether Ccr4-Not also controls transcription by other RNA polymerases is currently unknown . In this report , we provide evidence that Ccr4-Not does indeed contribute to the control of Pol I transcription at both the initiation and elongation stages . In support , we demonstrate that Ccr4-Not physically associates with the rDNA and the Pol I holoenzyme and that cells deficient in Ccr4-Not function increase Pol I transcription specifically under nutrient rich conditions . The increased rRNA synthesis in Ccr4-Not mutants is specific to loss of Ccr4-Not function . These effects are not attributable to decreased rRNA processing or rRNA turnover mediated by RNA processing activites associated with Ccr4-Not , including Pan2-Pan3 , TRAMP or the exosome as cells deficient in these activities do not increase rRNA levels . Instead , these Pol I transcriptional effects correlate with specific changes to core factor function and increased Rrn3-Pol I complex formation that occur only when Ccr4-Not mutants are grown in nutrient rich media . Ccr4-Not seems to limit Rrn3-Pol I complex formation in part by controlling the expression and/or turnover of Rrn3 specifically under these conditions which may normally restrict the number of initiation-competent Pol I complexes . Whether Ccr4-Not also impacts other aspects of Rrn3-Pol I regulation , such as their phosphorylation state which might modulate the strength and/or amount of Rrn3-Pol I complex formation , is unknown at this time . How Ccr4-Not disruption affects core factor is currently unclear . Although speculative , one possibility could be that Ccr4-Not disruption either increases core factor promoter binding and/or stabilizes its association with the promoter . Alternatively , physical remodeling of the core factor complex may occur in Ccr4-Not mutants such that the Rrn6 subunit becomes more accessible which might enhance its interactions with Rrn3 . Since no increase in UAF or Rrn3 promoter association occurred in the Ccr4-Not mutant , we believe that an increase in the number of rDNA promoters bound by Rrn6 , and hence an increase in the number of transcriptionally active rDNA repeats , is unlikely to explain these effects but cannot be completely ruled out at this time . Delineating these mechanisms in greater detail will be the subject of future studies . As Rrn6 contacts Rrn3 to facilitate transcription initiation , and formation of Rrn3-Pol complexes is a rate-limiting step for Pol I initiation , the changes to both core factor and Rrn3-Pol I complex formation in Ccr4-Not deficient cells could account for the increase in Pol I transcription [13–15] . Surprisingly , our data demonstrate that Ccr4-Not deficiency does not increase Rrn3-Pol I interactions or rRNA expression in cells grown under nutrient limiting conditions , suggesting that the role of Ccr4-Not in Pol I transcription is modified by the cellular nutrient environment . Ccr4-Not is functionally connected to both the Ras/PKA and mTORC1 pathways suggesting it could be a significant downstream effector of nutrient signaling [45 , 55] . However , these pathways and nutrient signaling in general , do not appear to affect Ccr4-Not interactions with Pol I since neither mTORC1 inhibition nor decreased Ras/PKA signaling altered their association . Since mTORC1 inhibition specifically causes Pol I nucleolar delocalization [46] , these data suggest Ccr4-Not stably associates with Pol I even when Pol I is not bound to the rDNA or under conditions that reduce the rate of cell growth and proliferation . We also provide evidence that Ccr4-Not is required for mTORC1 to effectively reduce Rrn3-Pol I complex formation , and hence suppress Pol I transcription , when signaling through mTORC1 is repressed . Ccr4-Not likely contributes to this process by regulating expression or stability of Rrn3 . Adjusting Rrn3-Pol I complex formation is an essential regulatory node for cells to bridge the rate of rRNA synthesis , and ultimately ribosome biogenesis , with changes in the nutrient environment . Therefore , disrupting Ccr4-Not uncouples Pol I transcriptional control from this key regulatory circuitry and likely explains part of its functional role in the mTORC1 signaling pathway . Cells lacking functional Ccr4-Not are also deficient in rDNA binding by two other key transcriptional regulators , specifically the SSU processome and Hmo1 . Decreased SSU processome binding would be predicted to contribute to some of the increase in rRNA containing ITS1 since SSU processome-dependent co-transcriptional cleavage would be impaired . However , the increase in ETS1 expression alone supports the conclusion that rRNA synthesis is elevated in Ccr4-Not mutants , a conlusion which is further reinforced by the increased binding of Pol I detected . More importantly , since SSU processome also promotes Pol I transcription , how increased Pol I transcription can be rationalized with decreased SSU processome binding is an intriguing issue . The simplest explanation is that the increased rate of Pol I transcription occurring may perturb and/or dislodge some of the SSU processome bound to the rDNA as it is a large molecular complex [60] . Likewise , the minor reduction in promoter binding by Hmo1 could be explained by the significantly increased number of initiating Pol I complexes which might in turn destabilize promoter bound architectural factors such as Hmo1 . Besides negatively regulating Pol I transcription initiation , we provide evidence that Ccr4-Not promotes Pol I transcription elongation as well . Ccr4-Not association with Pol I is modulated by the non-essential Pol I subunits such that Rpa34 promotes , while Rpa12 and Rpa14 oppose , Ccr4-Not interactions with Pol I . A recent study demonstrated that the non-essential Pol II subunits Rpb4 and Rpb7 , which regulate Pol II elongation , are required for Ccr4-Not to interact with Pol II [61] . This study , combined with the results presented here , suggest that Ccr4-Not may as a general principle utilize non-essential polymerase subunits as a means for modulating its interaction with RNA polymerase complexes . These interactions are functionally relevant to Pol I transcriptional control since individual Ccr4-Not or Rpa12 mutants reduce Pol I elongation in vivo while the combinatorial mutant exhibits a synthetic sick phenotype and dramatically enhanced sensitivity to the transcription elongation inhibitor 6AU . These data suggest that Ccr4-Not and Rpa12 function in at least partially parallel pathways to promote Pol I elongation . However , although the ccr4Δ rpa12Δ mutant significantly opposed the increased Pol I transcription caused by ccr4Δ alone , the double mutant did not enhance Pol I retention on the rDNA when challenged with 6AU . The inability to detect enhanced Pol I elongation defects occurring in ccr4Δ rpa12Δ is at least partially explained by the simultaneous increase in Pol I transcription initiation occurring in Ccr4-Not deficient cells which is likely confounding the analysis of Pol I elongation in vivo . Future studies will require careful dissection of the role Ccr4-Not plays in initiation versus elongation to gain a complete understanding of how Ccr4-Not acts at these two distinct stages of the Pol transcription cycle . Importantly , a role for Ccr4-Not as both a positive and negative regulator of Pol I transcription would be consistent with its still poorly understood functions in Pol II transcriptional regulation [35] . One of the most intriguing observations from our studies is that ccr4Δ rpa12Δ reduces the enhanced Pol I transcription occurring in ccr4Δ , yet it simultaneously rescues the extreme sensitivity to mTORC1 inhibition caused by ccr4Δ . Pol I transcription acts dominantly to coordinate expression of both Pol II and Pol III transcribed genes specifically involved in ribosome biogenesis [20 , 21] . Therefore , uncoupling Pol I control from upstream mTORC1 regulation through Ccr4-Not deficiency might disrupt the normal stoichiometry of ribosomal biogenesis and contribute to the induction of a stress state that is growth inhibitory . Previous studies support this concept . For example , uncoupling Pol I transcription from upstream mTORC1 signaling by direct fusion of Rrn3 to the A43 Pol I subunit enables cells to grow normally under nutrient rich conditions . However , when mTORC1 signaling is reduced , these cells are significantly growth impaired [20 , 21] . Additionally , overexpressing rRNA from a heterologous promoter in mTORC1 mutants , as well as mutants in chromatin pathways affecting Pol I transcription , also sensitizes these cells to mTORC1 inhibition [62] . Studies of Pol I transcriptional regulation have focused mostly on the initiation phase of the transcription cycle and have identified the promoter bound factors required for Pol I transcription initiation in both yeast and mammals . The importance of the ancillary factors involved in Pol I transcription , as well as the regulation of Pol I elongation , are emerging as important means for controlling rRNA biogenesis [4] . As such , Ccr4-Not appears to be an ancillary factor shared between the Pol I and Pol II transcription systems that is critical for bridging nutrient signaling through mTORC1 with Pol I transcriptional regulation and control of cell growth and proliferation .
All chemical reagents utilized in this study , including rapamycin , caffeine , and 6AU were purchased from Fisher Scientific or Sigma-Aldrich . The CCR4 and ccr4-1 expression vectors were generated by utilizing plasmids pJCN100 ( CCR4 ) and pJCN101 ( ccr4-1 ) as templates in a PCR reaction with CCR4 primers containing a C-terminal mono-FLAG sequence . The resulting PCR fragments were cloned into the Xba I and EcoRI sites of the p416ADH expression vector [63] . All yeast strains were derived from the BY4741 genetic background . Yeast strains and plasmids are listed in S1 Table . All yeast deletion mutants and genomically-tagged strains were generated utilizing PCR-generated targeting cassettes as previously described [64] . Antibiotics utilized for strain generation were purchased from Invitrogen or GoldBio . For yeast cells grown under nutrient rich conditions , the media used was 1% yeast extract , 2% peptone , and 2% dextrose ( YPD ) . Cells grown under nutrient defined conditions were cultured in synthetic complete ( SC ) consisting of 0 . 17% yeast nitrogen base without amino acids , 1 g/L of glutamic acid , 2% dextrose , and 1 . 9% drop-out mix with the appropriate drop-out nutrients added back . SC media reagents were purchased from US Biologicals . To select for yeast cells carrying plasmids , cells were grown in SC dropout media lacking either uracil or leucine . Cells were cultured to mid-log phase ( OD600 = 1 . 0–1 . 5 ) at 30°C in either YPD or SC media for the experiments outlined in this study . All yeast spotting assays were performed utilizing cultures grown overnight in the appropriate growth media . Cell numbers were then normalized based on their OD600 reading , five-fold serially diluted and spotted to the appropriate plate media . Cells were typically incubated for three to five days at 30°C unless otherwise specifically stated . For 6-azauracil assays , the indicated strains were transformed with a URA3 containing plasmid and cultured overnight in SC-ura media before spotting to either control SC-ura media or SC-ura media containing 15 μg/mL 6-azauracil . Plates were then incubated for six days at 30°C . Yeast strains were cultured in either YPD or the appropriate SC media to an OD600 = 1 . 0–1 . 5 before cross-linking with 1% formaldehyde for 15 minutes followed by a five minute quench with 125 mM glycine at room temperature with gentle shaking . ChIP extracts and immunoprecipitations were performed exactly as described previously utilizing 400–500 μg of ChIP extract [62] . 10% of total ChIP extract was also purified as the input control . Purified IP DNA was eluted in 50 μL of qPCR quality water and then diluted 1:5 while the input DNA was eluted in 100 μL and subsequently diluted 1:50 . Purified DNA was analyzed by qPCR on an Applied Biosystems StepOne Plus real-time PCR machine utilizing rDNA specific primers and the data were normalized with the formula 2 ( Input Ct-IP Ct ) and expressed as % IP/Input . Primer sequences are listed in S2 Table . Total RNA was isolated by resuspending yeast pellets in Tri-Reagent ( Sigma-Aldrich ) in the presence of chloroform and glass beads . Samples were homogenized by vortexing and then centrifuged for two minutes at maximum speed . The soluble fraction was removed , precipitated with isopropyl alcohol , and centrifuged for 15 minutes at 4°C to pellet the nucleic acids . Pellets were resuspended in 50 μL of DEPC-treated dH2O in the presence of DNase I and digested for 30 minutes at 37°C . Samples were then phenol/chloroform extracted , precipitated , and resuspended in 50 μL DEPC-dH2O . For cDNA synthesis with the Im-Prom II cDNA synthesis system ( Promega ) , 1 μg of total RNA was utilized with random hexamer primers , per the manufacturer’s instructions , in a total volume of 20 μL . After synthesis , purified cDNA were diluted to a final volume of 100 μL and then 1:5 dilutions were utilized for qPCR analysis with gene-specific primers listed in S2 Table . qPCR analysis was performed as described above and the gene-specific signals were normalized to the expression of the SPT15 housekeeping gene using the formula 2 ( SPT15 Ct- Target gene Ct ) . Wild-type was set to a value of 1 while all yeast mutants were expressed relative to wild-type . All results were presented as the average and standard deviation of four or more independent experiments and the statistical significance determined by Student’s t-test . Yeast whole-cell extracts were prepared as previously described and 500 μg of extract was utilized for immunoprecipitations in extract buffer ( 300 mM NaCl , 10 mM Tris pH 8 . 0 , 0 . 1% NP-40 , and 10% glycerol ) containing protease and phosphatase inhibitors [62] . Immune complexes were isolated using Protein A-conjugated agarose beads and washed extensively , resolved by SDS-PAGE , transferred to PVDF membrane , and immunoblotted with the appropriate antibody . Ubiquitin-conjugated Rrn3 was analyzed using a similar approach as previously described [36] . Wild-type and not4Δ cells expressing Rrn3-1XMyc/7X-histidine were cultured as described above in YPD , and then cell pellets were weighed and equivalent amounts of pellet were lysed in denaturing buffer ( 8M urea , 300 mM NaCl , 50 mM Tris pH 8 . 0 , and 0 . 5% Triton X-100 ) by bead beating at 4°C for six minutes in the presence of glass beads . Cell lysates were clarified by centrifugation at 15 , 000 rpm at 4°C for 15 minutes . From the clarified supernatant , 50 μL were removed and used as the total extract . The remainder of the supernatant was rotated with 50 μL of Ni2+-conjugated agarose beads at room temperature for two hours followed , by three washes in denaturing buffer before resolving samples by 8% SDS-PAGE and immunoblotting . All immunoblots were developed utilizing Immobilon ECL reagent ( Millipore ) and exposure to film . Quantification of immunoblots was performed using ImageJ . Antibodies used for immunoblot and ChIP analyses were as follows: α-HA ( mouse , Santa Cruz ) , α-Myc ( mouse clone 4A6 , Millipore ) , α-β-actin ( Abcam ) , α-ubiquitin ( mouse , Santa Cruz ) , α-G6PDH ( rabbit , Sigma-Aldrich ) and general IgG ( rabbit , Santa Cruz ) .
|
All cells communicate their environmental nutrient status to the gene expression machinery so that transcription occurs in proportion to the nutrients available to support cell growth and proliferation . mTORC1 signaling , which is essential for this process , regulates Pol I-dependent rRNA expression . We provide evidence that the RNA polymerase II regulatory complex , Ccr4-Not , also is a novel Pol I regulator required for mTORC1-dependent control of Pol I activity . Ccr4-Not disruption increases Pol I transcription due to an inability to decrease Pol I interactions with the transcription factor Rrn3 when mTORC1 signaling is reduced . Additionally , genetic and biochemical evidence supports a role for Ccr4-Not as a positive regulator of Pol I transcription elongation as well . Surprisingly , while Ccr4-Not mutations profoundly inhibit growth when mTORC1 activity is reduced , this phenotype is reversed by simultaneously impairing Pol I transcription . Overall , our data demonstrate that the evolutionarily conserved Ccr4-Not complex mediates environmental signaling through mTORC1 to control Pol I transcription initiation and , additionally , to regulate Pol I elongation . These studies further suggest that uncoupling Pol I from upstream mTORC1 activity by targeting Ccr4-Not sensitizes cells to mTORC1 inhibitors which is a concept that could have implications for anti-cancer drug development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Ccr4-Not Regulates RNA Polymerase I Transcription and Couples Nutrient Signaling to the Control of Ribosomal RNA Biogenesis
|
Habitat destruction and land use change are making the world in which natural populations live increasingly fragmented , often leading to local extinctions . Although local populations might undergo extinction , a metapopulation may still be viable as long as patches of suitable habitat are connected by dispersal , so that empty patches can be recolonized . Thus far , metapopulations models have either taken a mean-field approach , or have modeled empirically-based , realistic landscapes . Here we show that an intermediate level of complexity between these two extremes is to consider random landscapes , in which the patches of suitable habitat are randomly arranged in an area ( or volume ) . Using methods borrowed from the mathematics of Random Geometric Graphs and Euclidean Random Matrices , we derive a simple , analytic criterion for the persistence of the metapopulation in random fragmented landscapes . Our results show how the density of patches , the variability in their value , the shape of the dispersal kernel , and the dimensionality of the landscape all contribute to determining the fate of the metapopulation . Using this framework , we derive sufficient conditions for the population to be spatially localized , such that spatially confined clusters of patches act as a source of dispersal for the whole landscape . Finally , we show that a regular arrangement of the patches is always detrimental for persistence , compared to the random arrangement of the patches . Given the strong parallel between metapopulation models and contact processes , our results are also applicable to models of disease spread on spatial networks .
In an increasingly fragmented and patchy world [1] , species survival critically depends on dispersal , as local populations at high risk of extinction could be rescued by immigration from neighboring populations [2] . This intuition forms the core of metapopulation theory: even though local populations occupying patches of suitable habitat might undergo extinction , persistence can be achieved at the metapopulation level—rather than in each patch—as long as individuals can disperse between patches and thus recolonize empty ones [1] . It was Levins [2] who first proposed a species occupancy model ( where the goal is to measure the presence/absence of a species in a patch ) which assumed infinitely many patches of suitable habitat , all mutually reachable from any other . His work highlighted one of the main features common also to more complex models , namely that persistence is achieved when the colonization rate exceeds the extinction rate ( S1 Text ) . Hanski & Ovaskainen [3] extended this formulation to realistic landscapes , composed of multiple patches , each having a different “value” ( e . g . , size , or density of resources ) , connected by dispersal whose strength depends on the distance between patches . This effectively defines the landscape as a network in which the nodes are the patches , and the weighted edges express the colonization rates [3–7] . When developing a general theory of persistence in fragmented landscapes , we are faced with the problem that no two landscapes are alike . The situation is reminiscent of complex network theory , in which any two food webs , transportation , or gene-regulation networks are different , making it difficult to pinpoint the salient features of each system . In these cases , much progress has been made by contrasting empirical networks with those generated by simple models such as the Erdős-Rényi random graph or the Barabási-Albert model [8] . Our main goal is to propose and study a reference model for metapopulations dispersing in fragmented landscapes . One could be tempted to simply take the Erdős-Rényi model and apply it to metapopulations [7] . However , this model lacks a fundamental feature of real dispersal networks: two patches close in space are more likely to exchange individuals than two that are far away . A more fruitful avenue is to take N patches , distribute them randomly in space , and connect any two patches that are closer than some threshold distance [9] . This defines a so-called Random Geometric Graph [10–12] , for which it has been shown that the number of edges per node required to make the graph connected ( i . e . , the graph is composed of just one “piece” ) is much higher than that for Erdős-Rényi graphs , with the two converging for high-dimensional landscapes [13] . However , natural populations exist in low-dimensional environments , with species endemic to the dunes of Lake Michigan [14] experiencing what is effectively a one-dimensional space , and birds nesting in fragmented forests living in a two-dimensional landscape . As such , Erdős-Rényi random graphs are inadequate descriptors of natural dispersal networks [7] . Random Geometric Graphs can be generalized even further . Instead of treating the connectedness of two patches in an “either/or” manner , we may think of it as a smooth function of distance . This function is what we refer to as the “dispersal kernel” . Such networks have been introduced in the physics of disordered systems [15] and are called Euclidean Random Matrices ( S1 Text ) . In fact , a Random Geometric Graph is but one special case , in which the dispersal kernel is rectangular ( Fig 1 , top row ) , yielding a dispersal rate of either 0 or 1 , while generic Euclidean Random Matrices cover the broad spectrum of intermediate cases where dispersal rates vary smoothly with distance ( Fig 1 , bottom two rows ) . Here we use Euclidean Random Matrices to study metapopulations dispersing in random fragmented landscapes . For simplicity , we treat the case of patches that are uniformly distributed in space , and with patch values independently sampled from a random distribution . We derive a condition for metapopulation persistence analytically , highlighting that number of spatial dimensions , number of patches , shape of the dispersal kernel , and the variability in patch value are all key to determining persistence . We also show that a metapopulation can persist in two different regimes , localized and nonlocalized , giving rise to completely different responses to habitat loss . We finally show that arranging the patches in a perfect grid ( as often considered in the design of protected areas [16–18] ) yields a lower likelihood of persistence compared to the case in which we distribute the patches randomly .
Take N patches of suitable habitat , positioned in a landscape . Each patch can be either occupied or unoccupied by the species of interest at time t . Empty patches can be colonized through dispersal from occupied patches; occupied patches can become empty following a local extinction event . For simplicity , we assume that both colonization and extinction are independent Poisson processes in continuous time . Then , the dynamics of the system can be described exactly through a continuous-time Markov process with 2N configurations [19] , ranging from all patches being empty to all being occupied . In the absence of external input , the Markov process has only one absorbing state , the one in which the metapopulation is extinct and all patches are empty . Despite the fact that this is the ultimately absorbing state , the dynamics are dominated for a long time by a quasi-stable state , in which a characteristic proportion of patches are occupied . This quasi-stable state is the focus of our work . Because such large Markov processes are difficult to treat analytically , researchers have sought ways to approximate the dynamics using simpler models . Interestingly , the same class of models originated in two distinct scientific communities: one interested in metapopulations and conservation biology [3 , 20 , 21] , the other interested in the spread of infectious diseases in a social network [19 , 22 , 23] . In fact , a metapopulation model can be turned into a Susceptible-Infected-Susceptible ( SIS ) model by relabeling “patches” as “individuals” , “dispersal” as “infection” , and “extinction” as “recovery” . Though the two scientific communities have opposite goals ( preservation of metapopulations versus eradication of diseases ) , their models are identical , so results in one area directly translate into the other . In particular , both communities studied an approximation in which the system is modeled by N differential equations , each tracking the probability that a certain patch is occupied/individual is infected . Besides simplifying calculations , this approximation has the added benefit that the quasi-stable state of the Markov process becomes the steady-state of the differential equation model . Here we follow the formulation found in the SIS literature [23 , 24] , which might be new to readers more familiar with the metapopulation literature . Doing so highlights the critical step made to approximate the dynamics as well as the strong connection between metapopulation dynamics and disease spread . We have N patches/individuals Si ( t ) , each taking value 1 or 0 ( i . e . , each being a Bernoulli random variable ) at time t , depending on whether the given patch is occupied / individual is infected . Let δi be the probability per unit time of patch i going extinct / individual i recovering . Let Dij be the probability per unit time that , given that patch j is occupied while patch i is empty , patch j is going to colonize patch i ( individual j is infected while individual i is uninfected , individual j is going to infect individual i ) . Assuming that both colonization and extinction ( infection and recovery ) are Poisson processes , one can write a system of ordinary differential equations tracking the dynamics of the expectations 𝔼[Si ( t ) ] in time: d 𝔼 [ S i ( t ) ] d t = - δ i 𝔼 [ S i ( t ) ] + ∑ j ≠ i D i j 𝔼 [ S j ( t ) ] - ∑ j ≠ i D i j 𝔼 [ S i ( t ) S j ( t ) ] ( 1 ) Assuming independence , one can write 𝔼[Si ( t ) Sj ( t ) ] = 𝔼[Si ( t ) ]𝔼[Sj ( t ) ] , thus obtaining the Hanski-Ovaskainen model in metapopulation theory [3 , 20 , 21] or the so-called NIMFA model in the SIS literature [23]: d 𝔼 [ S i ( t ) ] d t = - δ i 𝔼 [ S i ( t ) ] + ( 1 - 𝔼 [ S i ( t ) ] ) ∑ j ≠ i D i j 𝔼 [ S j ( t ) ] ( 2 ) In general , the variables Si ( t ) and Sj ( t ) will be correlated . However , it has recently been proven that if extinction/cure and colonization/transmission are Poisson processes , the correlation can only be positive [25] . This means that the above equation necessarily overestimates the growth of the probability that patches are occupied/individuals are infected . In our setting , this turns out to guarantee that our estimate of the persistence threshold of the metapopulation is always conservative [19] . Moreover , in the disease literature a treatment of the second-order approximation ( accounting for pairwise correlations , but foregoing third-order ones ) has appeared [24] . Here we generalize the model proposed by Hanski & Ovaskainen [3] starting from Eq ( 2 ) . The N patches of suitable habitat are positioned in a d-dimensional landscape [26] . Each patch is characterized by a position xi ( a vector of length d ) , and by a patch value Ai , expressing for example the carrying capacity of the patch . The extinction rate in patch i is given by a general extinction rate δ ( a property of the species in question and of the landscape as a whole ) , whose effect is mitigated in patches of high value: δi = δ/Ai . Individuals can disperse between patches with a rate that depends on the distance between the two patches , a dispersal kernel function , and the value of the patch from which individuals disperse: Dij = Aj f ( |xi−xj|/ξ ) , where f is the dispersal kernel function , |xi−xj| is the Euclidean distance between patches i and j , and ξ is the typical dispersal length for individuals of the species of interest . Taking Eq ( 2 ) , writing pi ( t ) instead of 𝔼[Si ( t ) ] , and substituting in the above expressions , we obtain the model: d p i ( t ) d t = ( 1 - p i ( t ) ) ∑ j ≠ i f ( | x i - x j | ξ ) A j p j ( t ) - δ A i p i ( t ) . ( 3 ) where pi ( t ) is the probability of finding the species of interest in patch i at time t , and the contribution of each patch j to the probability of finding the species in i is weighted by the dispersal kernel f , the patch value Aj and the probability of occurrence in patch j , pj ( t ) . The extinction rate δ is mitigated in patches of high value . This system of equations has at least one equilibrium p , and the metapopulation is persistent when ∑i pi > 0 . Interestingly , if the equilibrium p is strictly positive , then it is also stable [20] . Although one cannot analytically solve for the equilibrium , all the information needed to calculate the persistence of the system is enclosed in the so-called dispersal matrix M , whose diagonal entries are zero , and off-diagonal are defined as Mij = Mji = Ai Aj f ( |xi−xj|/ξ ) . The leading eigenvalue of M , λ ( “metapopulation capacity” [3] ) , determines the persistence of the metapopulation . Specifically , the metapopulation can be persistent if and only if λ > δ [3 , 21] . As such , probing the dependence of λ on different factors is crucial to predicting persistence . If the leading eigenvalue determines persistence , the corresponding eigenvector wi quantifies the relative importance of patches [20 , 27] and , when δ ≈ λ , it is related to the stationary solution p itself [20] ( S1 Text ) . Habitat destruction ( i . e . , the removal of patches ) lowers the metapopulation capacity . Mathematically , patch removal is carried out removing a row and the corresponding column from the matrix M . The removal of a single patch has a negative effect on λ , but the magnitude of the effect depends on patch identity . The relationship between the eigenvalue and the eigenvector [27] quantifies the effect of the removal of patch i on the metapopulation capacity: λ - λ i - λ = Δ λ i λ ≈ w i 2 , ( 4 ) where λ is the metapopulation capacity before removal , λ i − is the metapopulation capacity after the removal of patch i , and w is the normalized ( ∑ i w i 2 = 1 ) dominant eigenvector of M ( before removal ) . Thus , the components of the eigenvector quantify patch importance , as typically found in the realm of complex networks when measuring eigenvector centrality [28] . Here we study the model in Eq ( 3 ) when patches are randomly distributed in the landscape . We take a large number of patches N randomly positioned in a d-dimensional cube by sampling their positions from a uniform distribution . The cube has sides of length L ( with L ≫ ξ to avoid dealing with edge effects ) . Although in principle patch values could be correlated ( e . g . , high-value patches being close to each other ) , for simplicity we sample the Ai independently from a distribution with mean one and variance σ2 . We examine different kernel functions ( Hanski & Ovaskainen considered only one ) , and we stress that , unless specified , our results hold for any kernel function , including those that are not monotonically decreasing ( S1 Text ) . For illustration , we use the Exponential , Gaussian and Rectangular kernel functions ( Fig 1 ) .
Knowing the number of dimensions ( d ) and size ( L ) of the landscape , the number of patches N , the dispersal kernel f ( |xi−xj|/ξ ) , and the distribution of the values of the patches ( σ2 ) , we want to approximate λ , the metapopulation capacity . The matrix M is nonnegative ( Mij ≥ 0 ) and symmetric ( Mij = Mji ) , therefore λ is bounded by the average row sum from below [29] . Take the patch values to be one for all patches , and assume a rectangular kernel ( Random Geometric Graph ) . Then , the average row sum is simply the average number of neighbors each patch has ( the average degree of the network ) . When using another kernel ( Euclidean Random Matrix ) , the row sum can be interpreted as the average number of “effective neighbors” , ne , meaning that patches that are closer contribute more to the sum than those that are far away . As such , when all patches have value one , λ ≥ ne . When the patch values are sampled from a distribution with mean one and variance σ2 , assuming N large , we obtain λ ≥ ne ( 1+σ2 ) ( S1 Text ) . This provides a conservative criterion for metapopulation persistence: n e ( 1 + σ 2 ) > δ . ( 5 ) Next , we want to approximate ne for different parameterizations . In fact , ne is influenced by the dispersal kernel , the dispersal length , the number of dimensions , the number of patches , and the size of the landscape . The formula , in the limit L ≫ ξ , reads n e ≈ N L d G f ( d ) ξ d ( 6 ) ( S1 Text ) , where the first term measures the patch density , obtained by dividing the number of patches N by the volume Ld ( area if d = 2 , or length if d = 1 ) , while the second term ( Gf ( d ) ξd ) measures the typical volume accessible via dispersal from a patch . Their product ne represents therefore the typical number of patches accessible via dispersal . The numerical factor Gf ( d ) depends on the functional form of the dispersal kernel and on d . For example , Gf ( d ) = ( 2π ) d/2 for the Gaussian kernel ( S1 Text ) . Note that although we kept ξ and L distinct , we could have measured one in units of the other , as what matters for the metapopulation capacity is their ratio . Fig 2 shows that λ depends on all the factors that are needed to estimate ne: the number of patches N , the kernel f , the dispersal length ξ , and the number of dimensions d . Moreover , λ depends on the probability distribution function for the value of the patches . However , when plotting λ versus ne ( 1+σ2 ) , where σ2 is the variance of the distribution , all curves collapse into the same one , meaning that two very different fragmented landscapes , or with different distribution of patch values but the same value of ne ( 1+σ2 ) , have approximately the same metapopulation capacity ( S1 Text ) . In the previous section , we derived a persistence criterion for the case in which the patches are randomly distributed in a d-dimensional cube . This random arrangement is among the most “disordered” possible . We now examine how a more regular arrangement of patches would affect persistence . We arrange the patches in a regular grid spanning the d-dimensional cube . We then perturb this regular arrangement by jiggling the patch positions described by the parameter η . When η = 0 the patches are perfectly arranged in a grid , and when η = 1 , the patches are distributed at random ( S1 Text ) . Tuning η , we can explore the effect of intermediate states between the fully regular and fully random . If the patches are arranged in a regular grid , M has a particularly simple structure . In fact , if the space had no edges ( as in a d-dimensional torus ) , all the rows of M would sum to exactly the same constant λgrid , the leading eigenvalue of M . For a sufficiently large number of patches , approximately the same is found when the space is bounded . As mentioned before , λ is larger than or equal to the average row sum of M , but since here all rows have the same sum , we obtain equality ( S1 Text ) . This means that λgrid is always lower than the corresponding λrand , found for a system with the exact same average row sum , but with random patch positions . Simulations show that—all other things being equal—increasing η always increases the leading eigenvalue ( S1 Text ) , so that the likelihood of persistence is increased by disordered patch arrangements . Both in the grid and in the random arrangements , the metapopulation capacity grows linearly with the number of patches ( Fig 3; see also Eq 6 ) , and we always find λgrid < λrand . Similarly , when we take the average patch occupancy p i ¯ , measuring the proportion of occupied patches , we find that randomly distributing the patches leads to a higher ( when λrand > δ ) or equal ( when both λrand and λgrid are lower than δ , and thus p i ¯ = 0 ) proportion of occupied patches ( Fig 3 ) . So far , we have considered monotonically decreasing dispersal kernels , in which the proverbial seed never falls far from the tree . This type of kernel was the first being studied [3] , and is frequently encountered in empirical analyses [30] . However , trees have strategies to disperse their seeds ( e . g . , fruits for seed-dispersers , wind dispersal ) , while seeds falling close to the tree could be disproportionally consumed by predators ( Janzen-Connell hypothesis ) , giving rise to more complex shapes for the dispersal kernel [31] . In the case of monotonically decreasing dispersal kernels ( such as exponential or Gaussian ones ) , it is possible to demonstrate analytically that λgrid < λrand ( S1 Text ) . In the case of non-monotonically decreasing , hump-shaped dispersal kernels , the same proof does not hold in general . Despite this however , numerical simulations ( S1 Text ) show that , even if the kernel is fine-tuned to peak around the nearest neighbor in a grid , the metapopulation capacity is still larger for a random assembly of patches than a regular arrangement . This is a counterintuitive result , since in principle we can always imagine a kernel which is zero everywhere except in an infinitely narrow band around the nearest neighbor distance . Then the slightest perturbation away from a perfect grid structure would immediately reduce the metapopulation capacity to zero . Based on this extreme case scenario , one may justifiably think that slightly relaxing the assumption of an infinitely sharp peak in the kernel will still result in a grid arrangement being more beneficial than a random one . Our results show that this is not so: even a very slight smearing of the kernel from the extreme-peaked case leads in practice to a random arrangement of patches having a higher metapopulation capacity than the grid arrangement . In network theory , the leading eigenvector measures nodes’ importance , with applications in many different areas [32–34] , including metapopulation theory [3 , 20 , 27] . Given that the leading eigenvalue of M determines the persistence of the metapopulation , naturally its value decreases when patches are removed , and it can be shown [27] that when patch i is removed , the relative change of the eigenvalue is approximately equal to w i 2 , where wi is the i-th component of the eigenvector w ( Eq 4 ) . If we assume that every patch is equally connected to every other patch , as in Levins’s model [2] , all the patches have the same importance . Our model predicts a radically different scenario , in which the importance of the different patches can be extremely heterogeneous ( Fig 4 ) . To quantify heterogeneity , we introduce Ψ , a measure related to the variance of w i 2 ( S1 Text ) . Ψ is zero if all the patches have the same importance , and is one if only a single patch contributes to the leading eigenvector , i . e . , all but one component of the eigenvector are zero . Ψ depends both on ne and σ2 . As the variance of patch values σ2 increases , Ψ increases ( S1 Text ) . The dependence on ne is more subtle and interesting: for large values of ne , Ψ is close to zero , while , as ne decreases , Ψ remains close to zero up to a critical value , at which point it suddenly increases ( S1 Text ) . This critical value corresponds to a transition from a system where all patches have more or less the same importance for metapopulation persistence to one where a few patches , localized in space , contribute disproportionately to the eigenvector ( Fig 4 ) . We emphasize that this result holds for any value of the extinction rate δ . In addition , in the λ ≈ δ limit , i . e . , when the metapopulation is close to the extinction threshold , the eigenvector is also related to the stationary solution p ( S1 Text ) . Close to the extinction threshold the persistent patches are spatially localized , i . e . , the patches with high likelihood of persistence are all close in space . In the previous section , we showed that the leading eigenvalue of M for a landscape in which patches are arranged in a grid is always smaller than that obtained for randomly distributed patches . A similar pattern holds also for the eigenvector: the variance of the eigenvector components of a perfect grid is always zero ( S1 Text ) , as all the patches have the same importance . However , when we disturb the arrangement , we find that , for ne ( 1+σ2 ) small , the variance in the importance of patches Ψ increases rapidly ( S1 Text ) . The important patches , i . e . , the patches associated with a large eigenvector component , are not uniformly distributed in space , but rather they are spatially clustered ( S1 Text ) . As such , not only the eigenvector is localized ( large Ψ ) , but the localization is spatial , with all the important patches enclosed in a small region of the landscape ( Fig 4 ) .
By modeling fragmented landscapes as networks [4 , 6] in which the nodes are patches and the weighted edges represent dispersal , we have shown that metapopulation persistence can be studied analytically for the case in which patches are randomly distributed in the landscape , and patch values are independently sampled from some distribution . The derivation highlights that a few key quantities determine the metapopulation capacity: the density of the patches , with denser landscapes yielding a higher probability of persistence; the shape of the dispersal kernel; the number of dimensions; and the variability in patch value , with higher variance being beneficial . Our analysis provides a null model for metapopulation persistence . For a given empirical landscape , in which the patches positions are not necessarily described by a uniform distribution and patch values are not independent of patch position [7 , 26] , the effect of these features on persistence can be disentangled from the effects of other factors by contrasting the metapopulation capacity of the empirical landscape with what is expected according to our framework . Interestingly , we found that even a very small amount of disorder and variation are beneficial for persistence . First , the variance in patch values has a very strong positive effect on the metapopulation capacity , meaning that highly heterogeneous patch values yield higher λ than homogeneous ones . Second , we found that more “disordered” arrangements of the patches increase both the metapopulation capacity and the expected proportion of occupied patches . This is especially relevant when metapopulations are close to extinction , as localization becomes key for maintaining the metapopulation viable , albeit in a spatially confined region . Note that these analytic results shed light on previous simulations suggesting that unequal spacing between the patches is beneficial for populations close to extinction [18] , and are consistent with what was found when considering stochastic dynamics [35] . The development in the study of metapopulation models is paralleled by that of contact processes in infectious diseases . Our results could find applications in the case of diseases spreading on a “geographic” network ( e . g . , agricultural pests ) . Similarly , the fact that variance in patch value increases the metapopulation capacity would suggest that adding individual variability to the recovery time and infectiousness would lead to faster transmission and an increased chance of epidemic outbreaks . Although we have examined the case of randomly distributed patches using a uniform distribution , the same approach holds when the distribution is not uniform [26] . For example , riparian plants in a landscape crossed by a river will be concentrated in the vicinity of the water , leading to a non-uniform distribution . Fortunately , in the limit of many patches , any distribution can be taken into account , by evaluating analytically or numerically the integral defining ne in the general case ( S1 Text ) . Similarly , we accounted for an integer number of dimensions , but a fractional d ( e . g . , in a fractal-like river basin ) would not alter the framework . Finally , we assumed that species can disperse equally in all directions , with no preference . When there is a clear preferential direction of dispersal ( e . g . , wind-dispersal of seeds , or fish larvae dispersing in a river ) , the theory of Euclidean Random Matrices can be extended to account for this lack of symmetry [36] . There are indeed several ways to generalize this work . One can consider a scenario where extintion and colonization have a different dependence on patch values or include a dependence on the latter in the dispersal kernel . These complications can all be viewed as generalizations of our approach , where new correlations are introduced between the elements of the random matrix . The derivation of a criterion for metapopulation persistence bears a striking resemblance to the derivation of stability criteria for large ecological communities [37 , 38] , as in both cases the use of random matrix theory led to the identification of the few basic parameters responsible for the large-scale behavior of the systems . The advantage of this approach is that , once the modeling of the matrices is in place , the derivation of the results requires only elementary algebra . Random matrix theory is currently experiencing an impressive growth [39] , greatly expanding the potential for biological applications .
|
Like the hundreds of paintings of water lilies by Monet , any two landscapes in which a metapopulation dwells are different , as the size , shape and location of the patches of suitable habitat ( the lilies ) , distributed over a inhospitable background ( the water ) vary among landscapes . Yet , as all the paintings depict the same pond in Giverny , different fragmented landscapes could have the same value to a metapopulation . Here we ask what are the key features we should measure to predict persistence of metapopulations inhabiting fragmented landscapes , and show that few quantities determine the fate of metapopulations—so that two very different-looking landscapes could lead to the same likelihood of persistence . We also show that regular arrangements of the patches in space are detrimental for persistence , and that the typical behavior of metapopulations close to extinction is to be mostly localized in a confined region of the landscape .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Metapopulation Persistence in Random Fragmented Landscapes
|
Previous studies have shown that let-7 can repress the post-transcriptional translation of LIN28 , and LIN28 in turn could block the maturation of let-7 , forming a double-negative feedback loop . In this study , we investigated the effect of germline genetic variants on regulation of the homeostasis of the let-7/LIN28 loop and breast cancer risk . We initially demonstrated that the T/C variants of rs3811463 , a single nucleotide polymorphism ( SNP ) located near the let-7 binding site in LIN28 , could lead to differential regulation of LIN28 by let-7 . Specifically , the C allele of rs3811463 weakened let-7–induced repression of LIN28 mRNA , resulting in increased production of LIN28 protein , which could in turn down-regulate the level of mature let-7 . This effect was then validated at the tissue level in that the normal breast tissue of individuals with the rs3811463-TC genotype expressed significantly lower levels of let-7 and higher levels of LIN28 protein than those individuals with the rs3811463-TT genotype . Because previous in vitro and ex vivo experiments have consistently suggested that LIN28 could promote cellular transformation , we then systematically evaluated the relationship between rs3811463 as well as other common LIN28 SNPs and the risk of breast cancer in a stepwise manner . The first hospital-based association study ( n = 2 , 300 ) demonstrated that two SNPs were significantly associated with breast cancer risk , one of which was rs3811463 , while the other was rs6697410 . The C allele of the rs3811463 SNP corresponded to an increased risk of breast cancer with an odds ratio ( OR ) of 1 . 25 ( P = 0 . 0091 ) , which was successfully replicated in a second independent study ( n = 1 , 156 ) with community-based controls . The combined P-value of the two studies was 8 . 0×10−5 . Taken together , our study demonstrates that host genetic variants could disturb the regulation of the let-7/LIN28 double-negative feedback loop and alter breast cancer risk .
MicroRNAs ( miRNAs ) are an abundant class of ∼22 nucleotides ( nt ) long endogenous noncoding RNAs that can guide post-transcriptional gene silencing . They have important roles in multiple physiological processes , including developmental timing , cellular proliferation , differentiation , and apoptosis [1] . Deregulation of miRNAs has been linked with the occurrence , development , and prognosis of human cancers [2] . Let-7 is a family of miRNAs that were initially identified to control heterochronic timing in the nematode Caenorhabditis elegans [3] . Defects in let-7 in C . elegans result in unfavorable phenotypes , such as over-proliferation and lack of terminal differentiation , which resemble the characteristics of carcinogenesis in human beings . Subsequent studies revealed that let-7 could regulate multiple oncogenes , such as RAS [4] , MYC [4] , [5] , and HMGA2 [6] . A recent study has further linked abnormal let-7 levels to the carcinogenesis of breast cancer and maintenance of malignant phenotypes [7] . Based on these findings , let-7 is currently recognized as a type of tumor suppressor miRNA . LIN28 , which was also initially discovered in C . elegans as a heterochronic gene [8] , has been identified as a reprogramming factor that can induce pluripotent stem cells [9] . Overexpression of LIN28 promotes cellular transformation and is associated with advanced human cancers [10] . Taken together , these findings imply that LIN28 possesses oncogenic characteristics . Intriguingly , there is a double-negative feedback loop between LIN28 and let-7; let-7 targets and represses the translation of LIN28 [11] , [12] , while LIN28 blocks the maturation of let-7 miRNAs [13]–[16] . This cascade has recently been demonstrated to be involved in the oncogenesis of human cancers [17] , [18] . It is therefore of critical importance that the let-7/LIN28 double-negative feedback loop is subjected to strict regulation to stay at an appropriate homeostatic status . Although detailed mechanisms of how this loop is regulated remain largely unknown , it can be deduced that slight changes in let-7 or LIN28 levels can be amplified by the loop , resulting in more significant alterations . Theoretically , physiological variations in let-7 and LIN28 levels are also subjected to the amplifying effect of the loop , leading to differential expression and/or function of these factors in different individuals . One important source of inter-individual variation in phenotypes is genetic variants , the major component of which is the single nucleotide polymorphism ( SNP ) . Many studies have shown that allelic variants of SNPs can influence the expression and/or function of their hosting genes . Several recent studies have further highlighted that the miRNA-related SNPs , especially those located within miRNA complementary sites , can remarkably alter the biogenesis and/or function of the corresponding miRNA [19]–[22] . Breast cancer is a common female malignancy , and the occurrence of breast cancer is associated with both genetic and environmental risk factors . Although previous studies have identified a group of high penetrance susceptibility genes and loci of breast cancer , such as BRCA1 , BRCA2 , and CHEK2 [23] , these genes could only explain a small part of the genetic risk of breast cancer . In the past several years , SNPs have been widely employed as a type of genetic marker to identify susceptibility genes or loci of breast cancer . Using this strategy , many low penetrance susceptibility genes of breast cancer have been successfully identified . These findings prompted us to investigate if there were any SNPs located with the let-7 and LIN28 genes that could alter the biogenesis and/or function of these two factors and whether such genetic variants were associated with risk of developing breast cancer .
We began by using in silico means to identify genetic variants located within the let-7 and LIN28 genes that had potential functional effect . The 12 genes of let-7 family with their 5′ and 3′ flanking sequences of 100 bp long as well as the coding sequence ( CDS ) , promoter region , and 3′ untranslated region ( 3′ UTR ) of LIN28 gene were searched in SNP databases as described in the Materials and Methods section . No common SNP that has minor allele frequency ( MAF ) higher than 5% in Han Chinese was found in the CDS of LIN28 and let-7 genes along with their flanking sequences , indicating LIN28 and let-7 are evolutionally conservative . We identified a SNP , rs3811464 , in the promoter region of LIN28 that was 126 bp upstream of the transcriptional start site of LIN28 . Although the initial prediction result showed that the minor allele of rs3811464 could lead to a loss of the binding site for one transcriptional factor named Zinc finger protein with Interaction Domain ( ZID ) , a luciferase assay failed to confirm such an effect ( data not shown ) . On the other hand , in the 3′ UTR of LIN28 , we found three common SNPs ( rs4659441 , rs3811463 , and rs6697410 ) among Han Chinese women . Because the 3′ UTRs are the primary binding and targeting site of miRNAs and recent studies have highlighted that allelic variants within the 3′ UTR could interfere with miRNA function [19]–[22] , we then investigated whether these SNPs were located near the binding sites of miRNAs targeting LIN28 . The predicted results showed that only rs3811463 was located near the target sites of several miRNAs ( Figure S1A ) . Surprisingly and interestingly , the nearest miRNA target site to rs3811463 was that of let-7 and , more importantly , this site was highly conserved across different prediction software packages . Therefore , we were very interested in whether allelic variants of rs3811463 could influence let-7-related regulation of LIN28 expression . By comparing the genomic locations of rs3811463 and the binding site of let-7 , we found that rs3811463 was located 81 bp downstream of the binding site of let-7 in the 3′ UTR of the LIN28 mRNA ( Figure 1A ) . Because the mRNA secondary structure is critical for mRNA-miRNA interactions [24] , we first examined whether genetic variants of rs3811463 could alter the local second structure of the LIN28 mRNA based on the minimum free energy ( MFE ) . RNAfold , an online RNA secondary structure prediction software , predicted that allelic variants of rs3811463 could alter the local mRNA secondary structure including that of the let-7 binding site . The minimum free energy changed from −93 . 0 kcal/mmol to −90 kcal/mmol when the nucleotide at the rs3811463 locus changed from U to C ( Figure 1B ) . A luciferase assay was then performed to determine if let-7 could differentially regulate LIN28 mRNAs with different alleles of rs3811463 . To begin with , the baseline levels of the four representative let-7 miRNAs ( let-7a , c , d , and f ) were evaluated in six common cell lines . The results revealed that two breast cancer cell lines , T47D and BT474 , express relatively lower levels of let-7 compared with other breast cancer cell lines ( Figure 1C ) . Fragments of the 3′ UTR of the human LIN28 with either a T or C at the site of rs3811463 were then subcloned into the psiCHECK2 vector , generating two reporter vectors named psiCHECK2:rs3811463T and psiCHECK2:rs3811463C , respectively ( Figure S1B ) . Different luciferase vectors ( psiCHECK2 , psiCHECK2:rs3811463T , or psiCHECK2:rs3811463C ) were co-transfected with the synthesized human let-7 mimics ( let-7a , c , d , and f ) or miRNA negative control ( mirNC ) into T47D cells . The dual luciferase assays demonstrated that artificial overexpression of let-7 in T47D could lead to differential suppression of the luciferase activity of psiCHECK2:rs3811463T and psiCHECK2:rs3811463C; the psiCHECK2:rs3811463T showed significantly lower scores of luciferase activity when compared with psiCHECK2:rs3811463C . However , even though we observed significant differences between the luciferase signal expressed by psiCHECK2:rs3811463T and psiCHECK2:rs3811463C , we found that the luciferase activities of the psiCHECK2:rs3811463C vectors did not change much after transfection with synthesized let-7 mimics , especially let-7c mimics . ( Figure 1D , top ) . To determine whether let-7 could regulate psiCHECK2:rs3811463C and the differential suppression of psiCHECK2:rs3811463T and psiCHECK2:rs3811463C was let-7 dependent , we replicated the luciferase assay with increasing amounts of synthesized let-7c . The results showed that the luciferase signal of the control psiCHECK2 vector was comparable in each group regardless of the doses of transfected let-7c ( data not shown ) . Both psiCHECK2:rs3811463T and psiCHECK2:rs3811463C vectors , on the other hand , were increasingly suppressed as the concentration of the let-7c mimics increased while psiCHECK2:rs3811463T was more significantly suppressed than psiCHECK2:rs3811463C ( Figure 1D , middle ) . The same trend was observed when we replicated the assay with mounting amounts of synthesized let-7f ( Figure S1C ) . To further confirm that the differential suppression of psiCHECK2 luciferase vectors containing different 3′ UTRs of LIN28 was the specific effect of let-7 , we performed a let-7 inhibition assay . We first replicated the luciferase assay with different psiCHECK2 vectors and let-7 mimics in HEK-293T cells . As indicated in Figure 1C , HEK-293T cell expresses a high level of endogenous let-7 , which was reflected by the luciferase assay in that the luciferase activity of psiCHECK2:rs3811463T and psiCHECK2:rs3811463C showed a significant difference even when the cells were transfected with the negative control of miRNA mimic ( mirNC ) ( Figure 1D , bottom ) . We then inhibited endogenous let-7 ( let-7c and let-7d ) in HEK-293T cells using specific let-7 inhibitors , the effects of which were confirmed by Taqman real-time PCR ( Figure S1D ) . The luciferase assay showed that a reduction in the concentration of endogenous let-7 in HEK-293T cells was able to abolish the differences in the luciferase activity of these two vectors compared with the baseline condition ( Figure 1D , bottom ) , confirming that the differential regulation of psiCHECK2:rs3811463T and psiCHECK2:rs3811463C is the specific function of let-7 . These experiments showed that let-7 could suppress expression of the luciferase gene by targeting the 3′ UTR of LIN28 and that the suppressing effect of let-7 could be weakened when the nucleotide at the rs3811463 locus changed from the ancestral T to the variant C . This discovery indicated that T/C variants of rs3811463 could lead to differential regulation of the LIN28 mRNA by let-7 miRNAs . Although previous studies have shown that LIN28 could negatively regulate let-7 , it remains unknown whether this is the same case in the molecular context of breast cancer . To address this question , we arbitrarily overexpressed LIN28 in MCF7 , SK-BR-3 , and MDA-MB-231 cells , which express relatively higher levels of let-7 ( Figure 1C ) , and evaluated the change in let-7 levels . Because the luciferase assay revealed that let-7f could more significantly repress LIN28 mRNA ( Figure 1D , top ) , we took it as a representative of other members of the let-7 family . Real-time PCR revealed that overexpression of LIN28 ( Figure 2A ) could lead to 30–60% lower levels of let-7f ( P<0 . 05 ) ( Figure 2B ) . To further investigate whether the reduction in the level of let-7f was LIN28-dependent , we infected SK-BR-3 and MCF7 cells with different volumes of unconcentrated viral supernatant containing LIN28 expression vectors . Real-time PCR results showed that there was a LIN28 dose-dependent reduction in the concentration of endogenous let-7f in SK-BR-3 and MCF7 cells ( Figure 2C ) , proving that LIN28 could inhibit the maturation of let-7 in the molecular context of breast cancer cells . Based on these findings , we proposed a biological model in which the variant rs3811463-C allele , compared with the ancestral rs3811463-T allele , can weaken the suppression of LIN28 mRNA by let-7 , leading to overexpression of LIN28 protein , which in turn reduces the level of mature let-7 and eventually results in altered status of the whole let-7/LIN28 loop . To test this model , we randomly selected 300 patients who had donated both peripheral blood and normal breast tissue and determined their rs3811463 genotypes by RFLP-based assays . From these patients , we selected 30 patients with TT genotypes and 30 patients with TC genotypes . We did not include the rs3811463-CC genotype due to the rareness of this genotype . The included patients with the TT genotype were comparable to those with the TC genotype in regard to the distribution of breast cancer risk factors and the histological characteristics of their disease ( Table S1 ) . We then examined the levels of let-7f , LIN28 mRNA , and LIN28 protein in the epithelial cells of the acquired normal breast tissue . As shown in Figure 3A , a significantly lower level of let-7f was observed in the normal breast tissue of individuals with the TC genotype than in that of women with the TT genotype ( P = 0 . 004 ) . Although the LIN28 mRNA expression level was higher in the tissue with the TC genotype than in those with the TT genotype , the difference was not statistically significant ( P = 0 . 81 ) ( Figure 3B ) . However , the level of LIN28 protein was significantly higher in the breast tissue with the TC genotype than that with the TT genotype ( P = 0 . 028 ) ( Figure 3C ) . Furthermore , lower levels of let-7f generally corresponded to higher levels of LIN28 protein ( Figure 3D ) , suggesting that let-7 regulate LIN28 mainly through inhibiting the translation , rather than through inducing the degradation , of LIN28 mRNA . Taking all these findings together , the rs3811463-C allele is correlated with a high level of LIN28 protein and a low level of let-7 in a normal physiological context , which is consistent with our model . Our results thus far indicated that genetic variants in let-7 and LIN28 genes , such as rs3811463 , might potentially alter the levels of the let-7/LIN28 loop in individuals with different genetic backgrounds . Because the increased LIN28 levels and decreased let-7 levels have been previously associated with carcinogenesis [7] , [8] , [10] , we were interested in whether such genetic variants , especially rs3811463 , were associated with the risk of breast cancer . Because an initial study failed to identify any common SNPs in let-7 genes , we conducted association studies to investigate the relationship between common SNPs in the LIN28 gene ( including rs3811463 ) and the risk of breast cancer at the molecular epidemiological level . The included SNPs were selected as described in the Materials and Methods section . In the first hospital-based case-control study , we evaluated the frequency distribution of the selected SNPs in 1 , 004 cases and 1 , 296 controls . All genotyped SNPs had a minor allele frequency ( MAF ) greater than 9% ( Table 1 ) . Based on a threshold of 0 . 05 , no significant deviation from Hardy-Weinberg Equilibrium ( HWE ) was found in the control group . As shown in Table 1 , we found that two SNPs located within the 3′ UTR of LIN28 were significantly associated with breast cancer . Encouragingly , one of these two SNPs was rs3811463 and another one was rs6697410 . The P-values continued to be significant after permutation tests or Bonferroni correction using a threshold of 0 . 05 . When compared to the ancestral rs3811463-T allele , the rs3811463-C allele was associated with an increased risk of breast cancer ( odds ratio ( OR ) , 1 . 25; 95% confidence interval ( CI ) 1 . 06–1 . 47; P = 0 . 0091 ) . In contrast , the rs6697410-G allele was associated with a reduced risk of breast cancer as compared to the ancestral rs6697410-T allele ( OR , 0 . 76; 95% CI 0 . 63–0 . 93; P = 0 . 0068 ) . The distribution of genotypes among cases and controls in the test set is shown in Table 2 . Using the common homozygous genotypes as the references , we found that the rs3811463-TC genotype correlated with an increased risk of breast cancer while the rs6697410-TG genotype was associated with a decreased risk of breast cancer . However , significant associations between the homozygous genotypes of variant alleles ( rs3811463-CC and rs6697410-GG ) and the risk of breast cancer were not found , which might be due to the rareness of these two genotypes in the population . To validate our findings in the test set , we performed another independent case-control study involving 511 cases and 645 controls . Unlike the first study , all of the controls in the validation set were recruited from the participants of a community-based breast cancer screening program as previously described [25] . In this set , the results of the initial study were successfully replicated; rs3811463-C allele was associated with an increased risk of breast cancer , while rs6697410-G was associated with a reduced risk of breast cancer . The associations were more significant when the two studies were combined together; the P value for the rs3811463-C allele was 8 . 0×10−5 , whereas it was 7 . 0×10−4 for the rs6697410-G allele . After adjusting for common breast cancer risks , these two allelic variants were still significantly associated with the risk of breast cancer ( Table 3 ) .
In the current study , we for the first time confirmed that the C allele of rs3811463 , a SNP located near the binding site of let-7 in LIN28 , can weaken the suppression of LIN28 by let-7 , disturb the let-7/LIN28 double-negative feedback loop , and ultimately result in an increased level of LIN28 protein and decreased level of let-7 . Population-based association studies further related the rs3811463-C allele with an elevated risk of breast cancer . Let-7 is comprised of a family of miRNAs that can target and repress the translation of LIN28 mRNA into LIN28 protein [11] , [12] . LIN28 protein , on the other hand , can block the maturation of let-7 [11] , [13] , [15] , [16] . Thus , these two factors form a unique double-negative feedback loop . Let-7 is a recognized tumor suppressor [3] , while LIN28 acts like an oncogene that can promote cellular transformation [8] , [10] . Deregulation of both let-7 and LIN28 has been associated with tumorigenesis [7] , [10] . Accumulating evidence has also demonstrated that the let-7/LIN28 loop can interact with other factors , such as RAS , MYC , and NF-kB [17] , [18] , to form a complex network associated with carcinogenesis . However , the mechanisms by which let-7/LIN28 loop homeostasis is maintained and whether inter-individual differences in the levels of this loop are associated with the risk of developing cancer , such as breast cancer , remain largely unknown . The results of this study suggest that germline genetic variants of LIN28 , such as with rs3811463 , may lead to altered status of the let-7/LIN28 loop and are associated with breast cancer susceptibility . As illustrated in Figure 4B , we define the green region as the normal condition , in which the nucleotide at the locus of rs3811463 is the ancestral T allele . Under this condition , let-7 can sufficiently regulate LIN28 , maintaining LIN28 protein at a relatively low level . The replacement of the T allele by the variant C allele , however , weakens the suppression of LIN28 mRNA by let-7 , resulting in overexpression of LIN28 protein , which can in turn reduce the level of let-7 . With the amplifying effect of the let-7/LIN28 double-negative feedback loop , the concentration of LIN28 protein will increase , while the level of let-7 decreases until a new balance is achieved by mechanisms that remain unknown ( Figure 4B; red circle ) . Both the increased level of LIN28 protein and reduced level of let-7 are associated with carcinogenesis , which explains the relationship between the rs3811463-C allele and an increased risk of breast cancer , as shown by the case-control studies . It should be noted that even though in this study we have proven that genetic variants of rs3811463 could influence the regulation of LIN28 by let-7 , we cannot exclude the possibility that they can also interfere with other regulatory mechanisms . The first possibility is that genetic variants of rs3811463 can interfere the functions of other miRNAs besides let-7 , especially considering that there are target sites for several other miRNAs near the binding site of let-7 according to the target prediction results . This possibility is also demonstrated in the luciferase assays , in which the luciferase activity of both psiCHECK2:rs3811463T and psiCHECK2:rs3811463C were significantly lower than those of the control psiCHECK2 vector even when no synthesized let-7 was transfected , and the concentration of endogenous let-7 is relatively low in T47D cells . A second possible mechanism is that the T/C variants of rs3811463 might alter the functional motif of other regulatory factors apart from miRNAs , even though there is currently insufficient evidence for this possibility . In addition , even though we have demonstrated that T/C variants of rs3811463 can lead to differential suppression of LIN28 mRNA by let-7 , we are still unclear about the precise mechanism of how these genetic variants influence the function of let-7 . Thus , more studies are warranted to elucidate these questions . The occurrence of human breast cancer is generally attributed to both the genetic and environment risk factors . Much effort has gone into identifying the genetic risk factors of breast cancer and previous studies have found a group of high-penetrance susceptibility genes for breast cancer , which are generally involved in DNA repair pathways . Mutations in these genes are rare but have significant effects [23] . However , these genes only account for a small proportion of the genetic risk of breast cancer . It has become increasingly evident that common low-penetrance genetic variants may have a more prominent role [27] . SNPs are the most common type of genetic variants in human genome [28] . They are the genetic basis of the inter-individual variations in heritable phenotypes , such as susceptibility to cancers . A number of SNPs have been identified by previous studies to be associated with breast cancers , many of which are located within the promoter or CDS regions of genes [25] , [29] . Our study , on the other hand , focused on miRNA-related SNPs . This study was part of a research program that aimed to identify: 1 ) common SNPs located within miRNA genes , miRNA target sites , and miRNA regulatory genes; and 2 ) the relationships between these SNPs and the risk of breast cancer . It was previously hypothesized that miRNA-related genetic variants might alter the biogenesis and/or function of their corresponding miRNAs [30] , [31] . This hypothesis has been validated by multiple studies that found that SNPs located within or near the target sites of miRNAs could influence the functions of miRNAs and were associated with human cancers [19]–[21] . It seems that this is a common mechanism [22] . In this study , we demonstrate that the genetic variants of rs3811463 , a SNP located within the 3′ UTR of LIN28 , can weaken the suppression of LIN28 by let-7 and is associated with the risk of breast cancer . Our study and previous studies all suggest that miRNA-related SNPs , especially those located near the complementary sites of miRNAs , are a new source of human cancer susceptibility loci . In the case-control studies , we also found that another SNP , rs6697410 , was associated with the risk of breast cancer . The underlying mechanism is still to be elucidated . In silico analysis showed that rs3811463 was located at the end of the 3′ UTR of LIN28 , and there was no miRNA target site nearby . We propose several possibilities . First , it is possible that other polymorphisms linked with rs6697410 are responsible for this relationship , which means that rs6697410 is a marker for another functional SNP rather than a functional SNP by itself . Second , the nearby sequence may contain target sites for certain miRNAs , but the current in silico analysis tools failed to identify them . Third , genetic variants of rs6697410 may exert their function by influencing other function motifs involved in the regulation of LIN28 [32] . Further studies are needed to address this problem . Finally , the focus of the current study is on the influence of genetic variants on the phenotypic variations of the let-7/LIN28 loop and their impact on the risk of developing breast cancer among women with different genetic backgrounds . We found that the existence of the let-7/LIN28 double-negative feedback loop might amplify even slight variations in the levels of let-7 and/or LIN28 , which could result in significant biological or even pathological effects . Based on such a finding , we hypothesize that other mechanisms that also change the levels of let-7 and/or LIN28 are equally possible to induce the same effects . These mechanisms may include an epigenetic switch , transcriptional regulation , and so on . In addition , we are also aware of the deficiencies inherent in the design of our study: the possibility that we may miss genetic variants with low allele frequencies but prominent function . Even though we have 95% power to identify SNPs like rs3811463 ( MAF = 14 . 6% and OR = 1 . 31 ) , when we combine the two studies together , the power to detect those with MAF less than 10% drops dramatically to approximately 75% . Thus , further investigations are warranted to fully evaluate the effects of other possible mechanisms as well as the effects of the less common SNPs in the LIN28 and let-7 genes .
The miRBase , the Gene and dbSNP databases of National Center for Biotechnology Information ( NCBI ) , and the International HapMap Project database were first employed to identify SNPs located within the LIN28 and human let-7 genes that had potential functional effect . The coding sequence ( CDS ) , promoter region ( defined as the 2 Kb sequence upstream of the transcriptional start site of LIN28 ) , and 3′ untranslated region ( 3′ UTR ) of LIN28 were located using Gene database of NCBI . The genes of human let-7 ( hsa-let-7 ) were determined through data curated by miRBase and confirmed through Gene database of NCBI . Because the let-7 genes acquired from miRBase and NCBI actually are those encode the precursors of let-7 , we further identified the 5′ and 3′ flanking sequences of 100 bp long of these genes . SNPs located within the above-identified sequences were searched in the dbSNP database of NCBI and International HapMap Project database . The criteria for SNP selection was based on: 1 ) minor allele frequency ( MAF ) higher than 5% in Han Chinese in Beijing ( CHB ) according to HapMap database in order to be appropriate for including in the following case-control studies and 2 ) have potential functional effect based on the in silico analyzing methods as listed below . TESS ( http://www . cbil . upenn . edu/cgi-bin/tess/tess ) and TFSEARCH ( http://www . cbrc . jp/research/db/TFSEARCH . html ) were used to analyze the binding sites of the transcription factors . TargetScanHuman 5 . 1 ( http://www . targetscan . org/ ) , PicTar ( http://pictar . mdc-berlin . de/ ) , and MicroCosm Targets Version 5 ( http://www . ebi . ac . uk/enright-srv/microcosm/htdocs/targets/v5/ ) were employed to predict the target sites of miRNAs . RNAfold software ( http://rna . tbi . univie . ac . at/ ) was used to calculate the secondary structure of RNA based on minimum free energy ( MFE ) . An 885-bp fragment of the 3′ UTR of human LIN28 gene centering rs3811463 and the predicted complementary site of let-7 was subcloned into the psiCHECK2 vector ( Promega , Madison , WI , USA ) using the Xhol and NotI restriction sites located 3′ to the Renilla luciferase translational stop codon . A mutant plasmid with C allele at the site of rs3811463 was constructed using a site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) . All vectors were verified by direct sequencing . The generated reporter vectors were named psiCHECK2:rs3811463T and psiCHECK2:rs3811463C , respectively . To conduct the luciferase reporter assay , T47D and HEK-293T cells were cultured in 96-well plates at 4 , 000 cells/well . After an overnight incubation , each well was treated with a transfection mixture consisting of 150 µl of Opti-MEM ( Invitrogen , Carlsbad , CA , USA ) , 0 . 75 µl of Lipofectamine2000 ( Invitrogen , Carlsbad , CA , USA ) , 10 pmol of synthesized miRNA [negative control ( mirNC ) or let-7 mimics ( let-7a , c , d , or f ) ( GenePharma , Shanghai , China ) ] and 0 . 1 µg of psiCHECK2 vector ( empty , psiCHECK2:rs3811463T , or psiCHECK2:rs3811463C ) . After a five-hour incubation , medium in each well was replaced by 150 µl of serum-containing medium . After 48 h , the Renilla and firefly luciferase activities were measured by the Dual-Luciferase Reporter Assay ( Promega , Madison , WI , USA ) on a Veritas Microplate Luminometer ( Turner BioSystems , Sunnyvale , CA , USA ) according to the manufacturers' protocol . To determine whether the differential suppression of the luciferase activities of psiCHECK2:rs3811463T and psiCHECK2:rs3811463C was let-7 dependent , similar luciferase reporter assay was performed in T47D cells with mirNC and different doses of synthesized let-7c ( 5 pmol–25 pmol ) or let-7f ( 10 pmol–20 pmol ) . The let-7 inhibiting assay was performed by transfecting HEK-293T cells with a transfection mixture consisting of 150 µl of Opti-MEM ( Invitrogen , Carlsbad , CA , USA ) , 0 . 75 µl of Lipofectamine2000 ( Invitrogen , Carlsbad , CA , USA ) , 20 pmol of let-7 inhibitors [negative control ( anti-mir-con ) or anti-let-7c plus anti-let-7d ( 10 pmol , respectively ) ( GenePharma , Shanghai , China ) ] and 0 . 1 µg of different psiCHECK2 vector ( empty , psiCHECK2:rs3811463T , or psiCHECK2:rs3811463C ) . The remaining procedures were identical to those of the above-mentioned luciferase assays . Each experiment was performed at least three times in triplicate . The luciferase score was calculated by normalizing the luciferase signal of Renilla to that of firefly . Fold change ( relative luciferase activity ) was reported by setting the scores of the control groups as one and normalizing the scores in other groups . Normal breast tissue was donated by patients who had undergone a mastectomy in the Department of Breast Surgery after completing a written informed consent document . The fresh breast tissue was obtained immediately after surgery , snap-frozen in liquid nitrogen , and stored at −80°C . Careful dissection and collection of normal epithelial cells from the fresh breast tissue was achieved by employing laser-capture microdissection ( Veritas Automated Laser Microdissection System ) as previously described [33] . Total RNA was extracted from cultured cells or derivatives of fresh breast tissue using TRIzol ( Invitrogen , Carlsbad , CA , USA ) and cDNA was synthesized with the RevertAid First Strand cDNA Synthesis Kit ( Fermentas , Vilnius , Lithuania ) according to the manufacturers' protocols . Real-time PCR evaluation of LIN28 mRNA was performed using the SYBR Green fluorescent-based assay ( Takara Bio , Shiga , Japan ) in triplicate as previously described [25] . GAPDH mRNA was used for normalization . The small RNA was isolated and enriched using the mirVana miRNA isolation kit ( Ambion , Austin , TX ) . RNA ( 10 ng ) was used for synthesizing cDNA of let-7 and U6 control using the reverse transcription ( RT ) primers ( Applied Biosystems , Foster City , CA , USA ) . The RT was performed with the TaqMan MicroRNA Reverse Transcription Kit according to the manufacturer's protocol ( Applied Biosystems , Foster City , CA , USA ) . Real-time PCR was performed with corresponding TaqMan primers and probes ( Applied Biosystems , Foster City , CA , USA ) in triplicate using the Opticon Real-time PCR system ( Bio-Rad MJ Research , Waltham , MA , USA ) , according to the protocols provided by the manufacturers . Western blot was performed as previously described [25] using an antibody against the human LIN28 protein ( ab46020 ) ( Abcam , Hong Kong ) . GAPDH protein was used as the loading control . The Western blot results were quantified with Quantity One ( Bio-Rad Laboratories , Hercules , CA USA ) . SNPs spanning a ∼21 . 5-kb region from 2 kb upstream to 0 . 5 kb downstream of the LIN28 gene were surveyed in the International HapMap Project database ( HapMap Data Rel 27 Phase II+III , Feb 09 , on NCBI B36 assembly ) . The International HapMap Project had genotyped a large number of SNPs in different populations and provided a set of tag SNPs ( tSNPs ) which could efficiently represent evolutionally linked genetic variants [28] . Using the Tagger Pairwise method with an r2 cutoff of 0 . 8 and MAF cutoff of 0 . 05 in Han Chinese population , we selected five tSNPs ( rs12122703 , rs3811464 , rs1247955 , rs3811463 , and rs6697410 ) which represent 68 . 8% ( 11/16 ) of the common SNPs in the LIN28 gene . The five excluded SNPs were located within the introns and they did not tag any other SNP . The selected tSNPs can tag all the SNPs located in the promoter and 3′ UTR of LIN28 . Two SNPs ( rs12122703 and rs3811464 ) were located in the 5′ flanking region ( promoter ) of the LIN28 gene , another two SNPs ( rs3811463 and rs6697410 ) in the 3′ UTR , and the remaining SNP ( rs11247955 ) was located in the intron ( Figure S2A ) . The relationship between the common SNPs of LIN28 and the risk of breast cancer was investigated through two case-control studies in a stepwise manner . All the participants were genetically unrelated Han Chinese living in Shanghai City and its surrounding areas . The first hospital-based case-control study consisted of 1 , 004 patients with pathologically-confirmed primary breast cancer and who were consecutively recruited from the Department of Breast Surgery at Fudan University Shanghai Cancer Center ( FDUSHCC ) between January 2004 and June 2008 . During the same period , 1 , 296 controls , matched to the cases by age and living regions , were enrolled from amongst women who had come to the Outpatient Department of FDUSHCC for breast cancer screening . In the validation study , we recruited an additional 511 patients through the same process between December 2008 and July 2009 and 645 control subjects who were frequency matched to the cases by age . These control subjects were from an on-going community-based breast cancer screening program that has been previously described [25] . Participants with a previous history of cancer ( except breast cancer ) and metastatic breast cancer were excluded from our study . All the controls were determined as cancer-free after comprehensive examinations . Table S3 presents the detailed baseline characteristics of the subjects enrolled in this study . After finishing a written informed consent document , each participant was carefully interviewed to obtain epidemiological information . Each participant donated approximately 3–5 ml of peripheral venous blood , which was collected in tubes with either EDTA or heparin anticoagulant . This study was approved by the Ethics Committee of FDUSHCC . All clinical investigations have been conducted according to the principles expressed in the Declaration of Helsinki . The patients and controls in both the test set and the validation set were comparable in age ( mean age was 48 . 2 years for the cases and 48 . 5 years for the controls in the first set; mean age was 48 . 6 years for the cases and 48 . 4 years for the controls in the validation set ) and menopausal status ( proportion of postmenopausal women was 37 . 1% for the cases and 24 . 7% for the controls in the test set; the proportion of postmenopausal women was 33 . 1% for the cases and 36 . 3% for the controls in the validation set ) . When compared with the controls , the patients in both sets were younger at menarche , and they had a higher body mass index ( BMI ) and fewer numbers of children ( P<0 . 05 ) . Furthermore , a significant number of these patients had a family history of first-degree relatives with breast cancer . These differences partially reflected some of the risk factors for breast cancer . The clinicopathologic record of each patient was acquired via the Patient Database of FDUSHCC . The status of estrogen receptor ( ER ) , progesterone receptor ( PR ) , and human epidermal growth factor receptor 2 ( HER2 ) was determined among patients with available data . The breast cancer IHC subtypes were defined as previously described [34] , only our classification was based on the IHC results for the above mentioned three markers ( ER , PR and HER2 ) , and we merged luminal A with luminal B as the luminal subtype . Thus we defined breast cancer subtypes as: luminal ( ER+ and/or PR+ ) , basal-like ( ER− , PR− , and HER2− ) , and HER2+/ER− subtype ( HER2+ , ER− , PR− ) . Genomic DNA was extracted from the blood leukocytes of the participants using Gentra's PureGene DNA Purification Kit ( Gentra systems , Minneapolis , MN , USA ) . Genotyping was done mainly using the 12-plex SNPstream system ( Beckman Coulter , Fullerton , CA , USA ) at the Chinese National Human Genome Center at Shanghai . SNPs that could not be genotyped by this platform due to the inability to design appropriate primers or probes were genotyped by PCR and RFLP-based assays ( Text S1 , Figure S2B and Table S4 ) . To ensure the reliability of the results , operators performing the genotyping assays were unaware of the disease status of each sample , and each batch of samples contained at least one positive control consisting of DNA samples with known genotype and two negative controls of pure water . Two researchers ( K . D . Yu and A . X . Chen ) independently examined the gel pictures of the RFLP assays . The assays were redone if there were inconsistencies regarding the results . Samples with disputable outcomes in the second test were directly sequenced . Ten percent of the samples were randomly selected for repeated RFLP assays , and the results were 100% concordant . The immunohistochemistry ( IHC ) assay was performed as previously described [35] using an antibody specific for LIN28 ( ab46020 ) ( Abcam , Hong Kong ) . The results were scored based on the percentage of tumor cells positively stained as follows: low expression required less than 15% of positive cells , moderate expression required 15–60% positive cells , and high expression required greater than 60% positive cells . The cell lines ( MDA-MB-231 , MCF7 , T47D , SK-BR-3 , BT-474 , MCF 10A , and HEK 293T ) were obtained from the American Type Culture Collection ( ATCC ) and maintained in complete growth medium as recommended by the distributor . Liquid nitrogen stocks were made upon receipt , and they were maintained until the start of each study . Morphology and doubling times were also regularly recorded to ensure maintenance of phenotypes . Cells were used for no more than 6 months after being thawed . The CDS of human LIN28 was cloned into the pCDH-CMV-MCS-EF1-Puro expression vector ( System Biosciences , Mountain View , CA , USA ) and confirmed by direct sequencing ( primers listed in Table S5 ) . The empty or LIN28-CDS-fused pCDH vectors were then packaged into VSV-G-pseudotyped viral particles using the pPACKH1 Lentivector Packaging Kit ( System Biosciences , Mountain View , CA , USA ) according to the manufacturer's protocol . The unconcentrated viral supernatant was collected for infection . MCF 10A cells stably transfected with LIN28 were acquired by infecting approximately 50 , 000 MCF 10A cells with 1 ml of unconcentrated viral supernatant , and they were selected in medium containing 3 ng/µl puromycin . Soft agar assays were performed as previously described [36] . Briefly , approximately 6 , 000 MCF 10A cells stably transfected with LIN28 or with empty vector were cultured in soft agar media ( Promega , Madison , WI , USA ) for three weeks before being stained with 0 . 05% crystal violet and photographed . The colony number was counted using Quantity One ( Bio-Rad Laboratories , Hercules , CA USA ) . The program Quanto ( http://hydra . usc . edu/gxe ) was used to estimate the statistical power of our study . A dominant model was adopted and a range of allelic frequencies , odds ratios , prevalence of breast cancer observed in the studied population ( 25 in 100 , 000 ) , and the sample size of 1 , 004 cases ( accompanied by 1 , 296 controls ) in the first case-control study were taken as the parameters . For alleles with minor allele frequencies of 15%–20% , our study has more than 95% power to detect alleles with ORs higher than 1 . 4 and more than 81% power to detect those with ORs of 1 . 3 . The power drops for alleles with lower frequencies; for those with 10% frequency , the power to identify effects higher than 1 . 3 is 71% . When combining the two case-control studies together , we have more than 99% power to detect alleles with MAF higher than 15% and ORs of 1 . 4 and more than 93% power for alleles with ORs of 1 . 3 and MAF higher than 15% . HWE was tested by χ2 tests for each SNP locus . The associations between alleles and breast cancer risk were determined by Pearson's χ2 test , and the P-values were corrected using the 1 , 000-time permutation test [37] or Bonferroni correction . Logistic regression was used to analyze the associations between genotypes and breast cancer risk . The crude OR and OR adjusted for age , age at menarche , menopause status , number of birth , BMI and family history of breast cancer ( in first- and second-degree relatives ) , along with 95% CI , were also determined . The distribution of rs3811463 genotypes in patients with different subtypes of breast cancer was analyzed by Pearson's χ2 test . Cochran-Mantel-Haenszel χ2 test was employed in association tests involving ordered variables . Student's t-test or Mann–Whitney U test was employed to compare continuous variables between two groups . One-way ANOVA or Kruskal–Wallis analysis was used to compare continuous variables among three or more groups . A P-value of no more than 0 . 05 was considered statistically significant . Statistical analysis was performed using Stata 10 . 0 ( StataCorp , College Station , TX , USA ) and SPSS Software version 12 . 0 ( SPSS Inc . , Chicago , IL , USA ) . The structure of the linkage disequilibrium ( LD ) block was constructed using Haploview 4 . 2 and showed in Figure S2C [37] .
|
Genetic variants are the genetic basis of inter-individual differences in susceptibility to common diseases like cancer . Single nucleotide polymorphisms ( SNPs ) are the major type of genetic variation in human beings , and previous studies have linked many SNPs with the risk of suffering human malignancies . In this study , we focused on microRNA ( miRNA ) –related SNPs and their relationship with the risk of breast cancer . MiRNAs are a kind of tiny endogenous RNAs that play important regulatory roles . Let-7 is a miRNA that has been proven to be capable of inhibiting the tumor occurrence . LIN28 , a gene that promotes cancer , is a known regulator of let-7 and , interestingly , LIN28 itself is subject to repression by let-7 . We have discovered that an SNP located near the target site of let-7 in LIN28 disturbs let-7's regulation of LIN28 . This disturbance eventually leads to an increased level of LIN28 and a decreased level of let-7 . We also , for the first time , confirmed that this SNP is associated with an increased risk of breast cancer in Han Chinese women . Our results suggest that genetic variants may potentially build a bridge from a physiological status to an aberrant , even pathological , status with the let-7/LIN28 loop in breast tissue , thus , facilitating carcinogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"oncology",
"medicine",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"genetics",
"and",
"genomics",
"clinical",
"genetics"
] |
2011
|
Germline Genetic Variants Disturbing the Let-7/LIN28 Double-Negative Feedback Loop Alter Breast Cancer Susceptibility
|
Cys-loop receptors ( CLR ) are pentameric ligand-gated ion channels that mediate fast excitatory or inhibitory transmission in the nervous system . Strychnine and d-tubocurarine ( d-TC ) are neurotoxins that have been highly instrumental in decades of research on glycine receptors ( GlyR ) and nicotinic acetylcholine receptors ( nAChR ) , respectively . In this study we addressed the question how the molecular recognition of strychnine and d-TC occurs with high affinity and yet low specificity towards diverse CLR family members . X-ray crystal structures of the complexes with AChBP , a well-described structural homolog of the extracellular domain of the nAChRs , revealed that strychnine and d-TC adopt multiple occupancies and different ligand orientations , stabilizing the homopentameric protein in an asymmetric state . This introduces a new level of structural diversity in CLRs . Unlike protein and peptide neurotoxins , strychnine and d-TC form a limited number of contacts in the binding pocket of AChBP , offering an explanation for their low selectivity . Based on the ligand interactions observed in strychnine- and d-TC-AChBP complexes we performed alanine-scanning mutagenesis in the binding pocket of the human α1 GlyR and α7 nAChR and showed the functional relevance of these residues in conferring high potency of strychnine and d-TC , respectively . Our results demonstrate that a limited number of ligand interactions in the binding pocket together with an energetic stabilization of the extracellular domain are key to the poor selective recognition of strychnine and d-TC by CLRs as diverse as the GlyR , nAChR , and 5-HT3R .
Strychnine and d-TC ( Figure 1A ) are alkaloids from poisonous plants . Strychnine exerts its lethal effects by antagonizing inhibitory glycine receptors ( GlyR ) in the central nervous system . Intoxication with strychnine causes muscle spasms , convulsions and eventually leads to death by respiratory paralysis . Clinical use of strychnine is restricted , but it is still applied as a rodenticide . Unlike strychnine , curare is not a homogenous substance but a cocktail of compounds derived from different plant families . One of the best-described active compounds is d-tubocurarine ( d-TC ) , a quaternary head-to-tail tetrahydroisoquinoline that potently antagonizes the action of acetylcholine on muscle-type [1] , [2] and neuronal [3] , [4] nAChRs . Intoxication leads to complete paralysis of all skeletal muscles and death by respiratory paralysis . In the Western world d-TC analogs have been used in anesthesia as a muscle relaxant during surgery . In addition to their clinical use d-TC and strychnine have been essential molecular tools for the pharmacological characterization of different cys-loop receptors ( CLR ) . d-TC and strychnine act as competitive antagonists with very high affinity for nAChRs and GlyRs , respectively . However , their actions extend to other members of the CLR family . For example , d-TC antagonizes the action of serotonin on 5-HT3 receptors [5] , [6] . Strychnine mainly blocks the inhibitory GlyR but also antagonizes certain GABAA receptors [7] and nAChRs [8] , [9] . This mode of action strikingly differs from that of protein and peptide neurotoxins such as α-bungarotoxin and α-conotoxins , which in general bind with high affinity and specificity to distinct subtypes of nAChRs , and not to other CLRs . Our understanding of the molecular action of d-TC and strychnine derives from decades of research including ligand competition assays , receptor labeling , electrophysiological studies , and site-directed mutagenesis [1] , [2] , [5] , [10]–[19] . Mutational analysis of the homomeric α1 GlyR revealed several residues in the extracellular ligand-binding domain important for agonist and antagonist binding ( reviewed in [20] , [21] ) . Additional evidence for amino acids involved in strychnine binding comes from the identification of a single amino acid substitution in the neonatal-specific α2 GlyR that renders newborn rats insensitive to strychnine poisoning [22] . Recently , Grudzinska et al . described the contribution of several key residues to strychnine binding in the β-subunit of heterooligomeric α1β GlyR [23] . Mutational analysis of conserved aromatic residues of nAChRs demonstrated their importance for binding of curariform antagonists [1] , [10] . Recently , Gao et al . [24] characterized an extensive set of mutants in acetylcholine binding protein ( AChBP ) , a structural and functional homolog of the extracellular domain of the nAChR ( Figure 1B ) [25] . Mutagenesis experiments in AChBP [24] and muscle-type nAChR [26] were based on the ligand-receptor contacts observed in docking simulations of d-TC- and metocurine-complexes with AChBP . In this study we addressed a question that is fundamental to CLR function and that is how the binding cavities of CLRs as diverse as the nAChR , GlyR , and 5-HT3R recognize inhibitors such as strychnine and d-TC with high affinity but low specificity . In particular , we investigated the molecular determinants of ligand recognition of these inhibitors . For this , we co-crystallized AChBP with d-TC and strychnine . These structures enabled identification of the ligand-binding modes and contacts formed in the receptor pocket and , complemented with computational simulations , revealed the dynamic effects of antagonist binding . Mutagenesis and electrophysiological recordings of human GlyRs and nAChRs were then used to test the functional relevance and predictive value of these models . Together , our study provides a blueprint for the molecular recognition of poorly selective alkaloid antagonists at different CLRs .
To investigate the validity of AChBP as a model to understand binding of strychnine and d-TC to CLRs we determined the affinity of these ligands for Aplysia californica AChBP ( Ac-AChBP ) [27] , a preferred homolog for structural studies . From competitive binding assays with 3H-epibatidine and 3H-methyllycaconitine we calculated Ki-values for strychnine and d-TC ( Table 1 ) . The affinity of strychnine for Ac-AChBP ( Ki = 38 . 0±3 . 3 nM ) is more than 100-fold higher than for α7 nAChR ( Ki = 4 , 854±133 nM ) and is actually close to the high affinity of strychnine reported for the α1 GlyR ( Ki = 16±2 nM ) . This suggests that AChBP is an appropriate model to predict binding of strychnine to the nAChR as well as to the GlyR . Similarly , we found that the affinity of d-TC for Ac-AChBP ( Ki = 509 . 2±38 . 0 nM ) is in the same range as the reported values for binding of d-TC at muscle nAChR [1] and the mouse 5-HT3R [28] . Together , the high affinity binding of strychnine and d-TC makes AChBP suitable for structural studies of ligand-binding modes of these ligands by X-ray crystallography . Crystallization of Aplysia AChBP with strychnine or d-TC gave co-crystals that diffracted at 1 . 9 Å and 2 . 0 Å resolution , respectively , and provided a highly detailed view of their binding modes . Crystallographic data are reported in Table S1 . The crystal structure of Aplysia AChBP in complex with strychnine contains 1 pentamer in the asymmetric unit ( Figure 2A ) . Similar to other AChBP co-crystal structures ( e . g . pdb code 2c9t ) the symmetry packing in this crystal form is characterized by an interaction of neighboring pentamers through an interface formed by two C loops ( Figure 2A ) . Inspection of simple difference electron density unambiguously revealed the ligand orientation in all five binding sites of the pentamer ( Figure 2B ) . Remarkably , one of the two C loops involved in forming a crystal contact is in a more extended conformation and reveals electron density for a second strychnine molecule in the same binding site . The second strychnine molecule has a B-factor = 60 . 67 Å2 compared to an average B-factor = 26 . 24 Å2 for all five other strychnine molecules , indicating that the additional strychnine molecule has a more disordered binding mode . The first strychnine molecule , which contacts the ( + ) face , is pivoted by 48° around the N-atom relative to the strychnine molecule in all other four binding sites ( Figure 2C , single occupancy in yellow and double occupancy in magenta ) . The second strychnine molecule , which contacts the ( − ) face , is stacked onto the first molecule in a mirrored upside-down orientation and both molecules are separated by a distance of 3 . 6 Å ( Figure 2B ) . In the binding site with double occupancy , the tip of loop C is displaced outward by a distance of 5 . 6 Å relative to the binding sites with single occupancy ( Figure 2C ) . Together , these results show that strychnine molecules , which have a rigid structure , can adopt very distinct but fixed binding orientations in each of the five binding pockets . For the first time we observe the presence of two ligands in the binding pocket of AChBP . Because the C-loop interacting with 2 strychnine molecules also interfaces with a C-loop from a neighboring pentamer the double strychnine occupancy might be the result of a crystal contact . However , interactions between neighboring pentamers comparable to those observed for AChBP may also occur under physiological conditions for intact GlyR and nAChR since these receptors are densely clustered at the neuronal synapse [29] , [30] . We also determined the co-crystal structure of Aplysia AChBP in complex with d-TC in order to verify whether multiple occupancies and distinct ligand orientations are a common property among these alkaloid antagonists . The structure of this complex was solved from diffraction data to 2 . 0 Å . The asymmetric unit contains two pentamers , which interact through loop C and form an interface that resembles the one seen in the strychnine complex ( Figure 2D ) . Inspection of simple difference electron density revealed occupancy of most of the binding sites by d-TC . Remarkably , at least three different binding orientations of d-TC can be distinguished ( indicated with binding mode 1–3 , Figure 2D ) . Binding mode 1 ( yellow , Figure 2E ) occurs at most binding sites , but with ligand occupancies that vary between 30% and 60% . d-TC molecules were not built in binding sites if the electron density indicated partial occupancy . The binding orientation for these ligands likely corresponds to binding mode 1 but were left unlabeled in Figure 2D . In binding mode 1 , the tertiary amine group of d-TC forms a hydrogen bond with the carbonyl oxygen of W145 and forms cation-π interactions with conserved aromatic residues of the binding pocket . A second ligand orientation ( mode 2 , magenta , Figure 2E ) occurs only once in the pentamer and is characterized by a polar interaction between the quaternary amine group of d-TC and the carbonyl oxygen of W145 . In addition to the upside-down orientation relative to binding mode 1 , this ligand is also rotated by 74° toward the ( + ) face . A superposition of the ( + ) face in binding mode 1 ( protein shown in yellow , Figure 2E ) and the ( + ) face in binding mode 2 ( protein and ligand shown in magenta , Figure 2E ) shows that the different ligand orientation in mode 2 results in an outward displacement of the tip of loop C by a distance of 4 . 5 Å relative to mode 1 . Finally , difference electron density at one of the binding sites involved in a crystal contact between neighboring C loops indicates the occupancy by a single ligand likely adopting multiple binding orientations . This ligand could be acceptably built into the clearest part of the density and is represented as binding mode 3 ( shown in green , Figure 2E ) . This ligand is rotated by 140° around the tertiary isoquinoline moiety relative to d-TC in binding mode 1 . Together , these data demonstrate that rigid molecules like d-TC and strychnine can adopt different binding orientations in each of the five equivalent binding sites of the receptor . Loop C , which forms part of the binding site , adopts a more contracted or extended conformational state depending on the binding orientation of the ligand , also when not involved in a crystal contact . Remarkably , this stabilizes AChBP in a structurally asymmetric state even though this pentameric protein is composed of identical subunits . These data imply that potentially a level of functional diversity of CLRs may arise , which depends on receptor occupancy , and that would add to diversity arising from homomeric and heteromeric assemblies of the α- and non-α subunits bearing intrinsic pharmacological differences . The conformational state of loop C and its contraction around the ligand was quantified by measuring the distance between the carbonyl oxygen atom of W145 and the γ-sulfur atom of C188 in each subunit of the pentamer . In the strychnine complex this average distance is 9 . 90±1 . 59 Å , compared to 11 . 80±1 . 32 Å in the complex with d-TC ( indicated with an asterisk in Figure 3A ) . For epibatidine , an agonist for the nAChRs , this distance is 6 . 88±0 . 16 Å and for α-conotoxin ImI , a subtype-specific antagonist for nAChRs , this distance is 14 . 38±0 . 13 Å ( Figure 3A and 3B ) . A comparative analysis of C-loop conformations for all agonists , partial agonists , and antagonists currently co-crystallized with AChBP reveals several features . First , for most ligands a correlation exists between the extent of C-loop closure observed in AChBP structures and the ligand mode of action at nAChRs . Ligands that act as antagonists ( shown in red bars ) typically displace loop C outward by a distance of 10–15 Å relative to the conserved Trp of loop A , whereas agonists ( shown in green bars ) cause a contraction of loop C around the ligand and reduce this relative distance to <8 Å . Second , partial agonists cause an intermediate contraction of 8–10 Å and typically induce larger variations in the extent of C-loop contraction in different subunits of the pentamer when compared to full agonists and antagonists . This variation can at least in part be explained by the occurrence of different ligand orientations for partial agonists [31] . Third , the position of the C-loop is not a strict predictor for the ligand mode of action at nAChRs because the C-loop closure for some partial agonists overlaps the diffuse boundaries that define agonists versus antagonists ( e . g . DMXBA and 4OH-DMXBA ) . Additionally , lobeline , which acts as a partial agonist at nAChRs [32] , causes an exceptionally strong C-loop closure . Together , this comparative analysis rationalizes results obtained from more than 30 co-crystal structures of AChBPs determined to date and shows in general a good correlation between C-loop contraction and predicted ligand action at nAChRs . Finally , multiple ligand orientations are not an exclusive property of partial agonists as suggested by Hibbs et al . ( 2009 ) [31] because antagonists such as strychnine and d-TC also show multiple conformations . Next , we examined the molecular contacts between ligand and receptor in more detail , and these results were compared for strychnine and d-TC . First , strychnine forms contacts with the following residues on the ( + ) face: Y91 , S144 , W145 , C188 , C189 , Y193 and ( − ) face: Y53 , Q55 , M114 , I116 , D162 , S165 ( Figure 4A , Table S2 ) . Three additional contacts are formed in the binding site with double ligand occupancy , namely Y186 on the ( + ) face and T34 , R57 on the ( − ) face ( Figure 4B ) . In comparison , d-TC forms contacts that are remarkably similar . In binding mode 1 ( Figure 4C , Table S2 ) , d-TC interacts with Y91 , S144 , W145 , C188 , C189 , Y186 , and Y193 on the ( + ) face . An additional hydrogen bond is formed with E191 , an interaction not seen in the strychnine-complex . On the ( − ) face d-TC forms contacts with T34 , Y53 , Q55 , M114 , I116 , and S165 ( Figure 4C ) . In binding mode 2 , d-TC forms an additional hydrogen bond with K141 on the ( + ) face . Due to the different ligand orientations , two hydrogen bonds are formed with Y193 and Q55 ( Figure 4D ) . These findings demonstrate that strychnine and d-TC , which differ in chemical and three-dimensional structure , have a large overlap in molecular contacts in the AChBP binding pocket . Also , the set of interactions formed by strychnine and d-TC has a much wider range compared to the agonist nicotine ( Table S2 ) , which interacts with Y89 , W143 , T144 , C187 , and C188 on the ( + ) face and W53 , L112 , and M114 on the ( − ) face ( residues and numbering correspond to the Lymnaea AChBP complex with nicotine , pdb code 1uw6 ) . Comparison with residue contacts formed by α-conotoxin ImI , which has high selectivity for distinct nAChR-subtypes ( pdb code 2c9t [33] ) , demonstrates that this peptide antagonist forms residue contacts that overlap with interactions seen for strychnine and d-TC but extend to a much wider range of residues ( Table S2 ) . α-Conotoxin ImI forms contacts with the ( + ) face: Y91 , S144 , W145 , V146 , Y147 , Y186 , C188 , C189 , E191 , Y193 , and I194 ( exclusive contacts are underlined ) . On the ( − ) face contacts are formed with Y53 , Q55 , R57 , D75 , R77 , V106 , T108 , M114 , I116 , D162 , and S164 . Thus , the high subtype-selectivity of α-conotoxin ImI likely arises from a broad range of contacts that are only found in specific nAChR subtypes [33] . In contrast , the poorly selective recognition of strychnine and d-TC may arise from similar interactions in binding pockets of different CLRs . For example , residues of loop A and loop C show good sequence conservation among most members of the CLR family . Specifically for loop E , residues homologous to M114 and/or I116 are well conserved between the α7 nAChR ( Q117 ) and 5-HT3R ( Q123 ) , which are both inhibited by d-TC . Good conservation also exists between the α1 GlyR ( L127/S129 ) and α1 GABAA-R ( L128/T130 ) , which are both inhibited by strychnine . Are the observed ligand orientations and molecular contacts in AChBP representative for our understanding of antagonist recognition in human CLRs ? To address this question , we performed alanine-scanning mutagenesis of the homologous residues in the human GlyR or nAChR using structure-based sequence alignments with AChBP ( see Figure S1 ) . The two-electrode voltage-clamp technique was used to measure ligand potency on wild type and mutant GlyR or nAChR expressed in Xenopus oocytes . For strychnine , we characterized homologous contact mutants in the human α1 GlyR because it is the primary target that mediates the physiological effects of strychnine . Results of this comprehensive mutagenesis study are summarized in Table 2 . We found that strychnine displays a decrease in potency for most α1 GlyR mutants by 1–2 orders of magnitude . F159A and F214A were not functional . Critical contact residues are located in loop B ( S158 , homologous to S144 in AChBP ) and loop D ( F63 and R65 , homologous to Y53 and Q55 in AChBP , respectively ) . Mutation of residue S158 in loop B of the GlyR ( this study ) causes a 300-fold decrease in strychnine-potency , whereas mutation F63A and R65A in loop D [23] cause a 250- and 3-fold decrease in strychnine-potency , respectively . Additionally , mutation of contacts in loop A ( A101 , homologous to Y91-AChBP ) and loop E ( L127 and S129 , homologous to M114- and I116-AChBP ) cause a ∼10-fold drop in potency . Mutation of Q177 in loop F ( homologous to D162-AChBP ) causes an apparent increase in strychnine-potency . To investigate the relevance of two strychnine molecules occupying a single binding pocket we investigated the effect of mutations at positions homologous to residues involved in unique contacts in the double strychnine occupancy mode , namely T34 , R57 , and Y186 in AChBP . The IC50-values for the homologous mutants in the α1 GlyR , F44A , and Q67A and F207A , are 3- , 15- , and 119 , 444-fold higher than wild type receptor , respectively . One of these mutants , F207A , was previously characterized in the work from Grundzinska et al . [23] . Such a profound effect of F207A could be expected if the interactions occur as observed for the first strychnine molecule under double occupancy binding mode . The profound effects of these mutations on strychnine inhibition , in particular F207A , suggest that the double occupancy binding mode of strychnine as observed in the crystal structure is biologically relevant . For d-TC , previous mutagenesis studies have been carried out either on the muscle-type nAChR [1] or 5-HT3R [5] , [12] . The heteropentameric muscle-type nAChR contains two different binding sites for d-TC with a 100-fold difference in affinity , whereas differences between human and mouse 5-HT3R yield a more than 1 , 000-fold difference in affinity . Both model systems either complicate the mutational analysis or make it difficult to derive conclusions that can be generalized to other CLRs . Here , we chose the human α7 nAChR to characterize the effect of homologous contact mutations on the potency of d-TC . The α7 nAChR , which is also a target of d-TC [4] , is closely related to Ac-AChBP and is an attractive model system to extrapolate the results obtained from our X-ray crystal structures . Fifteen potential contacts in α7 nAChR were mutated and seven of these yielded a non-functional receptor , most of which are mutants of loop C . Similar to strychnine , crucial contacts are localized in loop B ( S148 , homologous to S144-AChBP ) and loop D ( W55 , homologous to Y53-AChBP ) , which display a 148-fold and 14-fold drop in d-TC-potency , respectively . Mutations in other loops of the α7 nAChR have moderate to no effect ( S36A , Y93A , Q117A , L119A , and E193A—homologous to T34- , Y91- , M114- , I116- , and E191-AChBP ) . Two mutants , namely G167A in the α7 nAChR and Q177A in the α1 GlyR ( homologous to D162-AChBP ) , showed a significant decrease in the IC50-values for d-TC or strychnine compared to the wild-type receptor ( p<0 . 05 ) . A possible explanation for this observation is that these ligand interactions are energetically unfavorable in the wild type receptors . Together , these AChBP-directed mutagenesis analyses pinpoint crucial interactions of strychnine and d-TC to residues in loop B and loop D of the α1 GlyR and α7 nAChR . This indicates that strychnine and d-TC form similar interactions in different classes of CLRs , which parallels our observation from the X-ray crystal structures with AChBP . X-ray crystal structures of AChBP provide static snapshots of a receptor that undergoes highly dynamic changes upon ligand binding [34] . To investigate the validity of AChBP crystal structures and their different ligand binding modes observed in this study , we simulated the dynamic behavior of AChBP complexes with strychnine and d-TC and compared the calculated ligand orientations with those observed in co-crystal structures of AChBP . Simulation of an interface occupied either by a single strychnine molecule or two strychnine molecules shows that the system quickly resolves to equilibrium , indicating that a thermodynamically equilibrated conformation is obtained . Introduction of the ligand into an equilibrated conformation of the unliganded AChBP demonstrates that an induced fit is obtained within 9 to 12 ns ( available as Movie S1 ) . Superposition of simulated conformations and X-ray crystal structures showed only subtle changes characterized by an RMSD = 1 . 6 Å for strychnine in single occupancy , 1 . 8 Å for strychnine in double occupancy , 1 . 4 Å for d-TC in binding mode 1 , and 3 . 2 Å for d-TC in binding mode 2 ( Figure S2 , panel A , B , and C ) . Additionally , the system equilibrium was evaluated for each simulation as described in [35] . The equilibrium timeframe was truncated by 3 ns from the beginning along the time coordinate to avoid boundary artifacts . Interaction energy was measured for the ligand using thermodynamic integration in one direction along the time coordinate , giving 12±1 kJ/mol for the single strychnine conformation , 2×10±1 kJ/mol for double strychnine conformation , 11±1 kJ/mol for d-TC binding mode 1 , and 10±1 kJ/mol for binding mode 2 . We attributed significant differences of the d-TC ligand pose 2 from others in RMSD from crystal structure to failure of H-bond formation with residue K141 from the ( + ) interface , which is observed in the X-ray structure . To investigate this we performed a more precise QM/MM ( B3LYP/CHARMM ) simulation . Details on system setup and simulation procedure are given in the Text S1 ( see also Figure S3 ) . After a 40 ps simulation RMSD from the crystal structure for the d-TC ligand pose 2 dropped to around 1 . 8 Å ( Figure S2D ) . Finally , we measured the fluctuation properties of the equilibrium state using a direct Fourier transform . Fourier spectra and the resulting frequency characteristic ( Fc ) were derived for AChBP bound to d-TC and strychnine and compared to typical agonists and antagonists . The primary frequency characteristic for unliganded AChBP was located at 450±15 GHz , whereas the complex with d-TC exhibited a leftward shift to 212±30 GHz , indicative of lower oscillation energy ( Figure 5 ) . This shift can be interpreted as a decrease in the thermodynamic temperature , which leads to an increased stability of the complex . A comparable leftward shift ( Fc = 105±15 GHz ) was observed in a control simulation performed for AChBP in complex with a variant of α-conotoxin PnIA ( pdb code 2br8 ) . In contrast , AChBP in complex with nicotine ( pdb code 1uw6 ) demonstrated an extreme rightward shift ( Fc = 1 , 200±550 GHz ) . The partial agonist tropisetron also demonstrated a slight increase in oscillation frequency ( Fc = 745±50 GHz ) . These data suggest a correlation between a leftward shift of Fc and antagonistic action of the ligand and a rightward shift and agonist activity . This increase in thermodynamic temperature upon agonist binding agrees well with previous findings that agonist potency is correlated with increase in domain mobility upon ligand binding [31] , [36] and indicates a higher flexibility of AChBP compared to complexes with antagonists . Unexpectedly , the AChBP-complex with strychnine exhibits a small increase in oscillation frequency up to 655±30 GHz ( Figure 5 ) . This result parallels our analysis of the conformational states of loop C ( Figure 3 ) , showing that strychnine stabilizes loop C in a conformation similar to some agonists . In summary , we found that deviations of the crystal structures from the simulated energy minimum are relatively small . All ligand conformations induce receptor fit with high energy , thereby increasing binding efficacy and reducing the probability to switch from one conformation to another . Analysis of the oscillatory movement of AChBP shows that agonist binding results in a thermodynamic destabilization of the receptor , whereas antagonist binding freezes AChBP in a state with a lower oscillation frequency . In conclusion , using molecular dynamics we observe distinct binding modes that are present in the AChBP model system and that might correspond to binding poses present in CLRs .
Acetylcholine binding protein ( AChBP ) has proven a valuable tool for structural studies with more than 20 prototypical ligands for the nAChR . In this study , we take advantage of the molecular recognition by AChBP of strychnine , a prototypical antagonist for the GlyR , and d-TC , which acts on the nAChR and 5-HT3R . We determined X-ray crystal structures of strychnine or d-TC complexes with AChBP and quantified the energetic contribution of observed interactions using molecular dynamic simulation and mutagenesis in α1 GlyR and α7 nAChR . Our results demonstrate that AChBP binds strychnine and d-TC with high affinity and serves as an appropriate model for mapping the binding site topography in different CLRs , including the GlyR , nAChR , and 5-HT3R . In the AChBP complex with strychnine we observed that four binding pockets are fully occupied by a single ligand in identical binding orientations . A fifth binding pocket , which is also involved in a crystal contact , is occupied by two strychnine molecules in a different binding orientation . The relevance of the double occupancy in the binding sites of AChBP to true CLRs is not entirely clear . An intriguing possibility is that double ligand occupancy , which occurs at an interface between two neighboring AChBP molecules , may also occur at synapses where native CLRs are tightly clustered . In analogy , four molecules of epibatidine were suggested to be present in the muscle-type nAChR with its two expected binding sites [37] . In the AChBP complex with d-TC we observed that a preferred ligand orientation occurs in most binding pockets , but with varying degrees of occupancy . Two other binding pockets contain a ligand in a different orientation at full occupancy or a ligand that appears to switch between 2 orientations in a single site . Consequently , these different ligand binding modes result in varying conformations of loop C and stabilize the homopentameric AChBP in a structurally asymmetric state . This adds a new level of diversity among CLRs , whose heterogeneity is known to arise from homomeric and heteromeric assemblies of α- and non-α subunits with different pharmacological properties . Multiple orientations of the same ligand in binding sites of the same AChBP molecule revealed in our work may be present in complexes with true CLRs [38] . Our energy calculations of AChBP complexes , in combination with mutagenesis experiments on the α1 GlyR and α7 nAChR , point to crucial interactions with residues in loop A ( Y91-AChBP ) , B ( S144- and W145-AChBP ) , and D ( Y53-AChBP ) . Gao et al . [24] investigated d-TC and metocurine binding modes using computational methods available at that time and proposed residues Y89 ( loop A ) , W143 ( loop B ) , Y192 ( loop C ) , and L112 and M114 ( loop E ) in Lymnaea AChBP to be structural determinants of d-TC binding . Grudzinska et al . [23] simulated docking of strychnine into a homology model of the α1 GlyR and identified several residues crucial for strychnine-affinity , including F63 ( loop D ) and R131 ( loop E ) . Some of these ligand contacts identified in both studies are confirmed by our results , but the overall ligand orientation of the strychnine and d-TC molecules modeled using computational approaches differs from those observed in our X-ray crystal structures and MD simulations . Moreover , we have systematically mutated the homologous contact residues in all loops of the α1 GlyR and α7 nAChR , based on ligand binding poses experimentally observed in X-ray crystal structures of AChBP . This allowed us to derive a common mode of action that defines poor selective recognition of strychnine and d-TC by various CLRs . The essential residues in loop A , B , and D as identified in our study belong to the aromatic residues that are highly conserved among the CLR family , possibly explaining the wide range of CLRs that can be targeted by strychnine and d-TC . In contrast , peptide neurotoxins form an overlapping but more extended range of interactions with the principal and complementary faces of the binding site . This is clear upon comparison with the subtype-specific antagonist of nAChRs , α-conotoxin ImI . However , peptide toxins and smaller antagonists like d-TC and strychnine share a similar molecular mechanism of action: both classes of antagonists stabilize loop C in a similar extended conformation . Notably , for the ligands characterized in this study there is a significant correlation between C-loop closure observed in AChBP crystal structures , molecular dynamics equilibrium , and the frequency that characterizes AChBP oscillation . Thus , we propose that the conformation of loop C and the induced oscillation of the extracellular domain arise from residue interactions in the ligand-binding site . Combined , the effects on C-loop extension reflect the intrinsic properties of any given ligand and therefore predict well its type of action . We suggest that antagonist effects are transmitted through a thermodynamic stabilization of the extracellular domain and arise from a limited range of residue contacts as shown for strychnine and d-TC . These defined ligand-receptor interactions are found for homologous residues in different CLRs and likely explain the low selective antagonism of strychnine and d-TC .
Aplysia AChBP was expressed and purified from Sf9 insect cells as previously published [25] . Strychnine and d-TC were obtained from Sigma and co-crystallized at a concentration of 1–5 mM . Crystals for Ac-AChBP+ strychnine were grown in 200 mM sodium acetate , 100 mM bistrispropane at pH 8 . 5 , 15 . 5% PEG3350 . Crystallization conditions for Ac-AChBP+d-TC were 200 mM Na2SO4 , 100 mM bistrispropane at pH 8 . 5 , 15% PEG3350 . Growth of crystals at 4 °C was essential to obtain good diffraction for both complexes . Cryoprotection was achieved by adding glycerol to the mother liquor in 5% increments to a final concentration of 30% . Crystals were flash-cooled by immersion in liquid nitrogen . Diffraction data processing was done with MOSFLM and the CCP4 program suite . The structure was solved by molecular replacement using MOLREP . Automated model building was carried out with ARP/wARP and refinement was done with REFMAC or PHENIX with TLS and NCS restraints . Manual building was done with COOT and validation of the final model was carried out with MOLPROBITY . All model figures were prepared with PYMOL . Competitive binding assays with 3H-epibatidine were carried out as previously published with minor modifications ( see Text S1 ) . Electrophysiological assays were carried out using the two-electrode voltage clamp technique . The cDNA encoding human α1 GlyR was subcloned into pGEM-HE for oocyte expression with a PCR strategy and verified by sequencing . The cDNA was linearized with NheI and transcribed with the T7 mMessage mMachine kit from Ambion . The cDNA encoding the human α7 nAChR was cloned into pMXT and linearized with BamHI for transcription with the SP6 mMessage mMachine kit from Ambion . All mutants were engineered using a Quikchange method ( Stratagene ) and verified by sequencing . Recordings were obtained from oocytes 2–5 d after injection with 50 nl of ∼1 ng/nl RNA . For the determination of IC50-values varying concentrations of strychnine or d-TC were co-applied with EC50-concentrations of glycine or acetylcholine , respectively . Peak current responses in the presence of increasing concentrations of strychnine or d-TC were averaged and the mean ± s . e . m . analyzed by non-linear regression using a logistic equation ( GraphPad Prism 5 ) . Student's t test was used for statistical comparison of paired observations . Sequence analysis was performed with the ClustalW2 algorithm . Homology models of the α1 GlyR and α7 nAChR were used to verify our mutagenesis strategy , which was based on predictions from sequence alignments . Modeller9v6 was used without any GUI software and protein structures with pdb code 2vl0 and 2bg9 as templates . Energy for all models was minimized with CHARMM27 . Docking was performed in AUTODOCK4 with 32 flexible bonds in receptor selected from residues inside a sphere r = 5Å centered at mass center of the ligand in the corresponding X-ray structure . A full description of molecular dynamic simulation methods is given in Text S1 . Molecular analysis and visualization was performed in UCSF Chimera and PyMol . For video editing AVS Video Editor was used . Protein Data Bank accession codes for previously published X-ray crystal structures are: Ac-AChBP in complex with lobeline ( 2bys ) , epibatidine ( 2byq ) , imidacloprid ( 3c79 ) , thiacloprid ( 3c84 ) , HEPES ( 2br7 ) , polyethyleneglycol ( 2byn ) , DMXBA ( 2wnj ) , 4OH-DMXBA ( 2wn9 ) , cocaine ( 2pgz ) , anabaseine ( 2wnl ) , in silico compound 31 ( 2w8f ) , MLA ( 2byr ) , in silico compound 35 ( 2w8g ) , sulfates ( 3gua ) , α-conotoxin ImI ( 2c9t and 2byp ) , α-conotoxin PnIA variant ( 2br8 ) , α-conotoxin TxIA ( 2uz6 ) , apo state ( 2w8e ) , Y53C-MMTS ( 2xz5 ) with acetylcholine , Y53C-MTSET ( 2xz6 ) . Bt-AChBP in complex with CAPS ( 2bj0 ) and Ls-AChBP in complex with nicotine ( 1uw6 ) , clothianidin ( 2zjv ) , HEPES ( 1ux2 ) , carbamylcholine ( 1uv6 ) , imidacloprid ( 2zju ) , α-cobratoxin ( 1yi5 ) . Monomeric extracellular domain from mouse α1 nAChR in complex with α-bungarotoxin ( 2qc1 ) . Structures of Ac-AChBP in complex with strychnine ( 2xys ) and d-tubocurarine ( 2xyt ) were obtained during this study . PubChem coordinates: strychnine ( 441 , 071 ) and d-tubocurarine ( 6 , 000 ) .
|
Ligand-gated ion channels play an important role in fast electrochemical signaling in the brain . Cys-loop receptors are a class of pentameric ligand-gated ion channels that are activated by specific neurotransmitters , including acetylcholine ( ACh ) , serotonin ( 5-HT ) , glycine ( Gly ) , and γ-aminobutyric acid ( GABA ) . Each type of cys-loop receptor contains an extracellular domain that specifically recognizes only one of these four neurotransmitters and opens an ion-conducting channel pore upon ligand binding . In this study , we investigated the poor specificity with which two potent neurotoxic inhibitors , namely strychnine and d-tubocurarine , are recognized by different cys-loop receptors . Using X-ray crystallography we solved 3-dimensional structures of strychnine or d-tubocurarine in complex with ACh binding protein ( AChBP ) , a well-recognized structural homolog of the nicotinic ACh receptor . Based on ligand-receptor interactions observed in AChBP structures we designed mutant GlyR and α7 nAChR to identify hot spots in the binding pocket of these receptors that define potent inhibition by strychnine and d-tubocurarine , respectively . Combined , our results offer detailed understanding of the molecular recognition of antagonists that have high affinity but poor specificity for different cys-loop receptors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"neuroscience"
] |
2011
|
A Structural and Mutagenic Blueprint for Molecular Recognition of Strychnine and d-Tubocurarine by Different Cys-Loop Receptors
|
A survey of potential vector sand flies was conducted in the neighboring suburban communities of Vake and Mtatsminda districts in an active focus of visceral Leishmaniasis ( VL ) in Tbilisi , Georgia . Using light and sticky-paper traps , 1 , 266 male and 1 , 179 female sand flies were collected during 2006–2008 . Five Phlebotomus species of three subgenera were collected: Phlebotomus balcanicus Theodor and Phlebotomus halepensis Theodor of the subgenus Adlerius; Phlebotomus kandelakii Shchurenkova and Phlebotomus wenyoni Adler and Theodor of the subgenus Larroussius; Phlebotomus sergenti Perfil'ev of the subgenus Paraphlebotomus . Phlebotomus sergenti ( 35 . 1% ) predominated in Vake , followed by P . kandelakii ( 33 . 5% ) , P . balcanicus ( 18 . 9% ) , P . halepensis ( 12 . 2% ) , and P . wenyoni ( 0 . 3% ) . In Mtatsminda , P . kandelakii ( 76 . 8% ) comprised over three fourths of collected sand flies , followed by P . sergenti ( 12 . 6% ) , P . balcanicus ( 5 . 8% ) , P . halepensis ( 3 . 7% ) , and P . wenyoni ( 1 . 1% ) . The sand fly season in Georgia is exceptionally short beginning in early June , peaking in July and August , then declining to zero in early September . Of 659 female sand flies examined for Leishmania , 12 ( 1 . 8% ) specimens without traces of blood were infected including 10 of 535 P . kandelakii ( 1 . 9% ) and two of 40 P . balcanicus ( 5 . 0% ) . Six isolates were successfully cultured and characterized as Leishmania by PCR . Three isolates from P . kandelakii ( 2 ) and P . balcanicus ( 1 ) were further identified as L . infantum using sequence alignment of the 70 kDa heat-shock protein gene . Importantly , the sand fly isolates showed a high percent identity ( 99 . 8%–99 . 9% ) to human and dog isolates from the same focus , incriminating the two sand fly species as vectors . Blood meal analysis showed that P . kandelakii preferentially feeds on dogs ( 76% ) but also feeds on humans . The abundance , infection rate and feeding behavior of P . kandelakii and the infection rate in P . balcanicus establish these species as vectors in the Tbilisi VL focus .
Historically , visceral leishmaniasis ( VL ) in Georgia has been characterized by sporadic cases chiefly in eastern mountainous districts indicative of an endemic situation [1] , [2] . It wasn't until the 1990s that VL due to Leishmania infantum was recognized as a significant public health problem in the Republic of Georgia [3] . From 1990–2007 there was a resurgence of the disease , with 1 , 414 cases reported involving an 18-fold increase from 10–12 cases per year in the 1990's to 182 cases in 2007 [Official statistical records , National Centers for Disease Control and Public Health ( NCDCPH ) , Tbilisi , Georgia] . Sixty percent of these cases occurred within the capital city of Tbilisi , a modern city of 1 . 2 million inhabitants . In response to this alarming increase , a three-phase program was initiated to gain a better understanding of the epidemiologic cycle of the disease in this focus , including active surveillance in children , dogs and potential vector sand flies . Surveillance of children showed that 7 . 3% of 4 , 250 children aged 1–14 years were seropositive for Leishmania at the baseline survey , and 6 . 0% became seropositive over one year [4] . Risk of infection was associated with living in areas where clustered flying insects and stray dogs were observed . For dogs , the major reservoir of L . infantum infection , 18 . 2% of 588 domestic and 15 . 3% of 718 stray dogs surveyed were seropositive [4] . Among about 20 sand fly species that play a significant role in transmission of Leishmania parasites in the Mediterranean basin , members of the subgenus Larroussius represent the most important vectors of L . infantum [5] , [6] . In countries of the former Soviet Union , members of the subgenera Larroussius and Adlerius , such as Phlebotomus brevis Theodor & Mesghali and Phlebotomus perfiliewi transcaucasicus Perfil'ev in Azerbaijan , Phlebotomus longiductus Parrot in Uzbekistan and Kazakhstan , and Phlebotomus kandelakii Shchurenkova in Georgia , were suspected as vectors of L . infantum but none were incriminated [5] , [7] . Recent investigations on vectors of VL in northwestern Iran ( Ardebil and Fars provinces ) and East Azerbaijan found P . perfiliewi transcaucasicus , P . kandelakii , and Phlebotomus ( Adlerius ) sp . , naturally infected with parasites belonging to the Leishmania donovani complex by PCR [8]–[10] . Studies on the distribution , seasonality and behavior of sand flies in disease foci in Georgia have not been carried out for the past 20 years . Lemer [1] conducted an investigation of potential vector sand flies in eastern Georgia from 1942–1952 . He reported on the specific composition of sand fly populations in four localities and on the seasonal occurrence and epidemiological importance of Phlebotomus kandelakii Shchurenkova and P . chinensis balcanicus Newstead ( Phlebtomus balcanicus , Theodor ) , which were the predominant species and the only ones common to all four localities . In one of the localities , it was observed that both species were infected with leptomonads ( promastigotes ) , the infection rate being 3 . 7 percent [1] . Although such evidence casts suspicion on these species as vectors of L . infantum it does not prove the role of a vector [5] . A more recent review listed fourteen species of Phlebotomus sand flies identified in previous entomological surveys , all from eastern Georgia: Phlebotomus papatasi Scopoli of the subgenus Phlebotomus; Phlebotomus caucasicus Marzinowsky , Phlebotomus mongolensis Sinton , Phlebotomus sergenti Perfil'ev and Phlebotomus jacusieli Theodor of the subgenus Paraphlebotomus; P . kandelakii , Phlebotomus tobbi Adler & Theodor , Phlebotomus syriacus Adler & Theodor , P . transcaucasicus and Phlebotomus wenyoni Adler & Theodor of the subgenus Larroussius; and Phlebotomus simici Nitzulescu , Phlebotomus halepensis Theodor , Phlebotomus chinensis Newstead , and Phlebotomus balcanicus Theodor of the subgenus Adlerius [11] . P . kandelakii , P . balcanicus and P . sergenti were the predominant species , with P . kandelakii being the most abundant [11] . The involvement of these sand flies in the transmission of VL was not addressed in any of these surveys . Here , we report on the species diversity , relative abundance and spatial and temporal distribution of phlebotomine sand flies within an active VL focus in Tibilisi , Georgia . We also provide compelling evidence incriminating P . kandelakii and P . balcanicus as vectors of L . infantum in this focus , report their natural Leishmania infection rates and demonstrate that isolates obtained from these wild-caught specimens are identical to L . infantum isolates obtained from humans and canines in the same focus . Additionally , this is the first study in which live parasites were isolated from the sand fly species P . kandelakii and P . balcanicus and characterized as L . infantum .
Oral consent was obtained from heads of compounds chosen for collection of sand flies in Vake and Mtatsminda . Light traps and sticky traps were only placed outside houses , in courtyards , animal pens and shelters . Flies fed on human volunteers were obtained from activities related to a project addressing human immune responses to sand fly saliva . This project was approved by the Walter Reed Army Medical Center Human Use committee ( protocol # 355023 ) . All the subjects provided written informed consent . Acquisition of dog blood was done under animal protocol LMVR 7E approved by the NIAID DIR ACUC committee that adheres to the U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training and maintains animals in accordance with the PHS Policy on Humane Care and Use of Laboratory Animals , the Guide for the Care and Use of Laboratory Animals , and the Animal Welfare Act and Animal Welfare Regulations user guidelines . The city of Tbilisi is situated in a narrow valley on the banks of the Mt'k'vari ( Kura ) River , flanked on the east and west by steep hills . The neighboring communities of Vake and Mtatsminda , from whence cases of VL are reported , are located on the western flank of the valley overlooking the city ( Fig . 1 ) . Homes in these communities are modest , mostly of brick or stone construction . Most are within fenced or walled compounds containing courtyards , trees and orchards , gardens , grape arbors and pens for animals including dogs , chickens and rabbits , thus offering a diversity of blood meal sources as well as protected microhabitats suitable as resting and breeding sites for sand flies . Windows are unscreened , permitting sand flies free access to residents . During the 2010 field trip , 39 blood-fed wild-caught female sand flies were collected for host blood meal analysis by PCR . The head and tip of the abdomen of each fly were removed and used to identify the fly to species . The midguts , with blood meal intact , were removed and squashed onto a Whatman Clone Saver card ( Whatman plc Springfield Mill , Kent ME14 2LE , UK ) to preserve them for later analysis . Each preserved blood meal was labeled with the collection date , collection method ( light trap or sticky trap ) , collection site and species identity . PCR-based identification of the mammalian blood was carried out according to the method of Kent and Norris [20] . DNA was isolated from punches of the Clone Saver card using the QIAmp DNA Micro kit ( Qiagen Inc . , Valencia , CA ) according to the manufacturer's protocol and the DNA was eluted in 20 µL of ultrapure H2O . Due to the variability in the size of the bloodmeal of field-collected samples , the integrity of the DNA was verified using universal vertebrate-specific primers . Separate PCR reactions were prepared for the amplification of dog , human or universal vertebrate-specific cytochrome b . Each 25 µL reaction mixture consisted of 12 . 5 µL of PCR Master ( Roche Applied Science , Indianapolis , IN ) , 200 pmol of Human741F , Dog 368F , or UNFOR403 and UNREV1025 primers , 2 . 5 µL RediLoad ( Invitrogen ) and approximately 15 ng DNA template . The reaction was initiated with a 5-minute denaturation at 95°C followed by 35 cycles at 95°C for 1 minute , 58°C for 1 minute and 72°C for 1 minute . The extension step was performed at 72°C for 7 minutes . Amplicons were produced in a GeneAmp PCR System 9700 ( Applied Biosystems , Carlsbad , CA ) thermocycler and the products visualized on cyanine-stained 1 . 5% agarose gel alongside a TrackIt 100 bp DNA ladder ( Invitrogen , Carlsbad , CA ) . Positive control DNA templates were extracted from Phlebotomus perniciosus fed artificially through a membrane on dog blood and Phlebotomus duboscqi fed on a human and were processed in a manner identical to field-caught specimens .
Sand flies were collected in abundance in and around case sites , especially near chicken coops and animal pens , from early June through mid August during each of the survey years ( Figure 3 ) . Analysis of collection data over three summers clearly reveals a short sand fly season and a unimodal annual distribution pattern , with sand fly emergence beginning around the first week of June and population densities increasing to a peak in July and then declining through late August to zero in early September ( Figure 3 ) . In July–August of 2008 , three of 283 ( 1 . 1% ) female sand flies dissected and examined for Leishmania , were found infected . These included two of 223 P . kandelakii ( 0 . 9% ) and one of 14 P . balcanicus ( 7 . 1% ) ( Table 2 ) . All three flies harbored massive , mature infections with attachment to the microvillar lining of the midgut wall and to the cuticular intima of the stomodeal valve . Nectomonads and haptomonads were packed tightly in the thoracic midgut , behind the stomodeal valve , forming a “plug” with a high proportion of free-swimming metacyclic parasites that escaped into the dissection fluid and were distinguished by a small cell body , long flagella and rapid movement . These infectious forms were clearly visible under high magnification ( 40× ) . In some of the infections , the entire thoracic midgut was occluded with a plug of parasites that were mostly trapped in a gel matrix [21] . Similarly , in July of 2010 , nine of 376 ( 2 . 4% ) females dissected and examined were found infected , including eight of 320 P . kandelakii ( 2 . 5% ) and one of 24 P . balcanicus ( 4 . 2% ) ( Table 2 ) . PCR analysis of six successful isolates identified the parasites as Leishmania ( Figure 4A ) . Further sequencing of the Leishmania HSP70 from two P . kandelakii and one P . balcanicus isolates followed by sequence alignment against sequences from a human and two dog isolates from the same focus and other Leishmania species confirmed the parasite identity as L . infantum ( Figure 4B ) . Of note , the sand fly isolates were identical to the human and dog isolates from the same focus ( Figure 4B ) . Of 39 blood-fed Phlebotomus sand flies collected from the Mtatsminda district of Tbilisi , Georgia , 27 were suitable for analysis by PCR amplification of the cytochrome b gene . As L . infantum infection is propagated between dogs , the main infection reservoirs , and humans by sand flies , it was important to identify which of the blood-fed flies fed on either dog ( 730 bp ) or human ( 360 bp ) blood ( Table 3 , Figure 5A ) . A universal cytochrome b target was used as a DNA quality control ( Figure 5B ) . Phlebotomus kandelakii was the most abundant sand fly species collected , comprising 25 of the 27 blood-fed females available for analysis ( Table 3 ) . The remaining two blood-fed specimens were identified as P . halepensis and P . sergenti . There were no blood-fed P . balcanicus captured . The host source was identified in 80% of the 25 P . kandelakii blood meals . Dogs were identified as the preferred host ( 76% ) of P . kandelakii and were the source of blood for each of the fed P . halepensis and P . sergenti specimens ( Table 3 ) . A human blood meal was identified in a single P . kandelakii sand fly ( Figure 5A ) . Five P . kandelakii sand flies contained blood meals that were not identified as dog or human but were confirmed as vertebrate hosts by universal amplification of cytochrome b . Mixed dog/human blood meals were not detected .
Fourteen Phlebotomus species have been reported from the eastern part of Georgia , most in mountainous rural or periurban areas [11] . The rather low species diversity observed in the current study may be a reflection of the peri-urban environment in which they were collected and emphasizes the urban nature of the Tbilisi focus where sand flies are likely breeding within or close to human habitation . Of the species collected in this study , those of the subgenera Larroussius and Adlerius were of particular interest as potential vectors . Species of these subgenera have been incriminated or implicated elsewhere as vectors of L . infantum [5] , [7]–[10] , [22] . P . ( Larroussius ) kandelakii is a proven vector of L . infantum in Iran [22] and P . ( Adlerius ) balcanicus is a suspected vector of L . infantum in Greece and Serbia [23] . In the Old world there is a noticeable association between Leishmania species or complexes and particular subgenera of Phlebotomus [24] . Thus , it is reasonable to suspect putative vectors in previously unexplored foci based on their close taxonomic relationship to known vectors . Killick-Kendrick [5] noted that most incriminated vectors of L . infantum belong to the subgenus Larroussius . Phylogentically , the subgenus Adlerius is closely related to this subgenus [25] but most studies have not fully incriminated members of the subgenus Adlerius as vectors . Routine vector surveillance in the suburban communities of Vake and Mtatsminda revealed an abundance of sand flies during a remarkably short season spanning early June through early September , suggesting a univoltine population that undergoes an obligatory or facultative diapause that carries them through the fall , winter and spring months . This is consistent with the findings of earlier workers who studied the biology of sand flies in foci of VL in mountainous areas of eastern Georgian USSR from 1945–1952 [1] . Diapause appears to be a common strategy in temperate sand fly species for surviving unfavorably cold conditions . For example , Phlebotomus ariasi in the Cevennes region of southern France undergoes a facultative diapause triggered by lower temperatures and a shorter photoperiod at the end of a short summer season [26] , [27] . Other species undergo obligatory diapause triggered by diminishing photo period length [14] . In light of the shortness of the sand fly season , the high seroprevalence in children and dogs living in this focus [4] is indicative of a highly efficient transmission cycle . This is perhaps sustained by the presence , as established in this study , of more than one vector species transmitting and spreading the infection . The expansion and emergence of new foci of L . infantum in Georgia may also be accounted for by the breakdown of surveillance and vector control efforts in areas where the infection is prevalent . The overall infection rate reported in this study ( 1 . 8% ) is about half that reported by Lemer [1] over six decades ago . However , the promastigotes ( referred to as leptomonads ) in the earlier study were not identified as Leishmania nor was the feeding status of the infected flies determined . Killick-Kendrick and Ward [5] , [28] outlined five criteria that should be fulfilled before a sand fly species is incriminated as a vector of human leishmaniasis with reasonable certainty: 1 ) Overlap of the geographic distribution of the suspected vector and human cases; 2 ) Sufficient abundance of the suspected vector necessary to maintain transmission; 3 ) Mature infections in naturally or experimentally infected flies; 4 ) Experimental transmission by bite; and 5 ) Isolation of Leishmania from wild-caught sand flies indistinguishable from the parasite causing disease in humans in the same place . These are highly stringent criteria and in the case of P . kandelakii , all have been satisfied apart from experimental transmission . This includes the first isolation and characterization of live L . infantum parasites from this species . With the advent of PCR technologies there has been a tendency by some to incriminate vectors based solely on the presence of Leishmania DNA . However , because a non-vector may imbibe blood from an infected host and thereby ingest Leishmania parasites , such findings , particularly in the absence of careful assessment of the feeding status , must be interpreted with caution . For P . balcanicus , the three most relevant incrimination criteria ( the above-mentioned 1 , 3 , and 5 ) were also met , and while P . kandelakii was more prevalent in collections throughout the study period , the higher infection rates observed in P . balcanicus in 2008 and 2010 indicate that both are competent vectors of L . infantum in the Tbilisi focus . The finding in this study of massive , mature infections with a high proportion of metacyclic Leishmania parasites in both P . kandelakii and P . balcanicus is a demonstration of their natural ability to harbor the parasites through their complete extrinsic life cycle . More extensive trapping is needed to determine whether P . halepensis and P . wenyoni are also involved in parasite transmission in Tbilisi . VL due to L . infantum is a zoonosis where the parasites are circulated between canines , the reservoirs of the infection . Humans become infected as accidental or incidental hosts with dogs being the most relevant source of infection . Blood meal source identification clearly implicates dogs as the favored host for P . kandelakii and the best reservoir of L . infantum infection [29] . Additionally , finding one P . kandelakii specimen with human blood is epidemiologically relevant providing evidence that this species feeds on both dogs and humans and thus can potentially spread infections from dogs to humans . Unfortunately , the absence of P . balcanicus blood-fed flies from our collection did not allow us to determine its feeding behavior and whether its role is primarily to propagate the infection among dogs or whether , similar to P . kandelakii , it feeds on humans and thus is likely to also transmit the parasite from dogs to humans . In addition to dogs and humans , chickens , cats , rodents , rabbits and bovines were present in the compounds where sand flies were collected . These animals , though epidemiologically irrelevant , represent potential sources of blood for the sand flies and likely account for the five unidentified P . kandelakii blood meals . In conclusion , finding mature infections containing a high proportion of metacyclic promastigotes in both P . kandelakii and P . balcanicus provides strong evidence that they are capable of harboring L . infantum through its complete life cycle . These findings enable us to declare with confidence that P . kandelakii and P . balcanicus are vectors of Leishmania infantum in the VL focus in Tbilisi , Georgia . Based on its high relative abundance , P . kandelakii is probably the primary vector . However , the higher percentage of natural infections observed in P . balcanicus indicates that it is the more efficient vector and therefore plays a significant role in the epidemiology of visceral Leishmaniasis in this focus .
|
Visceral Leishmaniasis ( VL ) is a public health problem in Tbilisi , capital of the Republic of Georgia . VL is caused by Leishmania parasites and dogs represent the main infection reservoirs . VL is transmitted among humans and dogs by sand fly bites . Here , we carried out a three-year survey to assess the sand fly species in two communities within the VL focus of Tbilisi in the districts of Vake and Mtatsminda . We collected five sand fly species , and the most abundant was Phlebotomus kandelakii . We found live parasites in the midgut of P . kandelakii and another species , P . balcanicus . Using molecular techniques we identified the parasites as Leishmania infantum . We also found that these sand fly isolates shared a high identity to parasites isolated from dogs and humans in the same focus . This incriminates P . kandelakii and P . balcanicus as vectors of VL in this focus . The source of the blood meals in fed flies revealed that P . kandelakii preferentially feeds on dogs but also feeds on humans . The abundance , infection rate and feeding behavior of P . kandelakii and the consistently higher infection rate of P . balcanicus compared to P . kandelakii incriminate these species as primary vectors in the Tbilisi VL focus .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"ecology",
"biology",
"parasitic",
"diseases"
] |
2012
|
Incrimination of Phlebotomus kandelakii and Phlebotomus balcanicus as Vectors of Leishmania infantum in Tbilisi, Georgia
|
The evolution of drug resistance in HIV occurs by the fixation of specific , well-known , drug-resistance mutations , but the underlying population genetic processes are not well understood . By analyzing within-patient longitudinal sequence data , we make four observations that shed a light on the underlying processes and allow us to infer the short-term effective population size of the viral population in a patient . Our first observation is that the evolution of drug resistance usually occurs by the fixation of one drug-resistance mutation at a time , as opposed to several changes simultaneously . Second , we find that these fixation events are accompanied by a reduction in genetic diversity in the region surrounding the fixed drug-resistance mutation , due to the hitchhiking effect . Third , we observe that the fixation of drug-resistance mutations involves both hard and soft selective sweeps . In a hard sweep , a resistance mutation arises in a single viral particle and drives all linked mutations with it when it spreads in the viral population , which dramatically reduces genetic diversity . On the other hand , in a soft sweep , a resistance mutation occurs multiple times on different genetic backgrounds , and the reduction of diversity is weak . Using the frequency of occurrence of hard and soft sweeps we estimate the effective population size of HIV to be ( confidence interval ) . This number is much lower than the actual number of infected cells , but much larger than previous population size estimates based on synonymous diversity . We propose several explanations for the observed discrepancies . Finally , our fourth observation is that genetic diversity at non-synonymous sites recovers to its pre-fixation value within 18 months , whereas diversity at synonymous sites remains depressed after this time period . These results improve our understanding of HIV evolution and have potential implications for treatment strategies .
Understanding the process of adaptation is one of the basic questions in evolutionary biology . The evolution of drug resistance in pathogens such as HIV is a prime example of adaptation and a major clinical and public health concern because it leads to treatment failures . The likelihood that a population adapts in response to an environmental challenge , e . g . , a viral population in a patient develops resistance in response to a drug treatment , depends , among other things , on the amount of genetic diversity that the population harbors [1] , [2] . The amount of genetic diversity in a population depends in turn on the details of the adaptive process . In the classical model of adaptation , a beneficial mutation arises once in a single individual and , as this mutation increases in frequency in the population , other mutations “hitchhike” with it to high frequency [3] . In this process , referred to as a “hard selective sweep” , genetic diversity at linked sites is strongly reduced when the beneficial mutation fixes in the population . However , if the population is sufficiently large , beneficial mutations will occur frequently or will be present in multiple copies prior to the onset of selection . Then , multiple beneficial alleles may replace the wildtype without leading to a significant reduction in diversity . This is called a “soft selective sweep” [4] . We use HIV as a model system to ask the following evolutionary questions . How much do selective sweeps affect genetic diversity at neighboring sites ? How often does adaptation occur via soft and hard sweeps ? What is the effective population size of HIV relevant for adaptation ( later referred to simply as ) ? How does diversity recover after a selective sweep at synonymous and non-synonymous sites ? Three factors make HIV well suited to address these questions: ( a ) HIV evolves rapidly due to its short replication time and high mutation rate , ( b ) HIV populations in different patients act as independent replicates of the same evolutionary process , and ( c ) the main genetic targets of positive selection in HIV during antiviral treatment are known: these targets are drug-resistance mutations which have been well characterized . From the questions above , the estimation of the short-term effective population size ( ) of HIV is perhaps the most interesting and clinically important . We are interested in the short-term effective population size because it is one of the determinants of the probability that drug resistance evolves in a certain generation . This effective population size is primarily determined by current processes ( such as possibly background selection ) , and much less so by historical processes or events ( such as bottlenecks ) . The question of the effective population size of HIV has been a subject of a heated debate because of enormous discrepancies in the estimates obtained by different methods ( see [5] for a review ) . In an influential 1997 paper , Leigh-Brown used genetic diversity at synonymous sites and estimated the effective size of the viral population to be on the order of [6] . Around the same time , it became clear that the number of active virus-producing cells in the body of an infected person was around [7] which lead to a natural expectation that the viral population should be very large with almost deterministic evolutionary dynamics [8] . However , Frost et al found that frequencies of resistance mutations before the start of treatment varied greatly between patients , which led them to conclude that stochastic effects play an important role in HIV , and the population size must therefore be or smaller [9] . With more data and a more sophisticated linkage disequilibrium-based method Rouzine and Coffin inferred that the population size of the virus must be at least [10] . Finally , a recent study by Maldarelli et al [11] places between and , based on changes in allele frequencies over time . We estimate of HIV using previously published longitudinal sequence data from several HIV-infected patients under drug treatment [12] . Our approach to estimating is most closely related to the recent estimates done in Drosophila [13] , Plasmodium [14] , and a chimeric simian-human immunodeficiency virus [15] , but differs from these studies in that we use data from several patients for increased statistical power . The main idea of our method is as follows . Since the mode of adaptation ( hard versus soft sweeps ) depends on the supply of beneficial mutations which in turn depends on the effective size of the population , it is possible to use the observed frequency of hard and soft sweeps to estimate . The key problem is to distinguish hard and soft sweeps in the sequence data . We address this problem by taking advantage of a unique property of codon 103 in the reverse-transcriptase gene , namely that there are two distinct nucleotide changes that convert the ( wildtype ) amino acid lysine into asparagine , which confers drug resistance . If both of these nucleotide changes are observed in a single sample , a soft sweep must have occurred . While the effects of selection and demographic processes on the reduction of diversity have been extensively studied , the question of how diversity recovers after perturbations has received little attention . Yet , understanding the dynamics of recovery is important because it determines how long signatures of selective sweep or bottlenecks remain detectable in genetic data . Failure to account for the time it takes for diversity to reach its equilibrium after a perturbation can lead to biases in estimation of population genetic parameters such as [5] . Using longitudinal data we show here that diversity after a selective sweep recovers faster at non-synonymous sites than at synonymous sites .
We study how the genetic composition of HIV populations in patients treated with a combination of reverse transcriptase ( RT ) and protease inhibitors changed over the course of about one year ( see Ref . [12] and Loss of diversity for details on the data ) . Out of 118 patients studied in Ref . [12] , we selected a subset of 30 patients in which the virus had no resistance mutations at the start of the study but fixed a resistance mutation at one of the later time points . All our analyses were done on this subset of patients ( see Loss of diversity for details ) . We note that in 24 out of 30 patients ( ) we detect fixation of a single resistance mutation within the observation period , even though all patients were treated with multiple drugs ( see Suppl Table S1 ) . Samples taken prior to treatment in these patients show nucleotide diversities of at synonymous sites and at non-synonymous sites . At the time points when we detect fixations of drug-resistance alleles at one or several known sites we observe a substantial decrease in the levels of polymorphism in the 1 Kb region that was sequenced ( see Figures 1 , 2 , 3 , and Loss of diversity ) . Between the last sample before and the first sample after the fixation of the first drug-resistance mutation , viral populations lose of their genetic diversity , measured by the median drop in per-site heterozygosity . This difference is significant , with ( all P-values we report are from Mann-Whitney tests ) . We observe no loss of diversity in control intervals in which no resistance mutation was fixed ( see Loss of diversity ) . The loss of diversity that we observe is a result of hitchhiking: when an adaptive mutation rapidly increases in frequency , it takes with it the genetic background on which it arose [3] . Three factors can attenuate the observed loss of diversity after a sweep . First , recombination can preserve some diversity by allowing sites at some distance from the selected site to escape the effects of the sweep [16] . Recombinational escape does not appear to be a factor in these data however because we do not observe a correlation between post-sweep heterozygosity and distance from the selected site ( see Recombination ) . This is consistent with previously reported observations in HIV [17] . Second , the amount of diversity lost depends on the origin of the adaptive allele . Diversity may survive the fixation of an adaptive allele even in the absence of recombination if the selective sweep is “soft” , i . e . , if the same adaptive allele arises via multiple mutations on different genetic backgrounds [18] . On the other hand , genetic diversity at neighboring sites is reduced dramatically if the selective sweep is “hard” , i . e . , if the adaptive allele originates through a single mutation on one genetic background [3] . We demonstrate below that both soft and hard sweeps occurred in these patients . Importantly , whether soft or hard sweeps predominate in a population depends on the supply of adaptive mutations which is determined by the product of the beneficial mutation rate and the current effective population size . If adaptive mutations appear in the population on average more than once per viral generation , they are likely to fix via soft sweeps . If adaptive mutations appear on average less than once per generation , they typically fix via hard sweeps [4] . Thus , we can assess the supply rate of resistance mutations by determining whether the observed selective sweeps are soft or hard . Third , the observed loss of diversity depends on whether new mutations occur between the fixation of a resistance allele and the time when samples are taken and the sweep is observed . Since we do not know the actual time of fixation , we do not know the absolute amount of diversity loss due to the sweep . However , this fact does not obscure informative patterns of relative loss of diversity , for synonymous versus non-synonymous mutations and for soft versus hard sweeps . In 17 of the 30 HIV populations ( ) , we observe the fixation of just one , well-known drug resistance mutation , K103N ( lysine to asparagine ) in RT . This mutation confers high-level resistance to the non-nucleoside RT inhibitor used by the patients [12] . Two different nucleotide changes produce the same K103N amino acid change . The wildtype codon AAA encoding lysine can mutate to either codon AAC or codon AAT both encoding asparagine which confers resistance . This property is unique to the K103N mutation among all resistance mutations observed in these patients ( see Suppl . Table S1 ) and can be used to distinguish soft and potentially hard sweeps . In 7 of these 17 populations ( ) both codons ( AAC and AAT ) that code for asparagine are present in the population in the first sample after the sweep ( Figure 1 ) . These are certain to be soft sweeps . As expected , the median reduction of diversity in these populations is only and not significantly different from 0 . In the remaining 10 patients ( ) only one codon is present after the sweep ( AAC in 6 and AAT in 4 patients; Figure 2 ) . These sweeps could be either soft or hard . If in these 10 “one-codon” patients the resistance allele typically arose via the same number of unique mutation events as in the 7 “two-codon” patients , we would expect to see a similarly small reduction in diversity at linked sites in these two groups of patients . In contrast , we find that in the 10 “one-codon” viral populations , genetic diversity is reduced by ( ) , and the difference between the “two-codon” and “one-codon” populations is significant ( ) . It is possible that two codons initially fixed in the “one-codon” patients but one of the codons was lost ( for example , due to another sweep ) before the sample was taken . This scenario would have been plausible if the time intervals between samples were , for some reason , longer in the “one-codon” than in the “two-codon” patients . However , we observe no statistical difference in the inter-sample time intervals between the “two-codon” and “one-codon” patients ( ) . We therefore conclude that the selective sweeps in the “one-codon” patients are due to fewer mutational origins than in the “two-codon” patients , and some are likely hard sweeps ( see Figure 2 ) . The fact that we observe both soft and hard sweeps implies that the K103N mutation appears roughly once per generation [4] . A more accurate estimate of the mutation supply rate based on the frequencies of AAC and AAT alleles in the post-sweep samples yields new K103N mutations per generation ( confidence interval ) . Assuming that we sample 6 viral isolates per patient ( which is the median in our dataset ) , theory predicts that , if , of populations would show a hard sweep signature ( i . e . , the sweep would be due to a single origin ) , which is consistent with our observations . Assuming the point mutation rate of per generation for transversions [19] , we estimate the effective population size of HIV populations to be ( see Estimating the supply rate of resistance mutations for details ) . We estimated based on data from the 17 patients in which the K103N mutation fixed . Although we cannot do the same analysis with the other resistance mutations in the dataset , we find that the median reduction in diversity in the other 13 patients is intermediate between the “one-codon” and “two-codon” K103N patients ( see Suppl . Figure S1 ) , which suggests that K103N mutation is not special and that similar processes are taking place at the other resistance sites . Further , the haplotype structures in two patients with amino-acid changes in the 190th codon position suggest a soft sweep in one and a hard sweep in the other ( see Suppl . Figure S2 ) . Thus adaptation at the 103rd codon position appears to be representative of adaptation at other codon positions . There are other amino acid changes that are similar to the lysine to asparagine ( K to N ) change in that they can be achieved by two different nucleotide changes . We provide a list of these amino acid changes ( limited to those changes that are one mutational step away ) in Suppl . Table S2 . One of the changes on this list is the methionine to leucine ( M to L ) change , which is relevant for drug resistance in HIV . The M46L change in the Protease gene leads to resistance to certain Protease Inhibitors . A six-patient dataset from 1997 [20] provides two examples of the fixation of the M46L change from wildtype ( ATG ) to resistant ( TTG or CTG ) . In one of these two patients , we observe a mixture of the TTG and CTG codons ( thus a soft sweep ) whereas in the other patient we only observe the TTG codon ( potentially a hard sweep , see Suppl . Figure S3 ) . Of the other four patients , two did not fix the M46L mutation and two fixed it at the same time as another resistance mutation . It is difficult to draw conclusions based solely on the data from this study because it consists of only 6 patients [20] , but they do provide independent support for our observation that drug resistance mutations in HIV fix by means of both soft and hard sweeps . An interesting question is whether the resistance mutations that fix stem from standing genetic variation or are due to de novo mutations during treatment . If the resistance mutations stem from standing genetic variation , then our estimate of would reflect the pre-treatment effective population size , whereas , if the mutations would be due to de novo mutation , our estimate would reflect the effective population size during treatment . We compared the times of occurrence of soft and hard sweeps relative to the onset of treatment and found that these were statistically indistinguishable . There are three possible explanations for this observation . First , almost all observed sweeps are due to standing genetic variation , and our estimate of reflects the pre-treatment population . Second , almost all sweeps are due to new mutations and our estimate reflects only the on-treatment . Third , sweeps we observe originated from both standing genetic variation and de novo mutation , but the start of the treatment had no discernible effect on the effective population size , and our estimate reflects both the pre-treatment and on-treatment population size . We currently have no statistical power to distinguish between these three possibilities , and so it is possible that , if the second explanation is true , the pre-treatment population size is much larger than our estimate . However , some of the sweeps we observed occurred very early during treatment ( within one or two months ) , and it seems likely that at least these early sweeps are due to standing genetic variation ( see also [21] ) . Note that , counter to intuition , whether mutations stem from the standing genetic variation or not does not affect the expected number of origins in a sample ( see Suppl . Figure S4 and [4] ) . Our estimate of is similar to earlier estimates that were also based on resistance mutations [9] and other beneficial mutations [10] . Our approach may be viewed as a combination of these two previous approaches . Rouzine and Coffin [10] tested whether a recombinant or double mutant genotype is present , given that two beneficial mutations are sweeping through the population at the same time . Similar to a second mutation that leads to a soft sweep , a double mutant or recombinant can only occur in a population with sufficiently high mutational or recombinational input , so that the frequency of such a genotype in a sample permits the estimation of the mutational or recombinational input . Frost et al [9] used inferred pre-treatment frequencies of two resistance mutations at the same codon ( RT M184I and M184V ) to estimate the effective population size . They found that the two mutations were present at different pre-treatment frequencies in seven patients . In a very large population , the frequency of such mutations would be close to the expected frequency ( ) in every patient , but in smaller populations frequencies vary stochastically , and the spread in the observed frequencies can be used to obtain an upper bound for the population size . Our estimate of the supply rate of resistance mutations is much lower than would be expected if the effective population size were equal to the number of virus-infected cells in the body , which is estimated to be [7] , [8] . At the same time , it is much higher than estimates of the effective population size based on the level of diversity at synonymous sites ( assuming neutrality of synonymous variation ) which would be around [6] . Next we discuss possible reasons for these discrepancies . We propose three possible explanations for the large difference between our estimate based on the frequency of soft sweeps and the estimate based on the traditional analyses of synonymous diversity . All three explanations may be contributing to this difference . First , some synonymous mutations may be deleterious [22] . Second , positive and negative selection on linked sites ( draft and background selection ) reduce diversity at synonymous sites , but may have a weaker effect on beneficial mutations such as K103N . Our observation that the fixation of drug resistance mutations is accompanied by a reduction in diversity provides evidence for this explanation . Third , synonymous diversity may take a long time to recover after a sweep or a bottleneck . We discuss the third option in more detail and provide evidence from the data for this explanation . We observe that diversity at both synonymous and non-synonymous sites steadily recovers after the sweep ( Figure 3 ) . However , even after 6 to 18 months , synonymous diversity remains significantly depressed , with a median reduction of ( ) compared to pre-sweep diversity , up from ( ) directly after the sweep . In contrast , after 6 to 18 months after the sweep , non-synonymous diversity is fully recovered , with the median amount of diversity increased by ( n . s . ) compared to pre-sweep , up from ( ) directly after the sweep . Hence , even relatively infrequent selective sweeps can keep synonymous diversity at a level much below the equilibrium . The observation that stronger negative selection leads to faster recovery is consistent with recent predictions about the approach of the distribution of allele frequencies to stationarity at a single locus [23] . This effect is well known in systems biology: stronger negative feedback leads to a faster the recovery time [24] . A heuristic analysis ( Text S1 ) , similar to analyses in [25]–[27] , shows that neutral sites recover half of their diversity in roughly generations while deleterious sites recover half of their diversity in roughly generations , where is the strength of selection against deleterious mutations . Interestingly , for realistic values of the mutation rate , the recovery time is independent of the mutation rate ( see Text S1 and Suppl . Figure S5 ) . The dynamics of how diversity recovers are likely not specific to HIV but typical to any large population . In humans , for example , Europeans have reduced synonymous diversity , but a similar amount of non-synonymous diversity , compared to Africans [28] , [29] . This is consistent with the explanation that non-synonymous diversity has had enough time to recover since the out-of-Africa bottleneck , but synonymous diversity has not . This observation leads to a counterintuitive practical implication . Since non-synonymous diversity recovers faster , it is usually closer to its equilibrium than synonymous diversity . Thus non-synonymous sites may in fact be more useful for classical population genetics inference than synonymous sites . HIV populations consist of a heterogeneous collection of genotypes , many of which may carry deleterious and advantageous mutations . A new mutation arises in one such genotype and is therefore linked , at least temporarily , to other mutations already present in it . The probability that a new mutation eventually fixes or goes extinct thus depends not only on its own selective effect but also on the combined fitness of the genetic background in which it arose [30] , [31] . The effects of linkage on the fates of new mutations are complex and comprise an active area of research [30]–[39] , but the qualitative picture is straightforward . The fates of neutral , deleterious and weakly beneficial mutations are entirely determined by the background in which they arise [31]: only mutations that arise on high-fitness genotypes have a chance to persist in the population ( Figure 4a , b ) . The number of segregating neutral mutations in such a population will be small because high-fitness individuals comprise only a small fraction of the population [30] , [33] , [34] . At the other extreme , adaptive mutations with very large selective effects survive and spread irrespective of the genetic background in which they arise ( Figure 4c ) . The effective population size for such mutations may be close to the census population size [13] . The effective population size for mutations with intermediate effects is somewhere in between ( Figure 4d ) . The effective size we estimate from the K103N mutation is several orders of magnitude smaller than the estimated census size . This could mean that the distribution of fitnesses in the viral population due to ongoing positive and negative selection is wide , so that even a highly beneficial mutation such as K103N needs to appear on a relatively fit genetic background to have a good chance of survival . The width of the distribution of fitnesses in within-patient HIV populations is currently unknown . Theory shows that the width of the fitness distribution depends on the rates and typical effects of beneficial and deleterious mutations and on the viral census size [30] , [39] , [40] , quantities that are difficult to estimate . Neher and Leitner recently estimated that at least of non-synonymous mutations have a selection coefficient exceeding [41] . In another study , Batorsky et al concluded that the rate of adaptation of HIV rate is consistent with an exponential distribution of fitness effects of adaptive mutations with mean [42] . If these estimates are accurate , they could imply that the distributions of fitnesses in within-patient HIV populations are narrow so that fixation of highly beneficial mutations like K103N should not be affected by linked selection . Other effects may help explain why the effective size of HIV is smaller than the census size . For example , the treatment itself may reduce the population size of the virus before resistance evolves . If this is true , then soft sweeps must be especially common in patients who were treated with inferior drugs in the 1980s and 1990s , because early treatments were not as successful in reducing the population size of the virus . If treatment reduces the effective population size , then this would also mean that soft sweeps should be more common in patients who fail early versus patients who fail later ( after more time on treatment ) . We find no evidence for such an effect in the current data set ( results not shown ) . Alternatively , spatial population structure and epistatic effects may reduce the effective population size for resistance mutations . We obtained our population size estimate under the assumption—most certainly violated in reality at least to some extent—that all patients have the same constant effective population size . Unfortunately , we do not have sufficient statistical power to relax this assumption . Longitudinal data with denser and deeper sampling is required to study inter-patient and temporal heterogeneity in viral adaptation . We observe soft and hard sweeps in HIV , which leads to an estimated effective population size relevant for adaptation of around . This number is much higher than what the observed level of synonymous diversity suggests . An important caveat of our approach is that we assume that the supply rate of beneficial mutations is similar in all patients , which may not be true . Larger samples are needed to estimate the patient-specific supply rate of adaptive mutations . Our results show that neither synonymous diversity , nor the census population size provide sufficient information to infer a population's adaptive potential , which explains , in part , why it is hard to predict whether and when adaptive evolution will occur . Our finding that adaptive evolution in HIV is limited by the supply rate of beneficial mutations suggests that a relatively modest reduction of the effective population size may help prevent the evolution of drug resistance , and it is possible that the reduction of that results from modern combination therapy , does just that [21] . In addition , if the adaptive potential of the virus is limited because a large proportion of the beneficial mutations land on inferior genetic backgrounds , then the use of mutagenic drugs , in combination with standard antiretroviral drugs , should reduce the adaptive potential of the virus [43] . Finally , our results confirm what has already been predicted theoretically , that the idea of a single effective population that can be used to describe different aspects of the behavior of a population does not work [13] , [44] .
Out of a larger dataset [12] , we used viral sequences from patients for which we had samples at two consecutive time-points that satisfy the following two criteria . ( 1 ) At the first time-point , no known resistance mutations to any of the drugs used in the trial was present at more than frequency in the sample . See below for the list of drugs and mutations . ( 2 ) At the next time-point , at least one drug resistant allele increases in frequency by at least . There were 30 such patients . In most cases ( 26 out of 30 ) the frequency of the mutations changes from 0 to . The four exceptions are patient 89 ( mutation G190S changed from to ) , patient 168 ( mutation K103N changed from to ) , patient 22 ( mutation K103N changed from to , while mutation V82A changed from to ) , and patient 81 ( mutation K103N changed from to ) . In most cases ( 24 out of 30 ) only a single drug-resistance mutation went to fixation ( 2 mutations in patients 22 , 56 , 87 , 91 , 154 and 166 ) . For details per patient , see supplementary table S1 . The patients were treated with Zidovudine , Lamivudine , Efavirenz and Indinavir [12] . There are many known mutations that confer resistance to one or more of these drugs . We are interested in the fixation of the first resistance mutation in a viral population . We used the following list of major drug-resistance mutations ( the number is the codon and the letter is the amino-acid that confers resistance ) . Protease: 46IL , 82AFT , 84V; Reverse Transcriptase: 41L , 62V , 65R , 67N , 70R , 75I , 77L , 100I , 101P , 103N , 106MA , 108I , 116Y , 151M , 181CI , 184VI , 188LCH , 190SA , 210W , 215YF , 219QE , 225H [45] . Our dataset consists of coding sequences of the regions of the Pol gene that encode for protease ( all 297 basepairs ) and reverse transcriptase ( first 689 basepairs ) . We analyze separately all third codon position sites and all first and second codon position sites . Most observed mutations at third codon position sites have no effect on the amino acid , and we expect these synonymous mutations to be neutral or nearly neutral . Throughout the paper , we refer to mutations at first and second position sites as non-synonymous and mutations at third position sites as synonymous . Most mutations at the first or second codon positions change the amino acid , and we expect that most such non-synonymous mutations are selected against . No change in genetic diversity was observed in 27 control intervals that also started with drug susceptible virus but in which no fixation of drug resistant amino-acids occurred ( largest frequency change of drug resistant amino-acid in these intervals was ) . We found no difference in pre-sweep diversity at third codon position sites between hard sweep and soft sweep patients . To determine whether recombination affects the reduction of diversity , we split the sequences into a part close to the selected site ( less than 50 , 100 , or 200 basepairs distance from the selected site ) and the complementary part far from the selected site . We found no difference in the amount of diversity loss in the close versus far sites . Earlier work suggests that selection may be so strong , relative to the recombination rate , that the sweeps can affect the entire genome [17] , [41] . We can use the counts of the two beneficial alleles ( AAT and AAC at the 103rd amino acid of RT ) in the post-sweep samples to estimate the the supply rate of resistance mutations . We assume that the mutation rate to the AAT codon and the mutation rate to the AAC codon are equal to each other , . If is the effective population size , then , and is the total population-wide beneficial mutation rate , which we also call “the supply rate of resistance mutations” . Theory predicts that the population frequency of the AAC allele follows a beta-distribution with parameters ( see [4] ) . The number of AAC alleles in a sample of size follows the binomial distribution with parameters and . Thus , the likelihood of observing AAC alleles in samples of size in patients is given by the product of beta-binomial distributions , ( 1 ) where is the beta-function . By maximizing expression ( 1 ) , we obtain the maximum likelihood estimate . The estimated supply rate of K103N mutations is . The bootstrap confidence interval for the supply rate obtained by resampling patients with replacement is . We estimate the effective population size as . Because we only consider the K103N mutation , [19] , [46] . Note that this number is lower than the typical per site mutation rate , because most ( ) mutations in HIV are transitions , whereas the A to C and A to T mutations that create the K103N change , are both transversions . We thus estimate the effective population size to be ( confidence interval ) . For a given sample size and a given supply rate of beneficial mutations , we can also predict the probability that the beneficial mutation originates from 1 , 2 or more mutational origins following [4] . For a sample of size 6 ( the median in the dataset ) and the estimated supply rate of beneficial mutations of , the predicted probability that all observed beneficial alleles in the sample stem from a single origin is , the probability that they stem from 2 origins is , 3 origins: and 4 , 5 or 6 origins: . This shows that even if the supply rate is exactly the same in all populations , the observed sweep signature can vary widely .
|
It is well known that HIV can evolve to become drug resistant if it acquires specific drug-resistance mutations , but the underlying population genetic processes are not well understood . We found that the evolution of drug resistance in HIV populations within infected patients occurs by one mutation at a time ( as opposed to multiple mutations simultaneously ) and involves both hard and soft sweeps . In a hard sweep , a mutation originates in a single viral particle and then spreads to the entire viral population within the patient . As this mutation increases in frequency , other mutations linked to it hitchhike to high frequencies , which greatly reduces genetic diversity in the population . In a soft sweep , on the other hand , the same resistance mutation originates multiple times on different genetic backgrounds , and hitchhiking may have very little or no effect on diversity . The fact that drug resistance evolves by means of both hard and soft sweeps implies that the HIV populations are limited by the supply of resistance mutations . Using the frequency of hard and soft sweeps we obtain a point estimate of 150 , 000 for the effective population size of the virus , a number that is much higher than estimates based on diversity at neutral ( synonymous ) sites , but much lower than the actual number of HIV infected cells in a human patient .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"effective",
"population",
"size",
"natural",
"selection",
"virology",
"genetics",
"population",
"genetics",
"biology",
"evolutionary",
"theory",
"evolutionary",
"biology",
"genomic",
"evolution",
"genetics",
"of",
"disease",
"evolutionary",
"genetics",
"microbiology"
] |
2014
|
Loss and Recovery of Genetic Diversity in Adapting Populations of HIV
|
Pseudomonas aeruginosa is an opportunistic human pathogen that is a key factor in the mortality of cystic fibrosis patients , and infection represents an increased threat for human health worldwide . Because resistance of Pseudomonas aeruginosa to antibiotics is increasing , new inhibitors of pharmacologically validated targets of this bacterium are needed . Here we demonstrate that a cell-based yeast phenotypic assay , combined with a large-scale inhibitor screen , identified small molecule inhibitors that can suppress the toxicity caused by heterologous expression of selected Pseudomonas aeruginosa ORFs . We identified the first small molecule inhibitor of Exoenzyme S ( ExoS ) , a toxin involved in Type III secretion . We show that this inhibitor , exosin , modulates ExoS ADP-ribosyltransferase activity in vitro , suggesting the inhibition is direct . Moreover , exosin and two of its analogues display a significant protective effect against Pseudomonas infection in vivo . Furthermore , because the assay was performed in yeast , we were able to demonstrate that several yeast homologues of the known human ExoS targets are likely ADP-ribosylated by the toxin . For example , using an in vitro enzymatic assay , we demonstrate that yeast Ras2p is directly modified by ExoS . Lastly , by surveying a collection of yeast deletion mutants , we identified Bmh1p , a yeast homologue of the human FAS , as an ExoS cofactor , revealing that portions of the bacterial toxin mode of action are conserved from yeast to human . Taken together , our integrated cell-based , chemical-genetic approach demonstrates that such screens can augment traditional drug screening approaches and facilitate the discovery of new compounds against a broad range of human pathogens .
Microbial resistance flourishes in hospitals and community settings , and represents a major threat to human health worldwide [1] , [2] . Despite the threat , drug discovery methods have failed to deliver new effective antibiotics [3] . This problem is likely to worsen because major pharmaceutical and biotech companies are withdrawing from antibacterial drug discovery [4] . To address the challenge of developing new antibiotics and managing microbial resistance , alternative strategies are needed to define and inhibit pharmacologically validated targets [5] . Several lines of evidence support the hypothesis that bakers yeast Saccharomyces cerevisiae can contribute during early stages of antimicrobial development . Because many essential molecular mechanisms of cells are conserved , we hypothesized that bacterial virulence proteins could act similarly in both yeast and human cells . Indeed , the study of virulence proteins in S . cerevisiae has proved an effective alternative and proxy for a human model of bacterial infection [6] , [7] , [8] . In addition , S . cerevisiae is well-suited for screening small molecule inhibitors to inhibit overexpressed proteins [9] , [10] , and to discover molecules that disrupt protein-protein interactions [11] . Finally , the arsenal of available yeast functional genomics tools provides a powerful means to study the targets and pathways modulated by drugs ( reviewed in [12] ) . Together , these observations support the idea that compound screening in S . cerevisiae is a powerful tool to isolate small molecule inhibitors against potential drug targets of human pathogens . In antibacterial drug discovery , a particular concern is the emergence of multidrug resistant strains that require several drugs for efficient disease management . This problem is exacerbated in immunocompromised patients [13] . For example , P . aeruginosa affects immunocompromised individuals afflicted with cystic fibrosis and is the primary Gram-negative causative agent of nosocomial infections [14] . P . aeruginosa is resistant to the three major classes of antibiotics , namely β-lactams , aminoglycosides and fluoroquinolones [15] . Notably , P . aeruginosa strains have demonstrated an alarming ability to resist antibiotics , underscoring the need to discover novel molecules with new mechanisms of action [16] , [17] . Ironically , there are few innovative antibacterial molecules available or under development and the majority of these target Gram-positive bacteria [18] . Therefore , research on the opportunistic Gram-negative bacterium P . aeruginosa is medically relevant and is a logical choice to explore the utility of the yeast-based approach to discover new small-molecule inhibitors . A key feature of a number of Gram-negative bacterial infection is the Type III Secretion System ( T3SS ) [19] . P . aeruginosa manipulate host cells by injecting four effector proteins , exoenzyme S ( ExoS ) , exoenzyme T ( ExoT ) , exoenzyme Y ( ExoY ) and exoenzyme U ( ExoU ) , through the T3SS . ExoS and ExoT are bifunctional enzymes containing an amino-terminal GTPase-activating protein domain and a carboxy-terminal ADP-ribosylation domain . They inhibit phagocytosis by disrupting actin cytoskeletal rearrangement , focal adhesions and signal transduction cascades [20] . ExoY is an adenylate cyclase that elevates intracellular levels of cyclic AMP and causes actin cytoskeleton reorganization [21] . ExoU is a phospholipase whose expression correlates with acute cytotoxicity in mammalian cells [6] , [22] . Therefore , targeting P . aeruginosa virulence factors with small molecule inhibitors would be expected to modulate the pathogen's virulence and provide a starting point for antimicrobial drug development [23] . The pivotal role played by ExoS during P . aeruginosa infection validates this toxin as a promising target to discover small molecules that may interfere with P . aeruginosa pathogenicity and infectivity [20] . ExoS was initially described as a secreted ADP-ribosyltransferase ( ADPRT ) [24] . The toxin is a well-characterized bi-glutamic acid transferase that requires interaction with a 14-3-3 protein ( FAS ) for its activity [25] , [26] . Vimentin was characterized as the first direct target of ExoS ADPRT activity [27] . Shortly after , Ras and related proteins including Rab3 , Rab4 , Ral , Rap1A and Rap2 were identified as been modified by ExoS [28] , [29] . ADP-ribosylation of Ras at arginine 41 blocks the interaction of Ras with its guanine nucleotide exchange factor ( GEF ) , resulting in the inactivation of the Ras signal transduction pathway in the infected host cell [30] . Recently , ExoS was shown to ADP-ribosylate diverse molecules including cyclophilin A and Ezrin/Radixin/Moesin ( ERM ) proteins [31] , [32] . During infection of HeLa and fibroblast cells , P . aeruginosa translocates ExoS and induces cell death by apoptosis [33] , [34] . In this study , we used a novel combined phenotypic and chemical genomics screen in yeast to identify the first small molecule inhibitors of P . aeruginosa ExoS . The compound , exosin , modulates the toxin ADP-ribosyltransferase enzymatic activity in vitro suggesting the inhibition is direct . Furthermore , we observed a protective effect with this compound against P . aeruginosa in a well-established mammalian cell infection assay . Interestingly , although we designed the yeast phenotypic screen to assay heterologous proteins , we also observed that yeast Ras2p is directly modified by ExoS and we biochemicaly characterized this modification . This result reveals that bacterial toxins can target similar proteins in both human and yeast and validates our yeast-based approach for the study of toxin function and for the high-throughput screening for small molecule inhibitors . These initial lead compounds can be used as a starting point for new therapeutic treatments or can help to characterize the cellular functions of bacterial proteins . A similar strategy could also be applied to facilitate the discovery of new compounds against a broad range of human pathogens .
We developed a yeast-based strategy where S . cerevisiae was initially used to identify P . aeruginosa PAO1 virulence factors or essential ORFs that inhibit yeast growth ( Figure 1A ) . These particular genes were selected because they provide an attractive starting point to develop antibacterial drugs . Accordingly , we developed a list of 505 potential drug targets of P . aeruginosa ( Table S1 ) [35] , [36] , [37] . These bacterial ORFs were individually transferred into the yeast expression vector , pYES-DEST52 where the GAL1 promoter controlled their expression . Transformed yeast growing on 2% glucose served as control ( i . e . , wild type growth ) because in these conditions , the expression of the exogenous bacterial genes is repressed . Expression of these genes was induced by growing the yeast on selective solid medium containing 2% galactose + 2% raffinose ( Figure 1B ) . The experiment was repeated ( four times ) , and involved inoculating yeast cultures at different dilutions and spotting variable volumes of culture on agar plates in an attempt to increase the consistency of this test . Of the 505 P . aeruginosa ORFs screened , nine strongly or partially impaired the yeast growth when overexpressed ( Figure 1C ) . Five of these are essential genes , including; 1 ) the ribonuclease III – PA0770 , 2 ) two probable transcription regulators - PA0906 and PA1520 , 3 ) the transcription termination factor Rho – PA5239 and 4 ) a hypothetical protein – PA2702 . In addition , four virulence genes were also detrimental to yeast growth , ExoA – PA1148 , ExoS – PA3841 , ExoT – PA0044 , ExoY – PA2191 . Interestingly , each of these four toxins are secreted or translocated by the type II ( ExoA ) or type III secretion system ( ExoS , ExoT , ExoY ) and each act within the infected host cell . By comparing the phenotype of yeast harboring the empty vector , we could assess the relative strength of the Pseudomonas gene overexpression effect and classify them into three groups ( Figure 1C – right panel ) . Firstly , ExoA , ExoY , PA1520 and PA2702 strongly inhibited S . cerevisiae growth . Secondly , ExoS , ExoT , PA0906 and transcription termination factor Rho showed an intermediate growth impairment whereas expression of ribonuclease III weakly affected yeast fitness . To assess the influence of ExoA , ExoY and ExoS enzymatic activities on yeast growth , catalytic mutants were assayed . Residues important for the enzymatic activity of ExoA ( E553A ) , ExoY ( K81M ) and ExoS ( R146W and E379A+E381A ) were previously reported [21] , [38] , [39] , [40] and served to guide our mutant construction ( Figure 2A ) . Compared to the empty vector control , overexpression of active ExoA-wt and ExoY-wt induced a severe growth defect ( Figure 2B – top and middle panels ) whereas expression of the enzymatically inactive ExoA-ADPRT mutant and ExoY-AC mutant did not . This observation suggests that ExoA and ExoY toxicity is conferred by their ADPRT and AC activities , respectively . Moreover , whereas ExoS-wt expression reduced yeast growth , this dominant negative effect was totally abolished when expression of the ExoS-GAP and ExoS-ADPRT mutants were simultaneously induced , indicating one or both ExoS enzymatic activities are causative for the yeast growth defect ( Figure 2B – bottom panel ) . Because normal growth was observed only for the ExoS ADPRT domain mutant and not for the GAP mutant , this suggests that ExoS ADPRT enzymatic activity is responsible for the yeast toxicity consistent with previous observations [41] . Taken together , these observations attribute the yeast growth inhibition to the ExoA-ADPRT , ExoY-AC and ExoS-ADPRT activities and validate the three toxins as appropriate drug target candidates for further study . Due to its critical role in the initial steps of chronic infections of immuno-compromised patients and in the pathogenesis of acute P . aeruginosa infections , ExoS was selected for interrogation using our yeast-cell based inhibitor screen . To demonstrate that yeast can serve as a model system to mimic human cells during infection , we asked if these bacterial toxins modulate the biological activity of conserved eukaryotic targets . Following binding of P . aeruginosa to human cells , the bacteria inject ExoS directly into the cytoplasm where it inhibits the activity of several targets by ADP-ribosylation . Therefore , overexpressing yeast homologues of ExoS human targets should restore yeast growth by titrating the toxin's enzymatic activity ( Figure 3A ) . To test our hypothesis , forty-six yeast members of the Ras superfamily and cyclophilins were individually overexpressed in yeast in the presence of ExoS ( Table S2 ) . We first verified that individually , the overexpressed yeast proteins did not impair yeast growth . To accomplish this , cells were cultivated on galactose + raffinose in absence of copper such that only the yeast over-expressed candidates , but not ExoS , were expressed ( Figure 3B – top right panel ) . Yeast genes whose overexpression was toxic were eliminated from our analysis . In parallel , cells were grown on galactose + raffinose in presence of copper to assess the rescuing effect of yeast gene overexpression in the presence of the toxic ExoS ( Figure 3B – bottom right panel ) . Comparing yeast growth to the cell harboring the empty vector and yeast expressing ExoS alone ( Figure 3B – bottom left panel ) , ten yeast genes were found to rescue ExoS toxicity ( Table S3 ) . Subsequently , only yeast genes demonstrating a strong growth rescue phenotype ( such as RAS2 ) were analyzed further whereas genes showing weak rescue ( such as YPT1 ) were not studied further ( Figure 3B ) . S . cerevisiae possesses two homologues of the human Ras protein ( Ras1p and Ras2p ) . Interestingly , Ras2p was found among these ten ORFs , i . e . overexpression of Ras2p but not Ras1p rescued ExoS-induced toxicity ( Figure 3B ) . As previously described , ExoS requires Factor Activating Exoenzyme S ( FAS ) for its ADPRT activity [26] . FAS is a member of the 14-3-3 protein family which has two yeast homologues , the Brain Modulosignalin Homolog ( Bmh ) 1 and 2 . Accordingly , ExoS toxicity was assessed in the absence of Bmh1p or Bmh2p . As detected by the increase in yeast growth , ExoS-induced toxicity was diminished in cells lacking Bmh1p but not in those lacking Bmh2p ( Figure 3C – left panel ) . In a bmh1Δ yeast background , the toxic effect of ExoS was again restored when introducing BMH1 in the presence of the toxin ( Figure 3C – right panel ) . Together , these data imply that Bmh1p acts as ExoS cofactor in yeast . To better understand the mechanism of this toxicity , we demonstrated that yeast Ras2p was a direct target of ExoS and that Bmh1p was the ExoS cofactor in yeast , using a biochemical assay . To that end , an ADP-ribosyltransferase enzymatic assay was performed using the radioactive substrate [32P]-NAD+ , purified P . aeruginosa ExoS , yeast Ras2p and Bmh1p . Autoradiographic analysis showed that radioactive ADP-ribose was incorporated by Ras2p ( Figure 3D ) . Moreover , in absence of Bmh1p , no ADP-ribosylation was observed . These data reveal that in vitro , Ras2p is directly ADP-ribosylated by ExoS with Bmh1p as a cofactor . Taken together , these results allow us to conclude the following; ( i ) in yeast , the growth inhibitory effect observed in the presence of the P . aeruginosa ExoS is mediated by its ADPRT activity , ( ii ) this growth inhibition is due , at least in part , to the inactivation of the yeast protein Ras2p by ADP-ribosylation , ( iii ) ExoS ADPRT activity is activated by the yeast cofactor Bmh1p . Most significantly , conservation of several toxin targets from yeast to human , such as Ras2p , Rsr1p , Ypt52p and Cpr6p , suggests that P . aeruginosa ExoS acts in a related manner in both organisms . The sensitivity and specificity of our yeast-based assay allowed us to use S . cerevisiae to detect potential inhibitors of the three selected P . aeruginosa drug targets . Because ExoS-wt inhibited yeast growth when overexpressed , we reasoned that any molecule that inhibits this enzyme should restore yeast growth ( Figure 1A ) . Because we were unable to find any inhibitors when the bacterial toxin was expressed using the strong promoter GAL1 , we exchanged the GAL1 promoter with the copper inducible promoter CUP1 which allows a titrable expression of the toxins . Expression from this promoter decreases the toxin level in yeast and renders the conditions of the yeast screen less stringent . Over 56 , 000 compounds , primarily synthetic small molecules , were tested against ExoS . Effect of the compounds was compared to the yeast growth in absence of compound ( as control for inhibition ) and to the cells dividing in absence of toxin ( as a control for growth ) . With this strategy , we uncovered six potential inhibitors , Diosmin , Everninic acid , Flavokawain B , 0469-0796 , 4296-1011 and E216-5303 based on their ability to restore yeast growth ( Figure 4A ) . To determine if the observed yeast growth recovery was due to a direct modulation of the compounds on ExoS ADPRT activity , an in vitro fluorescent ADPRT enzymatic assay was performed . Diosmin , 4296-1011 , Everninic acid and E216-5303 modulated ExoS ADPRT activity and their IC50 values were determined as 3 , 6 , 21 and 23 µM respectively ( Figure 4A ) . Due to their intrinsic fluorescence , Flavokawain B and 0469-0706 effects could not be tested in our enzymatic assay . Because , only exosin protected CHO cells from lysis during P . aeruginosa infection in cell culture ( data not shown ) it was therefore selected for additional studies . Exosin acts as competitive inhibitor against the NAD+ substrate of ExoS as the Vmax values were largely unaffected , whereas the KM values increased from 9 to 30 µM ( Figure 4C ) . The Ki value ( dissociation constant for a competitive inhibitor ) was 33 . 0 ± 3 . 0 µM for exosin ( Figure 3D ) , which agrees favourably with the IC50 value for this compound ( Figure 4B ) . Thus , the drug-like compound exosin directly modulates ExoS ADPRT activity in vitro via competitive inhibition . Therefore , exosin seems to restore ExoS dependant yeast growth defect by directly inhibiting ExoS ADPRT activity . To determine if the small molecule inhibitor , exosin , could modulate the viability of CHO cells during P . aeruginosa infection , apoptotic CHO cells and living CHO cells were distinguished using the exclusion dye 7-AAD . Here , CHO cells were exposed to P . aeruginosa with or without the small molecule inhibitor for 2 hours , and the fraction of apoptotic cells was measured by 7-AAD staining and flow cytometry . The mean fluorescent intensities of 7–AAD were plotted as a histogram ( Figure S1 ) . When compared to the mean fluorescent intensity from the red peak ( background fluorescent intensity - 7 . 42; n = 3 ) and from the blue peak ( control for P . aeruginosa infection - 18 . 2; n = 3 ) , the green ( 20 µM ) , orange ( 40 µM ) , and light blue ( 80 µM ) peaks gave mean fluorescent intensities of 13 . 4 , 11 . 3 , and 10 . 3 , respectively , indicating that exosin exerted its effect in a dose-dependent manner . Therefore , a higher inhibitor dose reduced the number of cells undergoing apoptosis , reflecting a better protective effect . Similar observations were made in dot plots ( Figure 5A ) . In the presence of 80 µM exosin , a significant increase in the percentage of living cells ( 79 . 35%; n = 3 ) was observed with the serious reduction of dead cells ( 20 . 31%; n = 3 ) , compared to the infected CHO cells without inhibitor , 0 µM ( 49 . 72% and 50 . 28% , respectively ) . However , the protective effect of exosin at a concentration of 80 µM was not observed when CHO cells were infected by the P . aeruginosa PA14 strain , a strain expressing ExoT , ExoY and the phospholipase exoenzyme U ( ExoU ) but not ExoS , indicating the specificity of the compound exosin against ExoS only ( Figure 5A – lower panels ) . In the CHO cell infection assay , the protective effect of exosin was monitored during an early stage of infection by detecting the number of dying and dead CHO cells using flow cytometry . Moreover , the effect of the inhibitor at the late stage of infection was assessed by the quantification of lactate dehydrogenase ( LDH ) released from the population of lysed CHO cells . Four hours after P . aeruginosa PAK infection ( Figure 5C ) revealed a 6 . 93% decrease in lysis upon addition of 20 µM exosin , a 13 . 92% lysis reduction in the presence of 40 µM final of inhibitor and a 12 . 90% reduction at 80 µM . However , the protective effect of exosin at a concentration of 80 µM was not observed when CHO cells were infected by the P . aeruginosa PA14 strain that translocates ExoU instead of ExoS ( Figure 5C ) . Together , these data strongly support the conclusion that the inhibitor exosin is specific for ExoS and is able to reduce ExoS cytotoxicity against mammalian cells . P . aeruginosa PAK viability was tested by measuring optical density of cultures in the presence of 20 , 40 and 80 µM of inhibitor over a period of 10 hours . Addition of exosin did not affect Pseudomonas growth , further confirming the specificity of exosin for ExoS in CHO cells ( Figure S2 ) . Given the specificity of exosin , we screened 50 structural analogues of this compound in yeast to find molecules with increased potency against ExoS ADPRT activity . Seven analogues with an improved effect were found ( Figure 4E – exosin-5138 , exosin-5316 and the compounds marked by an asterisk ) . According to the flow cytometry results , all of these compounds protected CHO cells when infected with P . aeruginosa in cell culture ( data not shown ) . However , only exosin-5138 and exosin-5316 showed a protective effect when monitored with the LDH assay . Therefore , only these two compounds were used for further investigation . Exosin-5340 had no protective effect in yeast or in the CHO cell infection assay and served as a negative control . Importantly , results obtained from the yeast studies revealed the importance of the para position of the nitrobenzyl ring for the inhibitory activity of the compounds ( Figure 4G ) . The three different analogues , exosin-5138 , exosin-5316 and exosin-5340 , were then selected for quantification of the yeast growth recovery and for IC50 determination . Exosin-5138 showed 36 . 8% recovery , almost double the protective effect of exosin whereas exosin-5340 conferred no protection ( Figure 4F ) . The last analogue exosin-5316 ( with 27 . 4% recovery ) demonstrated a protective effect almost equal to the original compound ( 20 . 5% recovery ) . The fluorescent ADPRT enzymatic assay revealed that the three analogues directly modulate ExoS ADPRT activity in vitro ( Figure 4F ) . The IC50 for each compound was calculated and these values paralleled the effect of the small molecules in yeast , most strongly for exosin-5138 and exosin-5340 , and to a lesser extent for exosin-5316 . We extended our studies of these analogs in mammalian cells . For this purpose , protection provided by exosin-5138 and exosin-5316 was assessed in the CHO cell toxicity assay . Using the flow cytometry as described earlier , a strong protective effect of exosin-5138 was observed ( Figure 5A – left panel ) . Dot plots of exosin-5138 showed a large reduction in dead cells at a compound concentration of 80 µM ( Figure 5A – right panel ) . Exosin-5138 showed decreases of 25 . 20 , 44 . 05 and 60 . 83% in the number of dead/dying CHO cells in the presence of 20 , 40 and 80 µM of inhibitor , respectively ( Figure 5B ) . In the LDH assay , exosin-5138 reduced cell lysis by 9 . 34 , 18 . 61 and 24 . 64% in presence of the compound at 20 , 40 and 80 µM inhibitor , respectively ( Figure 5C ) demonstrating an improved efficacy of exosin-5138 against ExoS cytotoxicity versus the original hit . In addition , as shown by flow cytometry , exosin-5316 exerted a protective effect ( Figure 5A ) . The number of dead/dying CHO cells was detectably lower upon addition of the analogue exosin-5316; however , this reduction was not statistically significant compared to the original hit ( p>0 . 05 ) . In contrast , the LDH assay revealed a 7 . 94 , 12 . 84 and 15 . 81% reduction in cell lysis at 20 , 40 and 80 µM final concentration respectively ( p<0 . 05 ) . The analogue exosin-5316 showed similar protective effect compared the original hit ( p<0 . 05 ) ( Figure 5C ) . The data show a correlation between the protective effect of exosin and its analogues in yeast and for the results obtained in the CHO cell infection assay . Moreover , these observations establish yeast as a powerful assay system to estimate the effect of analogues of an original hit and to prioritize lead compounds before tedious subsequent experiments in a more complicated model of infection are undertaken .
In our report , S . cerevisiae produced a binary readout that allowed us to test 505 P . aeruginosa genes for their inhibitory effect in yeast . Expression of nine bacterial genes , five essential and four virulence genes , reproducibly prevented S . cerevisiae growth . Among the isolated essential genes , the Rho termination factor from E . coli was demonstrated to induce yeast RNA polymerase II release at all pause sites of the mRNA in vitro [42] . Thus , transcription deregulation could explain the yeast growth arrest in the presence of the transcription termination factor Rho . Members of the ribonuclease III superfamily are RNA-specific endonucleases involved in RNA maturation , RNA degradation and gene silencing [43] . We hypothesize that the observed yeast growth defect induced by expression of the P . aeruginosa ribonuclease III was caused by deregulated RNA degradation . Since no clear biological function is associated with the two probable transcriptional regulators – PA0906 and PA1520 , nor for the hypothetical protein – PA2702 , we cannot speculate on the mechanism of action of these proteins in yeast . During infection , P . aeruginosa manipulates host cellular function through the action of the toxins ExoA , ExoS , ExoT and ExoY using the type II and III secretion systems [44] . Since these toxins inactivate key molecules directly within the infected cells and because several basic molecular functions are conserved among eukaryotes , it seemed likely that the toxins could act similarly on targets conserved in both yeast and human . Therefore , inactivation of yeast homologues of the toxin human targets is an attractive and simple scenario to explain the growth inhibition effect conferred by the ExoA , ExoS , ExoT and ExoY expression in yeast . There are many possible reasons to account for the limited number of bacterial genes affecting yeast growth including; ( i ) a proper expression of the bacterial gene ( e . g . high CG content in the Pseudomonas DNA sequence [45] ) , ( ii ) the presence of the target and/or appropriate co-factor and ( iii ) required post-translational modifications . High conservation of basic molecular and cellular mechanisms between yeast and human cells highlights S . cerevisiae as an ideal model organism to study mammalian diseases and their underlying pathways [46] , [47] . Indeed , several reports have shown that this conservation can be used to decipher bacterial toxins mode of action [6] , [7] , [48] . Here , the toxicity caused by ExoA , ExoY and ExoS overexpression in yeast is mediated by their enzymatic activity ( Figure 2B ) . Interestingly , ExoS ADPRT activity alone is sufficient to induce yeast cell growth arrest . This last observation inspired us to study ExoS yeast toxicity into more detail . Once translocated in human host cells via P . aeruginosa type III secretion system , ExoS inhibits several cellular targets . Its GTPase activating protein activity reorganizes the actin cytoskeleton through RhoA , Rac1 and Cdc42 inactivation . In contrast , ExoS ADPRT activity inactivates a wide range of proteins such as several members of the Ras family and related proteins , cyclophilin A and the Ezrin/Radixin/Moesin ( ERM ) proteins . Since ExoS inactivates all its protein targets , overexpression of the yeast homologues of the known human targets should restore yeast growth by titrating ExoS enzymatic activity . The ERM proteins play a role during cell polarity establishment of multi-cellular organisms and no homologues were found in yeast . Therefore , only yeast members on the Ras superfamily and cyclophilin family were induced in our overexpression study . Globally , no yeast homologue of the human RhoA , Rac1 or Cdc42 rescued ExoS-induced toxicity indicating that , together with the results obtained in the ExoS mutagenesis experiments , the ExoS GAP activity does not play a role in ExoS yeast growth inhibition . Four homologues of the human ExoS targets Ras , Rap1b , hRab and CyclophilinA were identified as the yeast Ras2p , Rsr1p , Ypt52p and Cpr6p , respectively . Arf1p , a member of the Sar/Arf family and homologue of the human Arf1 protein , rescued yeast expressing ExoS . Arf1p is involved in the retrograde transport of vesicles from the trans Golgi to the plasma membrane . Biological relevance of Arf1p as a direct target of ExoS is questionable since , during P . aeruginosa infection , ExoS trafficking occurs from the plasma membrane to the perinuclear region [49] . In our system , ExoS is encoded by a plasmid , thus deregulation of the retrograde transport could prevent ExoS reaching its plasma membrane targets ( e . g . Ras2 ) and explain why Arf1p overexpression allowed yeast to divide in the presence of ExoS . No homologues of Cin4p ( Sar/Arf family ) , Cns1p , Cpr7p , Cpr8p exist in human confounding the interpretation of these results . ExoS overexpression in the absence of Bmh1p , the yeast homologue of the ExoS cofactor FAS , was not toxic to yeast ( Figure 3C ) . In vitro enzymatic assay demonstrated that ExoS ADP-ribosylated Ras2p with Bmh1p as a cofactor ( Figure 3B ) . Interestingly , substitution of the yeast Ras2p by the constitutively active Ras2-G19V mutation did not prevent ExoS toxicity ( data not shown ) . Additionally , previous results showed that a deletion mutant of ExoS ( lacking aa 51–72 membrane localization domain ) , which cannot ADP-ribosylate Rasp in vivo , is nevertheless as cytotoxic as wild type ExoS in CHO cells , indicating that Ras ADP-ribosylation is dispensable for ExoS virulence [50] . These observations suggest that , in yeast , the observed growth inhibition may be due to the cumulative inhibitory effects of ExoS on already known targets and/or due to the additional inactivation of an unknown key protein ( s ) . ExoS plays a pivotal role in the establishment of P . aeruginosa chronic infections and during acute P . aeruginosa pathogenesis . For that reason , this toxin was selected in our yeast phenotypic system to find inhibitors capable of modulating its enzymatic activity . Here , we report the isolation of exosin , the first inhibitor of ExoS ADPRT activity using a yeast cell-based screen . Six compounds were isolated from a library of 56 , 000 compounds and all rescued ExoS induced toxicity in yeast ( Figure 4A ) . This relatively small number of hits is likely due to several reasons; both chemical and biological . The compound library , though selected for its diversity , nonetheless samples a limited range of chemical space . Furthermore , of 6 , 000 compounds that were randomly picked from the library and tested against ExoS and ExoY in two different yeast genetic backgrounds ( the wild-type and the pdr1Δ+pdr3Δ strains ) , no difference in potency was observed , suggesting that the yeast genotype did not substantially influence the number of hits obtained in our screen . In the enzymatic and the CHO cells infection assays , only exosin modulated ExoS biological activity . The apparent inactivity of the five other small molecules can likely be explained by two arguments . First , the five inhibitors could exert their effect on molecules or pathways modulated by ExoS without directly inhibiting ExoS ADPRT activity . Furthermore , because these five compounds exerted no protective effect in the CHO cell infection assay we predict they act on yeast specific pathways . A second explanation could be that the effect observed in yeast requires metabolism of the compound and is not an effect of the original compound itself . Our results revealed that compounds derived from natural products were more bioactive on yeast ( 3 primary hits out of 580 natural products ) . However , the only hit conferring protection during infection of CHO cells by P . aeruginosa belongs to the class of the drug-like synthetic compounds ( 3 primary hits out of 53 , 000 compounds ) . Thus , there is certainly much more chemical space that can be probed using both natural and synthetic compounds . Exosin was shown to directly interact with ExoS in vitro as a competitive inhibitor against the NAD+ substrate of ExoS ADPRT activity with comparable IC50 and Ki values indicating that exosin likely binds to the NAD+-pocket within the ADPRT domain of ExoS . This was substantiated by our observation of a similar inhibitory effect of exosin on the ADPRT activity of ExoA ( IC50 = 17 µM; data not shown ) . Unfortunately , no high-resolution structure has been determined for the ADPRT domain of ExoS; however , ExoA is a well-characterized ADPRT enzyme for which there is a crystal structure of the ADPRT catalytic domain in complex with substrates and inhibitors [51] , [52] , [53] . By analogy with the recent X-ray co-crystal structure of ExoA with PJ34 [51] , the benzylmorpholine ring of exosin might be expected to intercalate into the nicotinamide pocket within ExoS . In this scenario , the inhibitor amide should form an H-bond with enzyme . Presently , the site of contact within the ExoS active site for the alkyl group on the exosin nitrobenzyl moiety ( shown by an arrow in Figure 4G ) is not known; however , a single ring is required for in vivo activity and an ethyl is preferred over a methyl at the alkylation site . In summary , although we lack atomic resolution , it appears likely that the in vivo inhibitory activity of exosin against ExoS toxicity is due to a direct interaction of the inhibitor with the ADPRT domain of the toxin . Using a yeast cell-based screen , the first known inhibitor of the P . aeruginosa ExoS , called exosin , was isolated and several analogues of the original hit were characterized . This work was facilitated by the partial conservation of the proteins inactivated by ExoS in both human and yeast . Thus , S . cerevisiae is a powerful tool to study bacterial toxins and to identify their corresponding inhibitors . Future studies could extend a similar approach to a broad range of human pathogens such as viruses and bacteria .
S . cerevisiae strain W303-1A harboring the plasmid pYES-DEST52 coding each of the 505 Pseudomonas aeruginosa PAO1 drug targets was grown overnight in SD–Ura to maintain selection of the plasmid . Yeast culture was directly submitted to 3 steps of a ten fold dilution using the liquid handling robot Q-Bot ( Genetix ) . The non-diluted and diluted cultures were then immediately inoculated in duplicate on solid medium containing either glucose-Ura ( repressing conditions ) or galactose+raffinose-Ura ( inducing conditions ) . Plates were incubated at 30°C and monitored for yeast growth defect after 2 and 3 days . Growth was compare to the fitness of yeast harboring the empty vector and to the yeast harboring the toxic pRS316-TUB2 vector [58] . The LOPAC library ( 1 , 280 compounds , Sigma-Aldrich ) , the SPECTRUM library ( 2 , 000 compounds , MicroSource Discovery Inc . ) and a ChemDiv library ( 53 , 000 compounds ) were screened at a final concentration of 50 µM . S . cerevisiae strain 14328-pdr1+pdr3 harboring the plasmid pDH105-exoA , pDH105-exoS or pDH105-exoY was grown overnight in SD -Leu to maintain selection of the plasmid and were diluted to a cell density of 5×103 cells/ml . Addition of 0 . 9 mM ( ExoA and ExoY ) or 1 . 5 mM ( ExoA ) of CuSO4 induced the expression of the toxin in yeast , the cultures were then aliquoted into wells of 96-well plates and compounds were added . Plates were incubated at 30°C and inspected for yeast growth recovery after 24 and 48 hours . As a control , cells containing the empty vector pDH105 were similarly grown , diluted and inoculated with copper and 0 . 5% DMSO . The effect of the hits on yeast growth recovery was quantified as a percentage of growth as described elsewhere [10] . Chinese hamster ovary ( CHO ) cells toxicity assay was performed as previously described with minor modifications [59] . CHO cells were routinely grown in F-12 medium supplemented with 10% fetal bovine serum ( FBS ) and 2 mM glutamine . Prior to infection , confluent CHO cells were washed and incubated with F-12 containing 1% FBS and 2 mM glutamine . P . aeruginosa was grown overnight in LB , subcultured into fresh LB , and grown to mid-log phase . 2 . 5×105 CHO cells per well were infected with P . aeruginosa at an initial multiplicity of infection ( MOI ) of 10 in duplicate . After 2 hours infection , CHO cells were harvested by trypsination and resuspended in Hank's Balanced Salt Solution + 1% bovine serum albumin . Induced cell death was measured by flow cytometry after 7-AminoActinomycin D ( 10 µg/ml ) staining , using a Beckman-Coulter EPICS Elite flow cytometer . Culture supernatants from a second duplicate of CHO cells were collected after 4 hours of infection and centrifuged for 10 min at 3 , 220×g to sediment bacteria and CHO cells . Lactose dehydrogenase ( LDH ) in the supernatant was measured with a Roche LDH kit in accordance with the manufacturer's instructions . Percent LDH release was calculated relative to that of the uninfected control , which was set at 0% LDH release , and that of cells lysed in absence of inhibitor , which was set at 100% LDH release . Recombinant yeast FAS , human Ras , yeast Ras2p , yeast Bmh1p and Bmh2p were cloned into pEGX ( GE Healthcare ) , transformed in E . coli BL21 and purified according to manufacturer's instructions . Recombinant ExoS was purified by gel filtration and ion exchange chromatography as previously described [60] . To monitor incorporation of ADP-ribose into yeast Ras2 , 50 nM of purified ExoS was added to a 20 µl reaction mixtures containing 1 µM of Bmh1 , 20 µM of yeast Ras2 , 2 mM of MgCl2 and 200 mM of sodium acetate ( pH 6 . 0 ) . The reaction was initiated by adding 2 Ci of ExoS radioactive substrate , nicotinamide adenine [adenylate-32P] dinucleotide ( 32P-NAD ) ( 1000 Ci/mmol , GE Healthcare ) . Reaction mixes were incubated for 0 to 30 min at 30°C . The reaction was terminated with 2× Laemmli sample buffer , resolved by a 15% SDS-PAGE , and analyzed by autoradiography using a Typhoon Trio Workstation ( GE Healthcare ) . The auto-ADP-ribosylation of ExoS served as a positive control in each reaction and reaction missing FAS served as a negative control . The NAD+-dependent ADPRT assay of ExoS was performed at 30°C with ExoS at 50 nM in the presence of 1 µM of FAS and 20 µM human Ras in 100 mM NaCl , 2 mM MgCl2 , 200 mM sodium acetate , pH 6 . 0 while the concentration of ε-NAD+ varied between 0 and 75 µM . The reaction was initiated with the addition of 50 nM ( final conc . ) ExoS in an Ultravette ( Brand Scientific ) 70 µL cuvette and the transferase reaction was monitored by recording the time-dependent change in fluorescence intensity with a PTI AlphaScan-2 fluorimeter ( PTI Inc . , New Jersey ) with 305 nm and 405 nm excitation and emission , respectively . The data were analyzed by nonlinear curve fitting using the Michaelis-Menten equation ( OriginLab v6 . 1; Northampton , MA ) and also by linear regression analysis of both the Hanes-Woolf and the Lineweaver-Burk ( LB ) plots . The Ki values were determined for various inhibitor compounds ( 1 . 4% DMSO , final conc . ) using Dixon plots , as well as from secondary plots of the slope of the LB plots versus inhibitor concentration . IC50 values , the concentration of the inhibitor that reduces the activity of the enzyme by 50% , were determined by non-linear regression curve fitting using Origin 6 . 1 [51] .
|
Microbial resistance to antibiotics is a serious and growing threat to human health . Here , we used a novel approach that combines chemical and genetic perturbation of bakers yeast to find new targets that might be effective in controlling infections caused by the opportunistic human pathogen Pseudomonas aeruginosa . P . aeruginosa is the primary cause of mortality with cystic fibrosis patients and has demonstrated an alarming ability to resist antibiotics . In this study , we identified the first small molecule inhibitors of ExoS , a toxin playing a pivotal role during P . aeruginosa infections . One of these compounds , exosin , likely works by modulating the toxin's enzymatic activity . We further show that this inhibitor protects mammalian cells against P . aeruginosa infection . Finally , we used yeast functional genomics tools to identify several yeast homologues of the known human ExoS targets as possible targets for the toxin . Together , these observations validate our yeast-based approach for uncovering novel antibiotics . These compounds can be used as starting point for new therapeutic treatments , and a similar strategy could be applied to a broad range of human pathogens like viruses or parasites .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"microbiology/applied",
"microbiology",
"biochemistry/drug",
"discovery",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] |
2008
|
Identification of Small Molecule Inhibitors of Pseudomonas aeruginosa Exoenzyme S Using a Yeast Phenotypic Screen
|
Many signaling proteins including G protein-coupled receptors localize to primary cilia , regulating cellular processes including differentiation , proliferation , organogenesis , and tumorigenesis . Bardet-Biedl Syndrome ( BBS ) proteins are involved in maintaining ciliary function by mediating protein trafficking to the cilia . However , the mechanisms governing ciliary trafficking by BBS proteins are not well understood . Here , we show that a novel protein , Leucine-zipper transcription factor-like 1 ( LZTFL1 ) , interacts with a BBS protein complex known as the BBSome and regulates ciliary trafficking of this complex . We also show that all BBSome subunits and BBS3 ( also known as ARL6 ) are required for BBSome ciliary entry and that reduction of LZTFL1 restores BBSome trafficking to cilia in BBS3 and BBS5 depleted cells . Finally , we found that BBS proteins and LZTFL1 regulate ciliary trafficking of hedgehog signal transducer , Smoothened . Our findings suggest that LZTFL1 is an important regulator of BBSome ciliary trafficking and hedgehog signaling .
Primary cilia are microtubule-based subcellular organelles projecting from the surface of cells . Studies during the last decade have shown that primary cilia play essential roles in regulating cell cycle , embryonic development , and tissue homeostasis by acting as a cellular antenna transducing extracellular signals into the cells [1] , [2] . Loss of cilia or ciliary dysfunction has been linked to a series of related genetic disorders in humans [3] , [4] . These disorders , collectively termed ciliopathies , share common features such as cystic kidney disease , retinal degeneration , and polydactyly . Bardet-Biedl Syndrome ( BBS ) is one of the human genetic disorders associated with ciliary dysfunction . Patients with BBS display obesity , polydactyly , retinal degeneration , renal abnormalities , diabetes , hypertension , hypogenitalism , and cognitive impairment . To date , as many as 16 genes have been reported to be involved in BBS [5] , [6] , [7] , [8] ( and references therein ) and molecular functions of BBS proteins have begun to emerge . Among the known BBS proteins , seven proteins ( BBS1 , BBS2 , BBS4 , BBS5 , BBS7 , BBS8 , BBS9 ) and BBIP10 form a stable complex , the BBSome , which mediates protein trafficking to the ciliary membrane [9] , [10] , [11] . BBS3 is a member of the Ras superfamily of small GTPases and controls BBSome recruitment to the membrane and BBSome ciliary entry [11] . Of the remaining , three BBS proteins ( BBS6 , BBS10 , BBS12 ) form another complex with the CCT/TRiC family of group II chaperonins and mediate BBSome assembly [8] . Many receptor proteins and signaling molecules localize to cilia , and the BBSome is involved in transporting at least some of these proteins . For example , several G-protein coupled receptors such as MCHR1 , SSTR3 , and Dopamine receptor 1 ( D1 ) fail to localize to or abnormally accumulate within the neuronal cilia in Bbs2 and Bbs4 null brains [12] , [13] . In Chlamydomonas bbs4 mutants , several proteins aberrantly accumulate within flagella [14] . However , most of the BBSome cargos are currently unknown in mammalian cells . Also unknown is how the trafficking activity of the BBSome is regulated . In an effort to understand how BBSome function is regulated , we initiated studies to identify BBSome interacting proteins in vivo . In this work , we show that LZTFL1 interacts with the BBSome and negatively regulates its trafficking activity to the cilia . We also provide evidence that the BBSome and LZTFL1 are part of the transport mechanism of Sonic Hedgehog ( SHH ) signal transducer , Smoothened ( SMO ) , that localizes to cilia [15] .
To isolate BBSome interacting proteins in vivo , we generated a transgenic mouse line expressing LAP-BBS4 , which allows localization studies and tandem affinity purification using GFP and S tags , under the control of cytomegalovirus ( CMV ) immediate early promoter . We used mouse testis , where BBS genes are the most abundantly expressed . Expression of LAP-BBS4 in the testis is approximately 2–3 fold higher than that of endogenous Bbs4 ( Figure S1A ) . We first tested whether the recombinant LAP-BBS4 protein is functionally equivalent to endogenous Bbs4 . We tested this by three criteria: 1 ) whether LAP-BBS4 physically associates with other BBSome subunits and is incorporated into the BBSome , 2 ) whether LAP-BBS4 properly localizes and reproduces the endogenous Bbs4 localization pattern , and 3 ) whether LAP-BBS4 can functionally rescue the BBS phenotype caused by loss of Bbs4 . To confirm the incorporation of LAP-BBS4 into the BBSome , extracts from wild-type , Bbs4−/− , and LAP-BBS4 transgenic mouse testes were subjected to co-immunoprecipitation ( co-IP ) with anti-GFP antibody . As shown in Figure S1B , pull-down of LAP-BBS4 efficiently co-precipitated all BBSome subunits tested ( BBS1 , BBS2 and BBS7 ) . In spermatozoa , endogenous Bbs4 is found in the middle piece and the principle piece of the flagella ( Figure S1C ) . Within the principle piece , Bbs4 staining is more intense at the proximal end and gradually decreases toward the distal end of the flagellum . LAP-BBS4 detected by GFP antibody shows a similar localization pattern . Finally , introduction of the LAP-BBS4 transgene into Bbs4−/− mice restores sperm flagella , which are lost in Bbs4−/− mice ( Figure S1D ) , and fertility to Bbs4−/− mice; all four transgenic Bbs4−/− males mated with wild-type females produced pups , while Bbs4−/− males without the transgene did not produce any pups . Based on these criteria , we concluded that LAP-BBS4 is functionally equivalent to endogenous Bbs4 . Protein extracts from wild-type and LAP-BBS4 transgenic testes were subjected to tandem affinity purification . In LAP-BBS4 transgenic testis , all BBSome subunits ( Bbs1 , Bbs2 , Bbs5 , Bbs7 , Bbs8 , and Bbs9 ) were co-purified with LAP-BBS4 , while no BBS proteins were purified when wild-type testes were used ( Figure 1A and Table S1 ) . In the LAP-BBS4 transgenic sample , one prominent additional protein was co-purified with BBSome subunits . Mass spectrometry analysis revealed that this protein is Leucine zipper transcription factor-like 1 ( Lztfl1 ) . To confirm the interaction and to identify other LZTFL1 interacting proteins , we generated a stable cell line expressing LZTFL1 with FLAG and S tags ( FS-LZTFL1 ) and conducted tandem affinity purification . In this experiment , at least three BBSome subunits ( BBS2 , BBS7 , and BBS9 ) were co-purified with FS-LZTFL1 ( Figure 1B and Table S2 ) . In addition , we found endogenous LZTFL1 proteins were co-purified , indicating that LZTFL1 forms homo-oligomers . Homo-oligomerization of LZTFL1 was confirmed by co-IP and in vitro crosslinking experiments ( Figure S2D and S2E ) . We also found two additional , smaller isoforms of LZTFL1 . Although it is unclear whether these smaller forms are bona fide LZTFL1 isoforms or cleavage products derived from the over-expressed FS-LZTFL1 , EST database searches revealed the presence of smaller isoforms of LZTFL1 ( AK093705 and AK303416 ) with predicted molecular weights approximately the same as the isoforms observed by us . To determine the LZTFL1-interacting subunit of the BBSome , each individual subunit of the BBSome was co-transfected with LZTFL1 and analyzed for co-IP ( Figure 1C ) . In this experiment , we found that BBS9 is the LZTFL1-interacting subunit of the BBSome . Based on interaction domain mapping studies , the C-terminal half of LZTFL1 ( amino acid ( aa ) 145–299 ) was found to interact with BBS9 ( Figure 1D ) . Within BBS9 , the fragment containing aa 685–765 interacted with LZTFL1 ( Figure S2F ) , which is a part of the α-helix domain at the C-terminus after the α/β platform domain [11] . In size exclusion chromatography , the peak of LZTFL1 was separated from that of the BBSome , suggesting that LZTFL1 is not a constitutive component of the BBSome and only a subset of LZTFL1 is associated with the BBSome ( Figure 1E ) . LZTFL1 maps to human chromosome 3p21 . 3 , which is often deleted in several types of cancer [16] . Recently , tumor suppressor function of LZTFL1 has been proposed [17] . However , very little is known about the molecular functions of LZTFL1 . To gain insight into the structure and functions of LZTFL1 , we performed homology searches . Reciprocal BLAST searches yielded LZTFL1 orthologs in all vertebrates and the flagellate Chlamydomonas reinhardtii , but not in plants , amoebae , or fungi ( Figure S2 ) . LZTFL1 homologs are also not found in Caenorhabditis elegans , Drosophila melanogaster , and planaria ( Schmidtea mediterranea ) . Sequence and secondary structure analyses indicated that LZTFL1 is mostly alpha-helical and has a coiled-coil domain in its C-terminal half ( Figure S2 ) . A leucine-zipper domain is present as part of the coiled-coil domain . InterPro Domain Scan analysis indicates that aa 212–295 of LZTFL1 has sequence homology to the t-SNARE domain ( IPR010989 ) . The structure of this domain in rat Syntaxin-1A ( Stx1A ) was previously determined ( Figure S2C ) [18] . The t-SNARE domain of Stx1A forms a rod-like α-helix and is involved in hetero-tetramer formation with other SNARE proteins . Purified Stx1A t-SNARE domain also forms a homo-tetramer [19] , suggesting that LZTFL1 may form a similar structure . We examined expression and localization of LZTFL1 . Antibodies against LZTFL1 selectively recognized a protein band at ∼36 kDa in SDS-PAGE ( Figure 2A and Figure S3B ) . This protein was diminished in cells transfected with siRNAs against LZTFL1 , verifying the specificity of the antibody . Lztfl1 expression was detected in almost every tissue tested except for skeletal muscle and white adipose tissue ( Figure S3A ) . In immunolocalization studies using hTERT-RPE1 cells , LZTFL1 was detected throughout the cytoplasm ( Figure 2B ) . In contrast to BBS proteins , which show ciliary and centriolar satellite localization ( Figure 2D and Figure S4 ) , we did not find enrichment of LZTFL1 around the centrosome or within cilia . GFP-tagged LZTFL1 also showed cytoplasmic localization with no enrichment in the cilia or centrosomes ( data not shown ) . Similar results were obtained from IMCD3 cells and HEK293T cells ( Figure 2B and Figure S3C ) . Consistent with this , we found most of the Lztfl1 immuno-reactivity in the inner segment of the photoreceptor cells and posterior side of the cell body of spermatozoa ( Figure S3D , S3E ) . Since the localization pattern of LZTFL1 is significantly different from that of BBS proteins , we examined where the BBSome-LZTFL1 interaction occurs . Using the in situ proximity-mediated ligation assay [20] , LZTFL1 bound BBSomes were found scattered throughout the cytoplasm ( Figure 2C ) . These data indicate that LZTFL1 and BBSome interaction occurs within the cytoplasm . Next , we sought to determine the biological functions of LZTFL1 . Since the BBSome is involved in protein trafficking to the ciliary membrane and LZTFL1 interacts with the BBSome , we investigated whether LZTFL1 has any cilia related functions . To this end , we examined whether LZTFL1 is involved in cilia formation , cilia stability , or cilia length . We found no differences in cilia formation after serum withdrawal , cilia stability upon serum treatment , or the length of cilia in LZTFL1 depleted hTERT-RPE1 cells ( data not shown ) . We next examined whether LZTFL1 is involved in BBSome assembly , BBS protein stability , or BBSome trafficking . We found no defects in BBSome assembly or BBS protein stability in LZTFL1 depleted cells ( Figure 3B and data not shown ) . However , we noticed a dramatic alteration in BBSome localization when we ablated LZTFL1 . Normally , BBS proteins localize either within cilia or around centrosomes ( Figure 2D and Figure S4 ) . In our experimental conditions , some 42% of control siRNA transfected cells show ciliary localization of BBS9 ( Figure 2D , 2F ) . However , when LZTFL1 was depleted by RNA interference ( RNAi ) , we observed a consistent and striking increase of BBS9 within the cilia with a concomitant decrease in the centriolar satellite pool of BBS9 . In contrast , over-expression of wild-type LZTFL1 inhibited ciliary localization of BBS9 ( Figure 2E , 2F ) . Interestingly , over-expression of an LZTFL1 deletion mutant , which lacks the N-terminal 70 amino acids , behaved as a dominant negative form and increased ciliary localization of BBS9 . Substitution of two highly conserved basic amino acids ( KR ) within that region ( Figure S2A ) was sufficient to cause the same increase in BBS9 ciliary localization . Similar results were observed for BBS4 and BBS8 ( Figure S4 ) , indicating that LZTFL1 regulates ciliary localization of the entire BBSome rather than merely BBS9 . Combined with the LZTFL1 localization and in situ PLA results , our data indicate that LZTFL1 binds to the BBSome in the cytoplasm and inhibits BBSome ciliary entry . Alternatively , LZTFL1 may promote ciliary exit of the BBSome . Our data also suggest that LZTFL1 has bipartite functional domains; the C-terminal half of LZTFL1 is responsible for BBSome binding and the N-terminal half is for regulating BBSome trafficking activity . To test whether LZTFL1 regulates general intraflagellar transport ( IFT ) , we examined localization of IFT proteins in LZTFL1 over-expressing and depleted cells . In contrast to BBS proteins , IFT57 and IFT88 were found in almost every cilium ( Figures S4 and 5 ) . Depletion of LZTFL1 did not further increase the frequency of ciliary localization of IFT proteins or the fluorescence intensity . More importantly , over-expression of wild-type or mutant variants of LZTFL1 had no impact on ciliary localization of IFT proteins . These data suggest that LZTFL1 is a specific regulator of BBSome ciliary trafficking but not general IFT . Previously , several BBSome subunits have been shown to be essential for BBSome ciliary localization in C . elegans and C . reinhardtii [14] , [21] . However , it has not been systematically investigated which BBSome subunits are essential for BBSome ciliary localization and which are dispensable . To address this , we transfected siRNAs against each BBSome subunit and BBS3 , which is involved in BBSome ciliary trafficking [11] , into hTERT-RPE1 cells and probed localization of BBS8 and BBS9 . Quantitative real-time PCR results confirmed efficient knock-down of each BBS gene expression after siRNA transfection ( Figure S6 ) . By using immunofluorescence microscopy , we found that all BBSome subunits and BBS3 are required for BBSome ciliary localization and loss of any single BBSome subunit precluded ciliary entry of the BBSome ( Figure 3A and Figure S7 ) . Interestingly , however , the localization pattern of BBS9 ( and BBS8 ) was distinct depending on which BBSome subunit was depleted , suggesting differences in their roles within the BBSome . For example , in BBS1 depleted cells , both BBS8 and BBS9 showed concentric enrichment around the centrosomes with a great increase in the staining intensity ( Figure 3A and Figure S7A ) . This suggests that BBSome components may form aggregates near the centrosomes in the absence of BBS1 . Alternatively , BBS1 may be required to return the BBSome back to the cytoplasm . Sucrose gradient ultracentrifugation results are more consistent with the second possibility ( Figure 3B ) . In BBS2 depleted cells , overall staining intensity of BBS9 was greatly decreased . When we measured BBS9 protein level in BBS2 depleted cells , the amount of BBS9 was significantly reduced compared to control siRNA or other BBS gene siRNA transfected cells ( Figure S6B ) . In addition , we found BBS2 protein level was also significantly decreased in BBS9 depleted cells , suggesting that BBS2 and BBS9 are dependent on each other for stable expression . Together , these data indicate that only the intact BBSome can enter the cilia . To gain insight into the molecular basis of this requirement of each BBSome subunit for BBSome ciliary entry , we investigated the status of BBSome assembly after individual BBSome subunit depletion ( Figure 3B ) . To this end , hTERT-PRE1 cells were transfected with siRNAs against control and each BBS gene , and cell lysates were analyzed by 10–40% sucrose gradient ultracentrifugation . Consistent with the previous result and formation of the BBSome [9] , BBS4 and BBS9 were found in the same fraction in control siRNA transfected cells . However , in BBS1 depleted cells , BBS4 and BBS9 were found in separate and lower molecular weight fractions , indicating that BBS4 and BBS9 were not associated in the absence of BBS1 . Similarly , depletion of BBS2 and BBS9 also caused disintegration of the BBSome with a significant reduction in BBS9 levels . In BBS4 and BBS8 depleted cells , although there was some disassembly of the BBSome , a significant proportion of the BBSome was still found to be intact . In BBS7 depleted cells , BBS4 and BBS9 were found in the same fractions but the peak was significantly shifted from that of control cells , suggesting that at least one additional subunit is missing from the BBSome in the absence of BBS7 . Of note are BBS3 and BBS5 depleted cells . In these cells , the vast majority of the BBSome remained intact , suggesting that BBS3 and BBS5 are not required for BBSome assembly . LZTFL1 depletion or over-expression did not cause any change in BBSome assembly , suggesting that LZTFL1 does not function by modulating BBSome assembly . IFT88 , a component of the IFT-B subcomplex , was found in separate fractions from the BBSome and none of the BBS or LZTFL1 perturbations caused any change in IFT88 migration . Together , these data suggest that BBS1 , BBS2 , BBS7 , and BBS9 , all of which have β-propeller domains , are required for BBSome assembly ( e . g . forming a core scaffold and required for recruitment of at least one additional BBSome subunit ) , while BBS4 , BBS5 , and BBS8 have relatively minor or no impact on BBSome assembly and are likely to be in the periphery of the BBSome . Since LZTFL1 is a negative regulator of BBSome ciliary entry , we tested whether LZTFL1 depletion can rescue the BBSome mislocalization phenotype caused by loss of BBSome subunits . We ablated LZTFL1 expression together with each of the BBSome subunits by RNAi and probed localization of BBS8 and BBS9 . Indeed , LZTFL1 knock-down significantly increased ciliary localization of BBS8 in most cells , particularly in BBS3 and BBS5 depleted cells ( Figure S7 ) . Ciliary localization of BBS9 was also rescued by LZTFL1 knock-down in all cases except for BBS1 and BBS4 depleted cells ( Figure 3C and Figure S7 ) . It is unclear whether the more efficient rescue of BBS9 ciliary localization compared to BBS8 is due to the higher sensitivity of anti-BBS9 antibody or whether it represents features of partial BBSome complexes lacking some BBSome subunits ( such as BBS8 ) . Whichever is the case , it is clear that LZTFL1 knock-down can restore ciliary localization of the BBSome at least in BBS3 and BBS5 depleted cells . It is noteworthy that BBS3 and BBS5 are the BBS proteins that have minimal impact on BBSome assembly . Therefore , as long as the BBSome forms , reducing LZTFL1 activity can restore BBSome trafficking to cilia . Since polydactyly is one of the cardinal features of BBS and a hallmark phenotype of Sonic Hedgehog ( SHH ) signaling defect , we examined roles of BBSome and LZTFL1 in the SHH pathway . We first examined the requirement of LZTFL1 for SMO ciliary localization in hTERT-RPE1 cells , which express SMO endogenously . In control siRNA transfected cells , SMO localizes to the cilia in response to SMO agonist ( SAG ) but not in SAG-untreated cells ( Figure 4 ) . However , in LZTFL1 depleted cells , ciliary localization of SMO was found in a significant number of cells even without SAG treatment and further increased by SAG treatment . For BBS genes , we tested BBS1 , BBS3 , and BBS5: two genes ( BBS3 and BSB5 ) that LZTFL1 depletion can restore ciliary trafficking of the BBSome , and one gene ( BBS1 ) that cannot be restored with LZTFL1 depletion . In contrast to LZTFL1 depleted cells , ciliary localization of SMO was significantly decreased in BBS depleted , SAG-treated cells . Consistent with the restoration of BBSome trafficking to cilia in LZTFL1 depleted cells , ablation of LZTFL1 expression in BBS3 and BBS5 , but not in BBS1 , depleted cells restored ciliary localization of SMO . These data indicate that BBSome function facilitates ciliary localization of SMO and that LZTFL1 suppresses SMO localization to cilia . Although we were not able to detect physical interactions between the endogenous BBSome and SMO in hTERT-RPE1 cells , presumably due to the transient nature of the interaction and the difficulty of extracting membrane proteins without disrupting protein-protein interactions , we found that several BBS proteins can associate with the C-terminal cytoplasmic tail domain of SMO ( aa 542–793 ) in transiently transfected cells ( Figure 4C ) . Previously , two amino acids ( WR; aa 549–550 ) immediately downstream of the 7th transmembrane domain of SMO were shown to be essential for SMO ciliary localization [15] . Deletion of this WR motif from the cytoplasmic tail of SMO abolished interaction between BBS proteins and SMO ( Figure 4D ) . These data suggest that the BBSome may directly interact with SMO and mediates SMO ciliary localization . Finally , we investigated whether downstream HH target gene expression is affected by loss of BBSome and LZTFL1 function . In hTERT-RPE1 cells , HH target gene GLI1 expression was relatively mildly induced by SAG treatment ( Figure S8A ) . Depletion of BBS1 , BBS3 , and BBS5 modestly but consistently reduced GLI1 expression . We also used mouse embryonic fibroblast ( MEF ) cells , which show robust responsiveness ( Figure S8B ) . Consistent with the results from hTERT-PRE1 cells , knock-down of BBS gene expression significantly reduced Gli1 expression in MEF cells . Although statistically not significant compared with BBS3 and BBS5 single knockdowns , reduction of Lztfl1 activity tends to restore Gli1 expression in Bbs3 and Bbs5 depleted cells . Interestingly , while LZTFL1 depletion resulted in ciliary translocation of SMO even in SAG-untreated cells , GLI1 expression did not increase in LZTFL1 depleted cells , suggesting that SMO accumulated within the cilia in LZTFL1 depleted cells is not activated . This is consistent with the idea that Smo is constantly transported in and out of the cilia even in the inactive state [22] , [23] and the recent finding of the 2-step model of SMO activation [24] . Finally , we used MEF cells derived from our Bbs2 and Bbs4 null embryos [25] , [26] and found similar reductions in Gli1 expression upon SAG treatment ( Figure S8C ) . Together , our data suggest that the BBSome and LZTFL1 are a part of the Smo ciliary trafficking mechanisms and contribute to the cellular responsiveness to the SHH signaling agonist .
BBSome functions as a coat complex to transport membrane proteins between plasma and ciliary membranes [11] . In this work , we identify LZTFL1 as a negative regulator of BBSome trafficking to the ciliary membrane . Our data indicate that LZTFL1 associates with the BBSome within the cytoplasm and inhibits ciliary entry of the BBSome . Alternatively , LZTFL1 may promote the exit of BBSomes from the cilia . Although we cannot completely rule out the second possibility , currently available data favor the entry inhibition model over the exit promotion model . First , LZTFL1 does not show any enrichment around the basal body or within the cilia either by immunological methods or by GFP-fused recombinant protein . Many proteins involved in vesicle trafficking commonly show some degree of enrichment around the donor compartment . BBSomes also show enrichment around the basal body and within the cilia with a gradual increase toward the ciliary base , which is consistent with the model that the BBSome is recruited to the plasma membrane near the basal body for ciliary entry and to the ciliary membrane at the ciliary base for exit . However , LZTFL1 does not show this localization pattern . Second , although BBSomes show enrichment around the basal body and within the cilia , LZTFL1-associated BBSomes are found scattered throughout the cytoplasm , which is consistent with the idea that LZTFL1 associates with the BBSome within the cytoplasm and limits ciliary access of the BBSome . These observations strongly favor the BBSome ciliary entry inhibition model . Localization of LZTFL1 to the cytoplasm with cilia-related function is similar to the recently characterized seahorse/Lrrc6 , which also localizes to the cytoplasm and regulates cilia-mediated processes [27] . Our findings indicate that every BBS gene tested so far is required for ciliary entry of the BBSome and loss of any single BBS protein commonly results in a failure of BBSome ciliary trafficking . This is not limited to BBSome subunits but also found with depletion of other BBS proteins that are not part of the BBSome including BBS3 , BBS6 , BBS10 , and BBS12 ( this study and S . S . and V . C . S . unpublished results ) . These findings suggest that a failure of BBSome ciliary trafficking is a common cellular feature of BBS and support the idea that BBS results from a trafficking defect to the cilia membrane . However , the precise mechanism leading to BBSome mis-localization is different depending on the missing BBS proteins . For example , BBS1 , BBS2 , BBS7 , and BBS9 , which commonly contain β-propeller domains , are likely to form the scaffold/core of the BBSome and required to recruit other BBSome subunits . BBS6 , BBS10 , and BBS12 were previously shown to be required for BBSome assembly by interacting with these β-propeller domain containing BBS proteins [8] . BBS4 , BBS5 , and BBS8 are likely to be at the periphery of the BBSome and have limited impact on BBSome assembly . However , BBS4 interacts with p150glued subunit of the cytoplasmic dynein machinery and may link the BBSome to the cytoplasmic dynein motor protein [28] . BBS5 was shown to interact with PIPs and may be involved in the association of the BBSome to the membrane [9] . The function of BBS8 is currently unknown and requires further characterization . Despite these functional differences , all BBS proteins are essential for BBSome assembly or ciliary trafficking and only the holo-BBSome enters the cilia . BBSome mis-localization may be used as a cell-based assay to evaluate BBS candidate genes . Remarkably , reducing LZTFL1 activity restores BBSome trafficking to the cilia at least in BBS3 and BBS5 depleted cells . It appears that decreased LZTFL1 activity can compensate for the loss of certain BBS proteins , which are required for BBSome ciliary entry , as long as the BBSome is formed . This implies that certain BBS subtypes may be treated by modulating LZTFL1 activities . Polydactyly is one of the cardinal features of BBS found in the vast majority of human BBS patients [3] and also a hallmark phenotype of disrupted SHH signaling or IFT function [2] , [29] , [30] . Several additional features including mid-facial defects and neural crest cell migration defects found in BBS mutant animals are also linked to SHH signaling defects [31] . Therefore , it is speculated that BBS proteins may be involved in SHH signaling . In this work , we show that BBS proteins are involved in ciliary trafficking of SMO . We found that the SMO cytoplasmic tail domain physical interacts with the BBSome at least in overexpressed conditions , while ciliary localization defective mutant SMO does not . Furthermore , ablation of LZTFL1 increases SMO ciliary localization . These data indicate that BBS proteins and LZTFL1 are at least part of the SMO ciliary trafficking mechanism . It remains to be shown that ciliary transport of Smo occurs together with the BBSome ( e . g . by live cell imaging ) and in a directly BBSome-dependent manner . Our data also suggest the presence of alternative ciliary trafficking mechanisms for SMO . For example , although ciliary localization of SMO is significantly reduced by BBSome depletion , it is not completely abolished , while ciliary localization of BBS8 and BBS9 is severely reduced . HH target gene GLI1 expression is also relatively mildly affected by BBS protein depletion . This suggests that the BBSome is only part of the transport mechanism and that there is likely to be another mechanism by which SMO is transported to the cilia . Currently , it is unknown whether IFT proteins can directly mediate SMO ciliary trafficking or are involved indirectly . These findings suggest that although BBS proteins are involved in Smo ciliary trafficking , the presence of alternative mechanisms is sufficient to support normal development of neural tubes and survival in BBS mutants . In addition , while polydactyly is very common in human BBS patients , none of the BBS mouse models generated so far display polydactyly as is seen in Smo or Shh mutants [2] , implying a potential species-specific requirement of BBS proteins or differential threshold in SHH signaling in the limb bud . While BBS and IFT proteins are part of the transport machinery ( like a train ) to deliver cargos to and from the cilia , NPHP , MKS , and Tectonic proteins localize to the transition zone and function like a station ( or immigration official ) to control the entry and exit of the ciliary proteins . For example , Garcia-Gonzalo , et al . recently showed that the Tectonic complex , which consists of Tctn1 , Tctn2 , Tctn3 , Mks1 , Mks2 , Mks3 , Cc2d2a , B9d1 , and Cep290 , localizes to the transition zone of cilia and controls ciliary membrane composition [32] . NPHP1 , NPHP4 , and NPHP8 form another complex at the transition zone and functions as a ‘gate keeper’ together with other NPHP and MKS proteins [33] , [34] , [35] . These NPHP , MKS , and Tctn protein functions also appear to be partly redundant . In this model , one can envisage certain ciliary proteins transported by the BBSome and allowed to enter the cilia by the Tectonic complex . Some other proteins may be transported by a non-BBSomal mechanism and granted access to cilia by the Tectonic complex or by NPHP1–4–8 complex . LZTFL1 is a specific regulator of BBSome ciliary trafficking activity . Some other proteins may regulate activities of IFT complex or specific transition zone complexes . Obtaining a complete picture of ciliary protein transport will be the focus of future studies to understand the precise mechanisms of cilia-related disorders .
All animal work in this study was approved by the University Animal Care and Use Committee at the University of Iowa . Expression vectors for BBS genes were published [8] . Human and mouse LZTFL1 cDNA clones ( NM_020347 and NM_033322 ) were purchased from OpenBiosystems and subcloned into CS2 plasmids with Myc , FLAG , HA , or FS ( FLAG and S ) tag after PCR amplification . Site-directed mutagenesis was performed by using QuikChange protocol ( Agilent ) and PfuUltra II Fusion HS DNA polymerase ( Agilent ) . Small interfering RNAs ( siRNAs ) were purchased from Dharmacon ( ON-TARGETplus SMARTpool ) and transfected at 100 nM concentration for Gli1 gene expression analysis and at 50 nM concentration for all other experiments with RNAiMAX ( Invitrogen ) following manufacturer's protocol . Antibodies against BBS1 , BBS2 , BBS4 and BBS7 were described previously [9] . To produce rabbit polyclonal antibody for mouse Lztfl1 , recombinant NusA-Lztfl1 protein ( full-length ) was purified using HIS-SELECT Nickel Affinity Gel ( Sigma ) and used as antigen to immunize rabbits ( Proteintech Group ) . Smo-N antibody was a generous gift from Dr . R . Rohatgi ( Stanford University ) . Other antibodies used were purchased from the following sources: mouse monoclonal antibodies against acetylated tubulin ( 6–11B-1; Sigma ) , γ-tubulin ( GTU-88; Sigma ) , LZTFL1 ( 7F6; Abnova ) , Myc ( 9E10; SantaCruz ) , FLAG ( M2; Sigma ) , HA ( F-7; SantaCruz ) , β-actin ( AC-15; Sigma ) , rabbit polyclonal antibodies against ARL13B ( Proteintech Group ) , BBS7 ( Proteintech Group ) , BBS8 ( Sigma ) , BBS9 ( Sigma ) , γ-tubulin ( Sigma ) , IFT57 ( Proteintech Group ) , IFT88 ( Proteintech Group ) , Smo ( Abcam ) , and rabbit monoclonal antibody against GFP ( Invitrogen ) . The LAP-BBS4 transgenic mouse line was generated by injecting the LAP-BBS4 expression cassette of the pLAP-BBS4 construct [9] into 1-cell pronuclear stage mouse embryo from B6SJL ( C57BL/6J X SJL/J; Jackson Laboratory ) strain in the University of Iowa Transgenic Animal Facility . Transgenic animals were maintained in the mixed background of C57BL/6J and 129/SvJ . Genotype was determined by PCR using the following primers ( 5′-GTCCTGCTGGAGTTCGTGAC-3′ and 5′-GGCGAAATATCAATGCTTGG-3′ ) . Progenies from three founders expressed the LAP-BBS4 transgene . In two lines , LAP-BBS4 levels were less than 50% of endogenous Bbs4 in the testis and only one line expressed LAP-BBS4 higher than endogenous Bbs4 ( Figure S1 ) . This line was used for the entire study . Bbs4 knock-out mouse model and hematoxylin/eosin staining was previously described [26] . Testes from six wild-type and LAP-BBS4 transgenic animals were used for TAP . Proteins were extracted with lysis buffer ( 50 mM HEPES pH 7 . 0 , 200 mM KCl , 1% Triton X-100 , 1 mM EGTA , 1 mM MgCl2 , 0 . 5 mM DTT , 10% glycerol ) supplemented with Complete Protease Inhibitor cocktail ( Roche Applied Science ) . The remaining TAP procedure was described previously [9] . TAP of FS-LZTFL1 was conducted with HEK293T cells stably expressing FS-LZTFL1 or parental cells . Cell lysates from twenty 15-cm dishes were loaded onto anti-FLAG affinity gel ( M2; Sigma ) , and bound proteins were eluted with 3xFLAG peptide ( 100 µg/mL; Sigma ) . Eluate was loaded onto S-protein affinity gel ( Novagen ) , and bound proteins were eluted in 2x SDS-PAGE sample loading buffer . Purified proteins were separated in 4–12% NuPAGE gels ( Invitrogen ) and visualized with SilverQuest Silver Staining Kit ( Invitrogen ) . Excised gel slices were submitted to the University of Iowa Proteomics Facility and protein identities were determined by mass spectrometry using LTQ XL linear ion trap mass spectrometer ( Thermo Scientific ) . Co-IP was performed as previously described [8] . Protein extract from one 10-cm dish of hTERT-RPE1 cell was concentrated with Microcon Centrifugal Filter Devices ( 50 , 000 MWCO; Millipore ) , loaded on a 4 ml 10–40% sucrose gradient in PBST ( 138 mM NaCl , 2 . 7 mM KCl , 8 mM Na2HPO4 , 1 . 5 mM KH2PO4 , 0 . 04% Triton X-100 ) , and spun at 166 , 400 x Gavg for 13 hrs . Fractions ( ∼210 µl ) were collected from the bottom using a 26 G needle and concentrated by TCA/acetone precipitation . Proteins were re-suspended in equal volume of 2x SDS-PAGE sample loading buffer and analyzed by SDS-PAGE and immunoblotting . Size exclusion chromatography was previously described [8] . Briefly , protein extracts from testis and eye were concentrated by Amicon Ultra-15 ( 30 kDa; Millipore ) and loaded on a Superose-6 10/300 GL column ( GE Healthcare ) . Eluted fractions were TCA/acetone precipitated and re-suspended in 2x SDS loading buffer . The column was calibrated with Gel Filtration Standard ( Bio-Rad ) . hTERT-RPE1 cells were maintained in DMEM/F12 media ( Invitrogen ) supplemented with 10% FBS . Immortalized MEF cells expressing Smo-YFP was kindly provided by Dr . M . Scott ( Stanford University ) and cultured in DMEM with 10% FBS . Primary MEF cells from wild-type and Bbs2 and Bbs4 null embryos [25] , [26] were prepared following a standard protocol at embryonic day 13 . 5 . Cells were transfected with siRNAs using RNAiMAX for 48 hrs and further incubated in serum-free medium for 24 hrs for ciliation . For RNA extraction , cells were treated with 100 nM SAG ( EMD Chemicals ) for additional 18 hrs in a fresh serum-free medium . Total RNA was extracted using TRIzol Reagent ( Invitrogen ) following manufacturer's instruction . Quantitative PCR was performed as previously described [8] . RPL19 mRNA levels were used for normalization and ΔΔCt method [36] was used to calculate fold inductions . The PCR products were confirmed by melt-curve analysis and sequencing . Knock-down efficiencies of BBS genes were measured by qPCR and samples with more than 90% reduction in BBS gene expression levels were used for GLI1 gene expression analysis . PCR primer sequences are in Table S3 . For immunofluorescence microscopy , cells were seeded on glass coverslips in 24-well plates and transfected with siRNAs using RNAiMAX or with plasmid DNAs using FuGENE HD ( Roche Applied Science ) . Cells were cultured for 72 hrs before fixation with the last 30 hrs in serum-free medium to induce ciliogenesis . For SAG treatment , 100 nM SAG was added to fresh serum-free medium and incubated for 4 hrs at 37°C . Cells were fixed with cold methanol , blocked with 5% BSA and 2% normal goat serum in PBST , and incubated with primary antibodies in the blocking buffer . Primary antibodies were visualized by Alexa Fluor 488 goat anti-mouse IgG ( Invitrogen ) and Alexa Fluor 568 goat anti-rabbit IgG ( Invitrogen ) . Coverslips were mounted on VectaShield mounting medium with DAPI ( Vector Lab ) , and images were taken with Olympus IX71 microscope . For in situ PLA , Duolink in situ PLA kit with anti-mouse PLUS probe and anti-rabbit MINUS probe ( OLINK Bioscience ) was used with mouse monoclonal antibody for LZTFL1 ( Abnova ) and rabbit polyclonal antibody for BBS9 ( Sigma ) following manufacturer's instruction . Briefly , RPE1 cells were seeded onto an 8-well Lab-Tek II chamber slide ( Nunc ) and treated as for immunofluorescence microscopy until the primary antibody binding step . After washing , cells were decorated with PLA PLUS and MINUS probes ( 1∶20 dilution ) for 2 hrs in a 37°C humidified chamber . Hybridization and ligation of probes , amplification , and final SSC washing were performed per manufacturer's instruction in a humidified chamber . Complex formation was detected by Duolink Detection kit 563 ( OLINK Bioscience ) and Olympus IX71 microscope . Homology search , secondary structure prediction , and modeling were performed by using human LZTFL1 protein sequence ( NP_065080 ) , SWISS-MODEL Workspace , and InterProScan [37] , [38] . Rat Stx1A H3 domain ( PDB: 1hvv ) was used as a template and aligned with human LZTFL1 aa 212–284 . For paraformaldehyde ( PFA ) cross-linking , HEK293T cells were incubated with 1% PFA in DMEM at 37°C for 10 min . For cross-linking with photoreactive amino acids , cells were cultured in Dulbecco's Modified Eagle's Limiting Medium ( DMEM-LM; Pierce ) with L-Photo-Leucine and L-Photo-Methionine ( Pierce ) supplemented with 10% dialyzed FBS ( Pierce ) . Cross-linking was performed following manufacturer's instruction . Cells were irradiated in the UV Stratalinker 2400 ( Stratagene ) 5-cm below the UV lamp for 6 minutes . After cross-linking , cells were washed with PBS three times and lysed in the lysis buffer ( 50 mM Tris pH 7 . 0 , 150 mM NaCl , 0 . 5% Triton X-100 , 0 . 5% CHAPS , 2 mM EDTA , 2 mM NaF , 2 mM NaVO4 ) supplemented with Complete Protease Inhibitor cocktail ( Roche Applied Science ) . Protein extracts were loaded onto a 4–12% SDS-PAGE gel and subjected to immunoblotting . Wild-type mice at 2–3 months of age were used for retinal sections . Animals were perfused with 4% PFA in PBS ( 2 . 5 ml/min , 50 ml ) , and excised eyes were post-fixed for 30 min in the same solution . After washing with PBS , eyes were frozen in OCT and sectioned using cryostat with CryoJane system ( myNeuroLab ) with 7 µm thickness . For mouse spermatozoa , testis and epididymis from wild-type animals were minced by forceps in Ham's F10 solution ( Invitrogen ) and incubated for 20 minutes at 37°C in 5% CO2 incubator . The turbid upper fraction ( “swim-up” fraction ) was collected using wide-opening tips and spread onto positively charged slide glasses . Excess liquid was slowly aspirated and samples were air-dried . After fixation with 4% PFA in PBS for 5 min , samples were processed for immunofluorescence following standard protocol .
|
Primary cilia are considered to be a signaling hub coordinating multiple signaling pathways . Impairment of ciliary function results in developmental defects in vertebrates and also underlies many human disorders including obesity , polycystic kidney disease , and retinopathy . BBS is a prototypical human genetic disorder associated with ciliary dysfunction . Among the known BBS proteins , seven form a complex , the BBSome , which was recently defined as a coat complex transporting membrane proteins between plasma and ciliary membranes . However , the molecular mechanisms controlling BBSome trafficking and the cargos transported by the BBSome are not well understood . In this work , we performed tandem affinity purification using transgenic mice expressing one of the BBSome subunits and identified a novel protein , LZTFL1 , as a BBSome interacting protein . We determined that LZTLF1 negatively regulates BBSome ciliary trafficking and that reduction of LZTFL1 activity can compensate for loss of certain BBS proteins and restores BBSome ciliary trafficking . Furthermore , we discovered that BBSome and LZTFL1 regulate ciliary trafficking of Smoothened , a 7-transmembrane Hedgehog signal transducer . Our findings identify an important player in cilia biology and provide novel insights into the regulation of Hedgehog signaling , a crucial signaling pathway for organizing the body plan , organogenesis , and tumorigenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"spectrometric",
"identification",
"of",
"proteins",
"protein",
"interactions",
"gene",
"function",
"proteins",
"membranes",
"and",
"sorting",
"biology",
"proteomics",
"molecular",
"biology",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"genetics",
"human",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] |
2011
|
A Novel Protein LZTFL1 Regulates Ciliary Trafficking of the BBSome and Smoothened
|
In this work , a stochastic computational model of microscopic energy deposition events is used to study for the first time damage to irradiated neuronal cells of the mouse hippocampus . An extensive library of radiation tracks for different particle types is created to score energy deposition in small voxels and volume segments describing a neuron’s morphology that later are sampled for given particle fluence or dose . Methods included the construction of in silico mouse hippocampal granule cells from neuromorpho . org with spine and filopodia segments stochastically distributed along the dendritic branches . The model is tested with high-energy 56Fe , 12C , and 1H particles and electrons . Results indicate that the tree-like structure of the neuronal morphology and the microscopic dose deposition of distinct particles may lead to different outcomes when cellular injury is assessed , leading to differences in structural damage for the same absorbed dose . The significance of the microscopic dose in neuron components is to introduce specific local and global modes of cellular injury that likely contribute to spine , filopodia , and dendrite pruning , impacting cognition and possibly the collapse of the neuron . Results show that the heterogeneity of heavy particle tracks at low doses , compared to the more uniform dose distribution of electrons , juxtaposed with neuron morphology make it necessary to model the spatial dose painting for specific neuronal components . Going forward , this work can directly support the development of biophysical models of the modifications of spine and dendritic morphology observed after low dose charged particle irradiation by providing accurate descriptions of the underlying physical insults to complex neuron structures at the nano-meter scale .
Neuronal cells show great diversity in morphology and are highly organized within the animal brain [1] . In general , neurons are differentiated cells with a tree-like structure including the soma which contains the cell nucleus , numerous dendritic branches emanating from the soma unidirectionally or from multiple poles , and a single axon . Numerous spines protruding from dendrites make synaptic connections with other spines and dendrites located on other neurons [2] . Neurons are excitable cells with a plethora of specialized ionic membrane channels that participate actively and passively in changes of membrane potential to transduce , relay and integrate information within the neurocircuitry of the nervous system [3] . Neuronal cells are also dynamic , as branches may extend and immature filopodia seek new connections to make synapses with cellular cues [4 , 5] . The hippocampus plays an important role in learning , consolidation of memory and retrieval of information [6 , 7] . The dentate gyrus ( DG ) in the hippocampus shows great similarity in cellular morphology and neurocircuitry across mammalian species [8] . The principal neurons in the dentate gyrus are dentate granule cells ( DGCs ) [9 , 10] . The DGC dendrites extend perpendicularly to the granule cell layer , into the overlying molecular layer where they receive synaptic connections mainly from the entorhinal cortex via the perforant pathway . DGCs are mostly monopolar neurons as dendrites emerge from apical portion of the cell body . Mossy fibers are the axons of the granule cells extending from the basal level and making synapses with the pyramidal cells of CA3 region of the hippocampus [11 , 12] . Investigations into ionizing radiation damage to cognition over a large range of absorbed doses ( <0 . 1 Gy to 10’s of Gy where the Gray ( Gy ) is the unit of absorbed dose defined as 1 Joule/kg ) ) and for distinct radiation qualities , including for high linear energy transfer ( LET ) radiation , is of growing interest [13–15] . The use of protons and carbon beams in so-called Hadron therapy for cancer treatment has increased by more than an order of magnitude in this century with a growing number of facilities in the U . S . , Japan and Europe [16 , 17] . Continued interest in space exploration where astronauts are exposed to cosmic rays including protons , iron and other heavy ions has led to research studies in mice and rats demonstrating cognitive detriments at doses below 0 . 5 Gy [18–20] . In addition , as treatment of brain cancers with conventional radiation ( electrons and photons ) has improved , longer patient survival and efforts to understand and mitigate cognitive changes is currently an important focus [21 , 22] . Neuronal injury leading to cellular death is likely initiated by structural damage to the tree-like dendritic arborization of neurons , which generally extend hundreds of microns from the soma [23–27] . In addition , cell signaling and the induction of neuro-inflammation or other detrimental changes will follow from the initial energy deposition events , which vary with the type of radiation as well as dose or particle fluence . In the past , energy deposition within neuronal cells and its influence on these processes has not been studied . The primary goal of the present work is to develop a model of microscopic energy deposition ( ED ) events in neurons from different radiation qualities and doses . Neuronal morphological data of a DGC was chosen as an example to explore the effects of ionizing radiation [28] , both to validate the model with accumulating data on DGCs [13 , 19 , 23] and make testable predictions for future studies . The simulations described are for particles in the so-called track segment mode where a particle undergoes a negligible decrease in velocity as it traverses a path-length through a volume , which are representative of space radiation exposures and normal tissue damage in therapy . Calculations for particles near the Bragg peak where LET reaches a maximum and particles are close to stopping important within the tumor volume in therapy are not discussed . Because high charge ( Z ) and energy ( E ) ( HZE ) particles create numerous secondary electrons denoted as δ-rays , calculations for a typical electron energy of 0 . 5 MeV are also described . This energy is also representative of electrons produced by a gamma-ray or X-ray sources . The in silico approach developed can be expanded to other types of neurons and regions in the central nervous system ( CNS ) to quantify energy deposition at microscopic scale for a given charged particle type and dose .
Energy deposition ( ED ) events from particle tracks are simulated with the RITRACKS software [29 , 30] ( available from http://spaceradiation . usra . edu/irModels ) . RITRACKS is a Monte-Carlo based computer model that calculates the positions of the ED events due to ionization and excitation as a particle traverses a media assumed to be water . The stochastic simulation environment for given charged particle with an initial energy , propagation direction ( e . g . along the z-axis ) and entrance point , e . g . ( 0 , 0 , 0 ) creates ED events at varying positions for the initial particle and the created secondary electrons denoted as δ-rays , which are correlated with the particles path . Energy deposition events are lumped within a predetermined voxel size [30 , 31] . The total ED in a voxel and its coordinates are an output value from the simulation . For a predetermined track length ( LTrack ) , voxel ED values and their coordinates are calculated , which includes contributions from the primary particle track and δ-rays . This stochastic process is repeated a large number of times using Monte-Carlo techniques with the same initial conditions to create a library of histories for a particles ED events . In this study , all the voxel sizes and track lengths are taken as 20 nm3 and 20 μm , respectively . Particle kinetic energies are expressed in units of MeV for protons and electrons , and MeV per atomic mass unit ( u ) for HZE particles . We considered simulations of 56Fe at 600 MeV/u , 12C at 300 MeV/u and 1H at 250 MeV particles to create a library of 5 , 000 , 10 , 000 and 20 , 000 histories , respectively that are used for calculations of energy deposition in the neuron structures considered . For the 20 μm track length , the initial energies are reduced by ( 0 . 01 , 0 . 007 , 0 . 003 ) % respectively at the end of the track . The average LET values calculated over all the histories are ( 172 . 4 , 12 . 9 , 0 . 4 ) keV/μm . HZE particles liberate numerous electrons through ionization in passage through a media such as water , and some of these electrons denoted as δ-rays deposit their energy outside a defined test volume where neurons are bounded . Because the range of δ-rays exceeds 0 . 1 cm for the energies considered , the appropriate volume to use to sample the particle track was a focus of numerical simulations as described below and in the S1 Text . We also considered 500 keV electrons , which is a typical energy of the δ-rays produced by HZE particles . Results do not depend critically on the starting electron energy for energies above about 50 keV . To obtain ‘nearly uniform energy’ electrons , a library of 20 , 000 histories was created starting from the same initial energy and propagation direction by following the dose deposition events from 500 keV to 490 keV . The neuron morphology of a hippocampal granule cell of C57BL6/Trk . T1 deficient mouse was acquired from publicly accessible repository neuromorpho . org ( ID: NMO_07656 ) [32 , 33] . The soma and dendrites are represented as cylindrical segments with axes points and radii . The NMO_07656 has 16 branches with total branch length ( LDN ) of 1392 μm , total soma and dendrite volume of 1129 μm3 and 1559 μm3 , respectively ( adds to total volume of 2688 μm3 ) . The overall measure of ( width , height , depth ) is ( 169 , 180 , 69 ) μm that is given a diagonal extension to 256 μm . These geometric measures are in agreement with the compiled 70 mouse hippocampal granule cells authored by Vuksic and Lee at neuromorpho . org website resulting in ( mean±SD ) for total volume ( 5631±1720 μm3 ) , branch number ( 25±5 ) , total branch length ( 2161±384 μm ) and diagonal extension ( 279±33 μm ) [32 , 33] . The reconstructed anatomical data do not include the spine and filopodia information of the mouse DGCs at the neuromorpho . org repository . Different tissue staining techniques ( Golgi-Cox , horseradish peroxidase ) and green/yellow fluorescent expressing transgenic mouse ( e . g . , Thy1-GFP ) with high magnification confocal images provide the statistics of total number of spines , filopodia per unit length ( 10 μm ) . We utilized the published data for Thy1 GFP mouse hippocampal granule cell linear spine density statistics ( DSN ) by the publications Parihar and Limoli [19] and Parihar et al . [13] , and value of DSN = 4 . 3±0 . 4 per 10 μm . Spines are also categorized depending on their morphology as tall , mushroom and stubby spines , Fig 1 . Their ratio is adapted from the same publications . In addition , not matured invaginations; filopodia linear density , DFN , of 4 . 3±0 . 4 per 10 μm was adapted from the same publications . The total dendritic length ( LDN μm ) was found by adding the height of dendritic cylindrical segment lengths by keeping track of segment number and base to ceiling directional information . A random number for total number of spines ( NST ) was found from a normal distribution with given mean and standard deviation ( NST = ( LND/10 ) × NormalDist ( DSN ) . The total number of NST uniform random points was located on the corresponding segments and distance from the base of the segment . The same procedure was applied to locate total number of filopodia ( NFT ) on the dendritic segments . Then , the distance between all spine and filopodium points are calculated and if there was any pair distance less than 0 . 5 μm , a new random location on the dendrite was found for one of the pairs . This procedure was repeated until all the spines and filopodia are separated with given distance criteria . Random axial directions of all spines and filopodia pointing perpendicular to the segment surface were found by drawing uniform random points between 0 to 2π . Finally , spines are registered as tall , mushroom and stubby spines by choosing random numbers from a normal distribution with given mean and standard deviation for two types and assigning the remaining registration for the third kind by keeping the NST value constant . Spines and filopodia were represented as two cylinder segments with overlapping axes directions and different diameter and axes heights ( Fig 1 ) . These cylinders are named spine/filopodia base segment protruding from surface of the dendrite and top segment on the base cylinder . Spine morphology is categorized by long , mushroom and stubby with different heights and diameter . The ratio of these three types of spines is adapted from [13 , 19] . Segment heights and diameter are similar for the filopodia and long spines . These geometric factors including different spine ratios are presented in Table 1 . The filopodia is a functionally separate category . A stochastic realization of the spine/filopodia distribution on the dendrites was run once and kept constant thorough this paper and these numbers are also presented in Table 1 . A sample particle beam of length LSample at ‘nearly uniform energy’ can be constructed by randomly selecting nsample ( ceiling ( LSample/LTrack ) ) histories of pre-simulated tracks from a corresponding library . First , sample histories can be translated along the propagation direction and data can be stitched together to create a beam that traverses the complete volume . Then , further translations and rotations operations can be applied on the beam for any beam , target coincidence event detection . In addition , multiple processors in a parallel computation can also take advantage of operating on individual Ltrack histories of a reconstructed beam instead of creating a new beam with many voxel values . The scoring process determines the coincidence of ED in voxels from a particle beam within the neuronal volume . A trial of the scoring library for any given charged particle utilizes the histories of the irradiation library to construct a beam that targets the sample neuron . The randomness between each trial includes the random construction of ED events of a beam and relative random orientation of the beam and the neuron . The geometry of a beam and a neuron configuration in Fig 2 is organized as follows: The scoring geometry contains two cylinders with overlapping cylinder axes along the z-axes with different axes length and radii . The larger scoring cylinder ( CylScore ) has the base at ( x , y , 0 ) and axis length of LScore that is equal to LScore , i = LForward + LNeuron , i + LBackward . The radius , RScore of the CylScore is given as RScore = RGap + RNeuron . The lengths , LForward , LBackward , RGap are constants for any beam type versus neuron simulations and discussed in detail in the S1 Text . RNeuron is the test neuron constant and LNeuron , i varies between each trial i as discussed below . Scoring is done for each beam by testing the coordinates of each voxel and segment boundaries of the neuronal cylinders if the voxel is inside of any segments of the neuron . If there are one or more coincidence events , then the trial is recorded as a ‘hit’ . Fig 2 illustrates that a large number of ED events are concentrated close to the particles path main , while the number of scattered electrons per unit distance increases with LET of the particles and can extend far from the particles location . The neuron is bounded by the smaller cylinder ( CylNeuron ) that has the maximum diameter ( radius of RNeuron ) and axis length ( LMax , Neuron ) of 256 μm that is the diagonal extension of the neuron . The base of the CylNeuron is located at ( x , y , LForward ) but has variable axis length LNeuron , i ( ≤LMax , Neuron ) at each trial i for computational purposes as discussed below . A unique geometric center is found for the neuronal tree and this center is situated along the longitudinal axis of the CylNeuron , as well as the CylScore . The neuron makes random rotations around its geometric center at each trial i and the coordinates of any dendritic cylindrical segments that have the farthest and the closest distances to the z = 0 plane are found . This range is specified as LNeuron , i and the cylindrical coordinates of rotated neuron is translated along the axis such that the geometric center coincides with ( 0 , 0 , LForward + ( LNeuron , i/2 ) ) point . A relative random orientation of a particle beam and the neuron is set by propagating the beam along the +z-axis , parallel to the neuronal cylinder axis at each trial and randomly rotating the neuron as described above . These translation and rotation information are recorded during the trial as the coordinates and energies of coincidence events are replaced back to the initial neuron configuration by back rotation and translation . The entrance point of the particle beam is bounded by the base surface of CylScore . A random entrance point ( xRand , i , yRand , i , 0 ) is chosen at each trial i . The construction of an ionizing beam depends on the z-axis path length , LScore , i , as total number of ( NHistory , i ) different histories with length , Ltrack , are read randomly from the particle track library created by the RITRACKS software . The total number of histories for each trial is found by NHistory , i = ceiling ( LScore , i/Ltrack ) . The random histories are translated to ( xRand , i , yRand , i , k×Ltrack ) point for histories spanning k = 0 , 1 , … , NHistory , i − 1 to create a beam of length NHistory , i × Ltrack . A finite length simulated beam is composed of linearly added track beams with Ltrack that goes through the cylinder volume perpendicular to the surface . A discrete number of beams , NHistory , to cover the total axis length LScore and corresponding distances , LForward , LBackward and LNeuron were chosen with values for LForward and LBackward given different values for each particle and are given in Table A of S1 Text . Fig 3 illustrates the methods for constructing each trial from the library generated using RITRACK . As discussed above the longitudinal range , LNeuron , i was variable at each trial and LNeuron = 140 μm is taken as the range to ensure a constant value in this calculation between the minimum thickness of the neuron 35 μm and maximum diagonal distance 256 μm . The start point of a track is plotted as the z = 0 point on the LCDF plot in Fig 3 . Contribution of each track in a beam to the gray shaded longitudinal volume is calculated by how much of LCDF ( Lk ) contributes to the volume of interest where Li are the distances of zero point of the track to the entrance ( L1 ) and exit point ( L2 ) distance of the volume of interest ( dashed lines in Fig 3 ) . In a different manner than that of HZE particle beam where the tracks are added subsequently , electrons are described by keeping the same random entry point i ( xRand , i , yRand , i , z ) , propagating along the +z-axis , while the z-coordinate of the next random track starts the z-coordinate of the last voxel of the previous track . In this way a continuation of the electron along the +z-axis is provided , as the first track is always entering LForward upstream and the last track leaving the CylScore at LBackward from the neuron at each trial . The mean last voxel point on z-axis is 46 . 4 μm at the library . The total energy lost along the initial propagation direction ( z-axis ) is 10 keV that gives and average LET value of 0 . 215 keV/μm for electrons . A coincidence algorithm between the voxels of each beam and the neuronal segments is made by testing if the voxel coordinates are inside any of the cylindrical segments including soma , dendrites , spines and filopodia . If there is any coincidence events these coordinates are back rotated and translated to initial and fixed neuronal coordinate configuration and recorded for that trial . A library is created over many trials and results are summarized in Table 2 . The “fluence dose” is calculated over all the trials as Fluence Dose = Number of trials × Dose per beam . The coincidence results library is further sampled to quantify the dose to dendrites , spines and filopodia . The probability of coincidence per beam over a trial between any voxel of a beam and the test neuron is low; ( 14 . 92 , 1 . 937 , 1 . 66 , 0 . 4871 ) % for ( 56Fe , 12C , 1H , e- ) beams because the neuron fills only a small fraction of the fluence volume defined by CylScore and the area of the fluence cylinder ( AScore=πRScore2 ) is kept large enough to have electronic equilibrium for the embedded neuron bounded by CylNeuron . The computational load of the coincidence of events per beam and neuronal segments at each trial can be estimated by giving the average number of voxels ( 36137 , 4517 , 234 , 292 ) per 20 μm ( Ltrack ) for ( 56Fe , 12C , 1H , e- ) particles and the number of histories , NHistory , i per beam for an average LScore of 300 μm which is found as 15 ( 7 for electrons; mean ( LTrack ) ~46 . 4 μm ) . The number of neuronal segments in this example is 2610 . The percentage of success defined as if one or more voxels of a beam is bounded by neuronal segment boundaries in a trial is given above . The particle fluence to CylScore is calculated as the ratio of the number of trials to the cylinder base area AScore the beam impinges , and the neuron dose is calculated by summing all the voxel ED events where voxels coordinates coincide with neuronal segments in the trials . The dose in Gray of a single beam is determined by 0 . 16 × LET ( keV/μm ) /AScore ( μm2 ) where LET is calculated over the average value of all the ED events of histories generated by RITRACKS and used in the simulations in Table 2 . Statistical realizations of any dose can be sampled from the trial library; first by finding the fluence , hence the mean number of beams ( < NDose > ) , then drawing a random number for that beam ( NDose , P ) from a Poisson distribution to reflect variability at low fluence . Another set of distinct NDose , P number of random integers can be drawn from the number of trials library for that beam and if there are any registered coincidence files; hits , the coordinates and energy values of the voxels can be placed in neuronal segments . These results can be displayed or further evaluated , such as finding the dose in the neuronal segments or counting how many voxels including coordinates and energies are located in dendrites and spines or filopodia . The δ-rays from HZE particles can extend for many millimeters from the particles track ( Fig 2 ) , both radially from the surface of entrance and longitudinally in backward and forward directions . The larger volume CylScore , containing the volume of interest at which test neuron and ED events are recorded , is utilized to reach electronic equilibrium in CylNeuron . Extending radial and longitudinal size of the CylScore volume lengthens the time of simulations , and the size of electronic equilibrium . The test neuron dose calculated from the fluence to the surface of CylScore and measured by adding all the ED events bounded by the neuron over all the trials are quantified by a predicted value of the CylNeuron volume by electronic equilibrium approach . The details of the numerical study are given in the S1 Text . Briefly , orthogonal radial and longitudinal normalized deposited energy functions in Fig 2 are numerically calculated for each HZE particle and electrons considered in this study . The spatial extent of each particle beam entering the CylScore cylinder surface and propagating along its z-axis is treated as unit sources and their radial and longitudinal components bounded by the CylNeuron numerically calculated to predict the measured dose .
The ED distribution within 20 nm3 voxel volumes generated by the RITRACKS code [29 , 30] for the 56Fe , 12C and 1H particle at ( 600 MeV/u , 300 MeV/u , 250 MeV ) are plotted in Fig 4 , with the mean energy deposited of the particles found as ( 95 . 5 , 57 . 3 , 34 . 0 ) eV , respectively . The peak positions of the histograms and amplitudes occur for ED lower than , e . g . 60 eV/voxel in Fig 4A–4D . This corresponds to the discreteness of the number of ED events occurring in the 20 nm3 voxels [31] . 56Fe particles were found to have a very high voxel ED tail ( >1000 eV/voxel ) ( Fig 4A ) . The number of voxels with over 1000 eV/voxel accounts for only 2 . 6% of the total voxels , but contains 44% of the total ED . These large ED events are located within 100 nm lateral to the 56Fe particle track . 12C and 1H particles were not found to have similar high ED tails , but instead a monotonic decrease with increasing ED occurs . For 12C and 1H particles , the highest 2% of voxel ED are larger than ( 470 , 229 ) eV/voxel and account for the ( 17 . 3 , 22 . 8 ) % of the total deposited energies , respectively . Predictions of ED distributions for 0 . 5 MeV electrons ( result not shown ) where found to be very similar to the 250 MeV protons . Unlike 56Fe particles , which have the mean voxel ED 100 nm lateral to particle track of 190 . 7 eV/voxel , 12C particles show a moderate increase ( 62 . 7 eV/voxel ) while a slight decrease was found for 1H ( 30 . 2 eV/voxel ) . However , the number of voxels within 100 nm accounts for ( 34 . 1 , 63 . 6 , 79 . 6 ) % of total voxels for ( 56Fe , 12C , 1H ) particles , respectively . These overall voxel distributions give rise to large ED distribution within 100 nm vicinity of the particle tracks as presented in radial distribution of spatial ED profiles in Fig 5 . In considering electrons the simulation initially starts at the ( 0 , 0 , 0 ) coordinate , and propagates along the +z-direction with the initial energy of 500 keV as detailed in Materials and Methods section . The mean ED per 20 nm voxels between 500 to 490 keV is found as 34 . 09 eV/voxel . The high energy tail of the voxel ED larger than 222 eV/voxel accounts for 2% of the voxels and 22 . 6% of the total deposited energy in Fig 5D . In comparison , the 20 nm3 voxel ED distribution for the 1H particles and electrons at these energies are very similar . Results show that modifying the voxel size changes the ED distribution as differences in stochastic coulomb interactions occur in the voxel as the volume is changed . However , normalized radial and longitudinal energy deposition distributions in Fig 5 were not found to change with voxel size . The neuronal segment and compartment volumes , the smallest being a spine segment of 125 nm3 and spine volume of 355 nm3 in this study , are much larger than the 20 nm3 voxels . Absorbed dose to the test neuron or any compartments including soma , dendrites , spines or filopodia can therefore be found by normalizing the total energy of voxel EDs located within the compartment used in the numerical simulation , assuming the cellular density is the same as water . The probability of success defined as the occurrence of an ED event in the test neuron that is hit by a beam is ( 14 . 92 , 1 . 94 , 1 . 66 , 0 . 49 ) % for ( 56Fe , 12C , 1H , e- ) , and this ratio depends on the number of voxels per track , hence LET and the extent of the geometrical distances between CylNeuron and CylScore to reach electronic equilibrium . A better electronic equilibrium would be reached by allowing for larger dimensions of build-up distances , however would result in a significant increase in simulation time and a decrease in the probability of hit per beam . The average number of voxels that coincide with the test neuron , if there is any hit , is ( 101 . 4 , 89 . 2 , 17 . 2 , 8 . 2 ) for ( 56Fe , 12C , 1H , e- ) particles , respectively . The mean ED to the voxels that are in coincidence with the test neuron are ( 104 . 57 , 58 . 11 , 34 . 19 , 34 . 01 ) eV/voxel for ( 56Fe , 12C , 1H , e- ) particles . The mean ED of voxels is larger lateral to the 56Fe and 12C particle tracks and main particle tracks are well confined by the simulation geometry but δ-ray events that extend over hundreds of microns in Fig 5 may not be bound by the volumes taken in this study . This also shows the difficulty of achieving electronic equilibrium for high LET particles in small test volumes . Normalization of sampled total voxel energy deposition by the number of voxels within the neuron increases the mean ED for 56Fe and 12C particles . A statistical realization of ED to the test neuron at given dose is sampled from the coincidence library and the pooled results are displayed in Fig 6 . A constant fluence value , as the number of particles for a given neuronal dose , is calculated and the random simulations that result from Poisson statistics are selected from the coincidence library as a statistical realization ( Materials and Methods ) . An example of such a sampling to dendritic branches , spines , filopodia and soma for 100 mGy neuronal dose is shown in Fig 6 for ( 56Fe , 12C , 1H , e- ) particle beams . The neuronal dose of the realization is ( 102 , 108 , 106 , 100 ) mGy in Fig 6 panels , A-D . If there is any coincidence of ED to any neuronal segments then the segmental dose is also calculated for the same sample in Fig 6E and 6F for the ( 56Fe , 12C , 1H , e- ) particles . The color code of the segment dose distribution represents dendrites ( green ) , filopodia ( white ) , long ( magenta ) , mushroom ( red ) and stubby ( blue ) spines . The total neuronal dose shows variability for the same fluence between each statistical sampling . Frequency histograms for four charged particles at 100 mGy are shown in Fig 6I–6L . A close inspection of the dose distribution in Fig 6A demonstrates that it results predominantly from δ-rays , and not the primary particle track . There are multiples of primary 12C tracks depicted as voxelated , dense straight lines both in the soma and the main dendritic branch in Fig 6B . There are many primary 1H beams traversing the soma , dendrites and some of the spines and filopodia in Fig 6C . Likewise , electron ED events in Fig 6D nearly fill every segment randomly , including the spines and filopodia . The number of neuronal segments hit and variability of segmental dose are correlated with the LET of the charged particles . The number of dendritic segments ( green dots ) out of 192 segments that received any hit are ( 126 , 140 , 191 , 192 ) for ( 56Fe , 12C , 1H , e- ) beams in Fig 6E–6H . Dendritic segmental doses show great variability for high LET 56Fe and 12C particles and there are segments receiving over 1 Gy doses for the 56Fe beam . Spines and filopodia with submicron volumes receive fewer hits for high LET particles compared to electrons or protons . However , average segment doses for these compartments are higher for high LET particles . A systemic statistical evaluation of these observations is given in the next section . The variability , quantified as standard deviation ( SD ) in total neuronal dose is ( 49 . 5 , 14 . 8 , 4 . 0 , 1 . 7 ) mGy in Fig 6I–6L . A useful measure to quantify variability of neuronal dose at given fluence is to introduce a coefficient of variation ( CV ) as the ratio of standard deviation to mean ( SD/mean ) neuronal dose over many sampling trials . In this way , the increase in standard deviation with increasing dose is represented as a unitless quantity ( CV ) and the coefficient of variation can be analyzed for the same neuron between different LET particles . As it is only displayed for the highest LET particle , 56Fe in Fig 7 for different neuronal doses the SD increases with dose but the ratio of SD to mean decreases . Similar results as found for Fe particles are also obtained for the other three ionizing particles used in this paper . A power law relation can be fit to the form of SD/mean = amplitude × Dosepower where Dose , amplitude and power are the mean dose and the amplitude and exponent of the power law relation . The values for amplitude ( 55 . 50 , 15 . 74 , 4 . 55 , 3 . 10 ) and power ( ˗0 . 5266 , ˗0 . 5138 , ˗0 . 5271 , ˗0 . 5345 ) are fitted for ( 56Fe , 12C , 1H , e- ) particles , respectively . The amplitude term ( amplitude ) signifies the variability of SD at the same mean dose between different LET particles as in Fig 6I–6L and there is a qualitative relation between LET and amplitude terms; high LET particles have higher amplitude and low LET particles have lower amplitude . Details of this relationship likely depend on the neuron structure and volume . On the other hand , the decrease in the power law relation fits to the same exponent ( ˗0 . 526±0 . 0091 ) for all particles considered . The numerical value of the exponent depends on the volume of the neuron , its compartments , LET of the particles and fluence dose that will be investigated in the next section . These results show that for 56Fe particles at low dose , e . g . 10 mGy with large CV of 1 . 64 in Fig 7A , 35% of the instances the neuronal dose is smaller than 2 mGy . On the other hand , the mean dose is larger than 50 mGy at 2 . 8% of the trials and even larger than 100 mGy at 0 . 84% of the sampled trials . The frequency of these neuronal doses can be understood by frequency of HZE particle tracks passing through the soma . For example , an 56Fe particle track with LET of 172 keV/μm crossing a path length of 10 μm of the soma and depositing 80% of its energy to the neuron would result in 81 . 5 mGy neuronal dose for a neuron with volume of 2700 μm3 . A similar calculation for two particle tracks crossing the soma but shorter cellular path lengths could give similar results . This example indicates the larger variability that will occur for low dose exposures of neuronal structures , especially for HZE particles and other high LET radiation . The neuron is represented as cylindrical segments and voxel ED events are recorded in all the segments . Functional compartments including soma , spines and filopodia are represented as two interconnected cylinders . Defining two corresponding segments as a unit compartment ( soma , spine , filopodium ) helps to characterize experimentally relevant observations including the number of spines , filopodia pruned or neurons survived following irradiation of biological samples [13 , 19 , 23 , 24] . Joining two sub-micron segment volumes to represent a spine and a filopodium compartment also increases total compartment volume hence reduces the standard deviation . Each dendritic segment is interpreted as a compartment in this study . The sampling statistics represents dose value as mean±SD at given fluence dose over many trials in soma , dendrites , spines and filopodia for ( 56Fe , 12C , 1H , e- ) particles and the ratio of dendrites , spines and filopodia are hit presented in Table 3 . The dose at each compartment is found by normalizing the total ED energies to the corresponding compartment volumes . The percentage of hits is reported as the number of compartments hit by ED events to total number of compartments of relevant type . The soma with a large volume ( 1129 . 6 μm3 ) is always hit at the doses considered in Table 3 . The statistics of other compartments of the test neuron is as follows . The volumes ( mean±SD ) of ( dendrite , spine , filopodium ) compartments are ( 8 . 12±5 . 91 , 0 . 119±0 . 039 , 0 . 114±0 . 028 ) μm3 . The total number of ( dendrite , spine , filopodium ) compartments are ( 192 , 631 , 577 ) giving rise to total volumes of ( 1558 . 9 , 75 . 1 , 65 . 8 ) μm3 , respectively . In general , the percentage of a compartment type that is hit and the mean compartment volume are inversely proportional for all the particles in Table 3 , and reaches unity at higher neuronal doses starting with the lower LET particles; electrons and protons . The probability of a hit of any compartment type is expected to be the ratio of the compartment type volume to the total neuron volume if voxel distribution is uniform . This inverse relation has the lowest ratio for the highest LET particle; 56Fe . In addition , the mean dose of any compartment type hit is inversely proportional to the mean compartment volume and the mean compartment dose is highest for the highest LET particle . Taken together , this relation leads to the same mean neuronal dose for all the particles . An interesting observation is the nearly constant and high mean compartmental dose at 1 to 400 mGy in spines and filopodia for 56Fe and 12C particles . The variability in mean dose between trials decreases in these sub-micron volumes with increasing fluence dose . The low LET protons and electrons achieve homogenous voxel dose distributions , however in comparison for high LET iron and carbon particles the ratio of spine , filopodia dose to neuron dose is still an order of magnitude higher at 10 mGy and only approaches unity at the higher doses in Table 3 . The variability in compartmental dose between types of compartments , particles of different LET values over a range of fluence dose require further analysis . The overall neuronal dose is distributed with large variability and the results in Table 3 support that the highly sampled data in Fig 7; the coefficient of variation of neuronal dose decreases with dose , close to an inverse square root relation . The sampled data in Table 3 support an exact power value of ˗0 . 5 for the neuron ( ˗0 . 5045±0 . 0404 ) and the soma ( ˗0 . 5084±0 . 0158 ) for all four particles considered . The dendrites with much smaller mean volumes have the power value of ( ˗1 . 692 , ˗1 . 269 , ˗0 . 5769 , ˗0 . 5352 ) for the ( 56Fe , 12C , 1H , e- ) particles . This indicates that lower LET particles approach the ˗0 . 5 exponent value in small dendritic segments . The power law relation for spines and filopodia with sub-micron volumes are fitted to power value of ˗2 . 658±1 . 281 and ˗2 . 579±0 . 673 for all the particles . All the amplitude terms on these fits are ordered from high to low as the LET values of the four particles , which is in agreement with Fig 7 .
The in silico model developed unifies stochastic energy deposition events of different radiation qualities and doses , and morphological data of neurons to explore radiation damage to neurons and their sub-compartments . The Monte-Carlo track structure simulation code , RITRACKs , characterizes the stochastics of energy deposition events of given charged particle at predetermined voxel volumes . Each Monte-Carlo simulation of a 20 micron track segment generated for the radiation library in this work represents a statistical sample for the subsequent calculations . The accuracy of the RITRACKs has been described in terms of comparisons of average quantities that result from statistical samples such as LET and radial dose distributions and for the case of a ED in fixed spherical or right cylindrical target volumes in previous work [29 , 30] , and in tests of similar quantities in analysis of electronic equilibrium described in this work . The neuron morphologies for hippocampal granule cells in C57BL6/Trk . T1 deficient mice [32 , 33] represent deterministic samples , with the exception of the number and positions of spines and filopodia along the dendrites as well as the orientation of a neuron cell relative to the particle track . Coincidence events of ED in voxels and neuronal segments are simulated to determine dose at given neuronal compartments . In this approach , layers of randomization steps are applied to statistically capture possible outcomes . Stochastics of arrival of charged particles , microscopic ED events of a particle , relative orientation of a particle track and the neuron and sampling of possible outcomes at given fluence dose are studied for a test neuron . The procedure with higher computational power can be extended to in silico brain structures or real anatomical morphological data , such as sliced and confocal scanned control and irradiated brain tissues that can be computationally processed and ED profiles at given fluence dose can be compared with irradiated structures to infer changes in neuronal anatomy for a particular type of particle and its kinetic energy . Statistical correlates of these predictions can be quantified by the ED distribution and dose in microscopic compartments including spines , filopodia , dendrites and soma . A useful outcome of this computational study would be to associate structural damage quantified as changes in the number of spines , filopodia , total dendritic length , total number of branches and cell number on control versus irradiated tissue samples of the same anatomical region of experimental animals . These changes can be interpreted as normal tissue effects in neurons for different types of ionizing radiation , at a given particle fluence or dose . It is widely known that changes in neuronal and spine structure as well as changes in the expression of various neurotransmitters account for the cognitive decline observed in normal aging and in the early stages of numerous neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease [34–37] . Many of the consequences of the foregoing insults to the brain can be linked to alterations in the outgrowth and elongation of dendrites , branching of the dendrites , number of dendritic endings and cell body area [34–37] . These morphometric changes in neurons depend on a variety of signaling cascades that regulate the amount of remodeling and neurite outgrowth in response to specific stimuli [38–40] . Neuronal cell morphology is modulated by a variety of intrinsic and extrinsic stimuli such as trophic factors , electrical activity , synaptogenesis , dendritic arborization , functional maturation and differentiation of neurons [40] . Morphologic remodeling of dendrites usually reflects a response to adverse conditions that trigger adaptive remodeling within the compromised microenvironment of the brain [40] . In addition to changes in neuronal morphology , slight changes in dendritic spines can have tremendous effects on synaptic function and the connectivity within neuronal circuits [34–37] . Dendritic spines are small membranous protrusions on neuronal dendrites that receive synaptic input from axon terminals and represent the structural correlates of learning and memory [37–43] . Dendritic spinogenesis and remodeling underscores the structural and synaptic plasticity critical for CNS function , as spine enlargement is tied to long-term potentiation , while spine shrinkage corresponds to long-term depression [38] Notably , disruptions in dendritic spine morphology , including the shape , size and number of spines , have been found in several developmental and neurodegenerative brain disorders [37–43] suggesting that dendritic spines are likely to serve as key players in the pathogenesis of radiation-induced cognitive dysfunction . The tree-like structure of neurons requires morphological constraints to be included in analysis of the response of neurons to ionizing radiation . In general , dendritic branches extend over a hundred microns from soma . The protruding spines from dendrites establish diffusion limited compartments for ions and cellular proteins [44 , 45] , while dynamic filopodia search for new connections sites to establish synapses [46] . The soma is spherical in shape and contains most of the organelles surrounding the nucleus . Organelles that are synthesized at the nucleus are transported to dendritic branches including spines to support and maintain cellular functions [47] . A direct target at the soma for microscopic ED events can be the nuclear DNA . The main dendritic branches extending from the soma support distal dendritic branches , spines and filopodia . Cellular damage to dendritic architecture by ionizing radiation could impact directly or indirectly the transport of cellular organelles to distal points [48] , local protein synthesis , and polymerization of actin based cytoskeleton to maintain spines and filopodia [49] . Cellular injury on main dendritic branches , proximal to soma as in Fig 6A–6D , may initiate cell death pathways and cause collapse of the whole neuron [50] . Differences in radiation sensitivity and morphology of the three types of spines [13 , 19] indicate dynamic molecular processes between cellular injury and spine pruning within micron scale compartments . Dendrite and spine pruning can change the electrophysiological properties of neurons , for example post synaptic input currents at spines established by spine properties change with spine numbers at excitatory synapses . These membrane currents coded as a change in membrane potential conducted along the dendrites to soma would change action potential kinetics . Filopodia are the most ionizing radiation sensitive compartments and reduction in their numbers was found to increase with dose following proton and X-ray irradiation [13 , 19] . Because filopodia are the motile ends it would be interesting to investigate if there are any long term detrimental effects marked by molecular processes to generate new filopodia after irradiation [26] . Energy deposition by charged particles is a stochastic process and as shown in this study ( Table 3 ) , compartments of a neuron can accumulate different amounts of energy for different particles types at given mean volume dose , Fig 6I–6L . The probability of hit of a compartment also varies greatly with LET for the same fluence dose . Voxel ED profiles of HZE particles and neuron morphology should be unified to have a conceptual picture of statistics presented in Table 3 . HZE particles have large local ED distribution within a fraction of a micron range shown as radial increase in Fig 5 . The fluence of HZE particles with large LET values , like 56Fe and 12C in this study , is sparse compared to low LET protons and electrons at the same dose as the number of particles aiming the anatomical structure is inversely scales with their LETs . Large mean voxel energies of high LET particles also contribute to heterogeneous dose distribution . In addition , the volume of a neuronal cell is compartmentalized and can extend great distances . Thus , a particle beam coinciding with different compartments of a neuron can have different structural damage patterns not adequately described by the particles absorbed dose or fluence . The larger dose variability of a neuron , quantified as standard deviation at lower fluence for high LET particles in Table 3 , indicates the number of primary particles crossing the soma . The low LET particles; protons , and electrons in this study establish a homogenous voxel distribution even at 1–100 mGy in Table 3 and 100 mGy in Fig 6G and 6H . Individual dendritic segments of a neuron with micron scale axial cross-section have a lower probability of hit than compared to the soma . The total deposited energy normalized by a dendritic segment volume ( 8 . 12±5 . 91 μm3 in this study ) leads to an order or larger compartmental doses for dendrites . High LET particles crossing the specific locations of dendritic branches may have different biological outcomes compared to low LET particles . Carbon has a LET value that is an order of magnitude smaller than iron and two orders of magnitude larger than protons or electrons . A comparative study between iron and carbon particles should include the variability between trials in Table 3 . For example , for the fluence at 10 mGy , the mean values of probability of hit and dose in dendritic segments are almost the same for 12C and 56Fe particles ( 12 . 1 , 11 . 4 ) mGy , respectively . The other parameters read from the Table 3 are; the variability is represented as CV ( 1 . 36 , 0 . 36 ) , the mean dendritic dose ( 110 , 73 ) mGy , and the mean number of dendritic segments hit ( 21 , 30 ) for ( 56Fe , 12C ) particles , respectively . The larger CV for 56Fe leads to a few dendritic segments to have an order higher segmental dose . The magnitude of CV for 12C particles would provide much uniform dendritic dose around the mean value . The outcomes of this statistics largely depend on neuronal response . Very high local compartmental dose may lead to irreparable cellular injury and pruning of dendritic branches . Determining dose and LET sensitivity of dendrite pruning thresholds for dendritic branches by experimental techniques and computational studies could help elucidate clinical implications in normal cell response under therapeutic radiation treatment and space radiation [20 , 21] . The smaller spine and filopodia compartments are hit more often by low LET protons and electrons than high LET particles at the same neuronal dose in Table 3 . Sparse coincidence events of high LET particles with large compartmental doses may guarantee spine and filopodia pruning . However , details of spine pruning process and compartmental dose relation can shift the effectiveness of low LET particles at low doses . For example , roughly 50% of the spines and filopodia receive over 160 mGy dose at 100 mGy fluence dose by protons and electrons while the ratio is around 7% for carbon and iron over 1 Gy compartment dose in Table 3 . A dose-response curve represented as a sigmoid function and mid-point value can determine the ratio of spines pruned and filopodia retracted by different LET particles and electrons at given fluence dose . Sensitivity of spine and filopodia pruning to low LET proton and electrons at 0 . 1 Gy [13] and at higher doses [16 , 19 , 23 , 26] motivates extensive experimental investigations complemented with computational microdosimetric studies as presented in this paper . In conclusion , evidence for the sensitivity of spines to low and high LET radiation has now been reported [13 , 19 , 51] , and suggests that radiation-induced reductions in spine density and morphologies are contributory if not causal for certain cognitive deficits found after cranial exposure . Even lower fluence of HZE particles [51] compared to photon or proton irradiation [13 , 19] were found to elicit impaired cognition coincident with dendritic alterations , suggesting that astronauts engaged in longer term space travel and exposed to charged particles are at heightened risk for developing adverse CNS deficits . The present study is the first investigation of the microscopic energy deposition that is induced by different radiation qualities across a wide range of dose or fluence in spines , dendrites and soma of hippocampal neurons . The results of the present study will be extremely valuable to support interpretation of experimental studies of radiation changes to cognition , and for developing detailed computational models of cognitive changes observed for different radiation qualities and doses .
|
Changes to cognition and other central nervous system ( CNS ) effects following charged particle exposures are of concern for medical patients undergoing radiation treatment in cancer therapy and for cosmic ray exposures to astronauts during space travel . Neuronal cell injury initiated by ionizing energy deposition involves a cascade of physico-chemical and patho-physiological pathways that are poorly understood . Neurons have diverse structural compartmental geometries , including the cell soma , dendrites , and spines . The intricate network of cellular connections requires the study of radiation damage to neuronal morphological features in understanding cellular injury and possible changes in cognitive function . This paper for the first time describes the spatial dependence of a particle’s microscopic dose deposition events on a detailed neuron structure . Heavy ions including iron , carbon and hydrogen particles , and energetic electrons that are common in space radiation exposures are considered . The computational model covers the stochastics of the generation of energy deposition events , delivery of particle beams and possible encounter schemes of microscopic dose and neuronal morphology . Results from this in silico study can be used to consider experimental studies in order to understand structural and functional neuronal damage following irradiation .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Irradiation of Neurons with High-Energy Charged Particles: An In Silico Modeling Approach
|
Mucosal-associated invariant T ( MAIT ) cells contribute to protection against certain microorganism infections . However , little is known about the role of MAIT cells in Orientia tsutsugamushi infection . Hence , the aims of this study were to examine the level and function of MAIT cells in patients with scrub typhus and to evaluate the clinical relevance of MAIT cell levels . Thirty-eight patients with scrub typhus and 53 health control subjects were enrolled in the study . The patients were further divided into subgroups according to disease severity . MAIT cell level and function in the peripheral blood were measured by flow cytometry . Circulating MAIT cell levels were found to be significantly reduced in scrub typhus patients . MAIT cell deficiency reflects a variety of clinical conditions . In particular , MAT cell levels reflect disease severity . MAIT cells in scrub typhus patients displayed impaired tumor necrosis factor ( TNF ) -α production , which was restored during the remission phase . In addition , the impaired production of TNF-α by MAIT cells was associated with elevated CD69 expression . This study shows that circulating MAIT cells are activated , numerically deficient , and functionally impaired in TNF-α production in patients with scrub typhus . These abnormalities possibly contribute to immune system dysregulation in scrub typhus infection .
Scrub typhus is a mite-borne bacterial infection in humans caused by Orientia tsutsugamushi , an obligate intracellular bacterium , prevalent in Asia , Northern Australia , and the Indian subcontinent . With early diagnosis and management , most patients with scrub typhus are able to recover without complications [1] . However , some patients develop serious and potentially fatal complications such as interstitial pneumonia , acute renal failure , myocarditis , meningoencephalitis , gastrointestinal bleeding , acute hearing loss , and multiple organ failure [2–4] . As the primary targets of O . tsutsugamushi are endothelial cells , the variable extent of vasculitis in each individuals helps in part to explain the different levels of severity [5] . However , a previous study has shown that diffuse alveolar damage could present without evidence of vasculitis , suggesting that the immunologic response plays a significant role in development of the disease and determination of the severity of illness [3] . The immune response induced by O . tsutsugamushi is a combination of innate and adaptive immunity and the proper response of macrophages and T lymphocytes may be the driving factor in immunity in patients with scrub typhus [5] . Furthermore , several studies reported dysfunction of the immunologic response of the host to O . tsutsugamushi; dysregulated levels of certain cytokines , imbalance of Th1/Th2 cytokines and apoptosis of T lymphocytes during acute infection [6–9] . These findings suggested that the pathogenesis of O . tsutsugamushi infection is not only related to the virulence of O . tsutsugamushi , but also to the host immune response . Mucosal-associated invariant T ( MAIT ) cells are a relatively newly recognized T cell subset that expresses a conserved invariant T cell receptor ( TCR ) α-chain ( Vα7 . 2-Jα33 in humans and Vα19-Jα33 in mice ) paired with a limited set of Vβ chains [10] . Human MAIT cells are defined as CD3+TCRδγ-Vα7 . 2+CD161high or CD3+TCRδγ-Vα7 . 2+IL-18Rα+ cells [11 , 12] . MAIT cells recognize bacteria-derived riboflavin ( vitamin B2 ) metabolites presented by the MHC class 1b-like related protein ( MR1 ) [10 , 13] . Upon antigen recognition , MAIT cells rapidly produce proinflammatory cytokines , such as interferon ( IFN ) -γ , tumor necrosis factor ( TNF ) -α , and interleukin ( IL ) -17 , in an innate-like manner [14] . MAIT cells maintain an activated phenotype throughout the course of an infection , secrete inflammatory cytokines , and have the potential to directly kill infected cells; thus , playing an important role in controlling the host response [11 , 12 , 15–19] . However , little is known about the role of MAIT cells in O . tsutsugamushi infection . Accordingly , the aims of this study were to examine the level and function of MAIT cells in patients with scrub typhus and to evaluate the clinical relevance of MAIT cell levels .
The study cohort comprised 38 patients with scrub typhus ( 25 women and 13 men; mean age ± SD , 64 . 3 ± 15 . 6 years ) and 53 healthy controls ( HCs; 30 women and 23 men; mean age ± SD , 63 . 6 ± 12 . 2 years ) . All patients were confirmed as having scrub typhus by the serologic test using a passive hemagglutination assay ( PHA ) to detect O . tsutsugamushi antigen . A positive result was defined as a titer of ≥ 1:80 in a single serum sample or at least a fourfold rise in antibody titer at follow-up examination . PHA was performed using Genedia Tsutsu PHA II test kits ( GreenCross SangA , Yongin , Korea ) . Scrub typhus patients were further divided into subgroups according to disease severity as previously described [7] . Patients with no organ dysfunction were considered to have mild disease , those with one organ dysfunction were considered to have moderate disease , while those with dysfunction of two or more organ systems were defined as having severe disease . Organ dysfunction was defined as follows: ( 1 ) renal dysfunction , creatinine ≥ 2 . 5 mg/dL; ( 2 ) hepatic dysfunction , total bilirubin ≥ 2 . 5 mg/dL; ( 3 ) pulmonary dysfunction , bilateral pulmonary infiltration on chest X-rays with moderate to severe hypoxia ( PaO2/FiO2 < 300 mmHg or PaO2 < 60 mmHg or SpO2 < 90% ) ; ( 4 ) cardiovascular dysfunction , systolic blood pressure < 80 mmHg despite fluid resuscitation; and ( 5 ) central nervous system dysfunction , significantly altered sensorium with Glasgow Coma Scale ( GCS ) ≤ 8/15 . None of the controls had a history of autoimmune disease , infectious disease , malignancy , chronic liver or renal disease , diabetes mellitus , immunosuppressive therapy , or fever within 72 hours prior to enrollment . The study protocol was approved by the Institutional Review Board of Chonnam National University Hospital , and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki . The following mAbs and reagents were used in this study: Allophycocyanin ( APC ) -Cy7-conjugated anti-CD3 , phycoerythrin ( PE ) -Cy5-conjugated anti-CD161 and fluorescein isothiocyanate ( FITC ) -conjugated anti-TCR γδ , FITC-conjugated anti-CD3 , FITC-conjugated anti-IFN-γ , FITC-conjugated annexin V , PE-conjugated anti-CD3 , PE-conjugated anti-IL-17 , PE-Cy7-conjugated anti-TNF-α , PE-conjugated anti-CD69 , FITC-conjugated mouse IgG isotype , PE-conjugated mouse IgG isotype and PE-Cy7-conjugated mouse IgG isotype control ( all from Becton Dickinson , San Diego , CA ) ; PE-conjugated anti-programmed death-1 ( anti-PD-1; eBioscience , San Diego , CA ) and APC-conjugated anti-TCR Vα7 . 2 ( BioLegend , San Diego , CA ) . Cells were stained with combinations of appropriate mAbs for 20 minutes at 4°C . Stained cells were analyzed on a Navios flow cytometer using Kaluza software ( version 1 . 1; Beckman Coulter , Brea , CA ) . Peripheral venous blood samples were collected in heparin-containing tubes , and PBMCs were isolated by density-gradient centrifugation using Lymphoprep ( Axis-Shield PoC AS , Oslo , Norway ) . MAIT cells were identified phenotypically as CD3+TCRγδ-Vα7 . 2+CD161high cells , by flow cytometry as previously described [20 , 21] . Total lymphocyte numbers were measured by Coulter LH750 automatic hematology analyzer ( Beckman Coulter , Miami , FL ) . Absolute numbers of MAIT cells were calculated by multiplying the MAIT cell percentages by the CD3+γδ- T cell percentages and the total lymphocyte numbers ( per microliter ) in peripheral blood . IFN-γ , IL-17 , and TNF-α expression in MAIT cells was detected by intracellular cytokine flow cytometry as previously described [11 , 12 , 21] . Briefly , freshly isolated PBMCs ( 1 × 106/well ) were incubated in 1 mL complete media , consisting of RPMI 1640 , 2 mM L-glutamine , 100 units/mL of penicillin , and 100 μg/mL of streptomycin , and supplemented with 10% fetal bovine serum ( FBS; Welgene , Gyeongsan , Korea ) for 4 hours in the presence of phorbol myristate acetate ( PMA ) ( 100 ng/mL; Sigma , St Louis , MO ) and ionomycin ( IM ) ( 1 μM; Sigma ) . For intracellular cytokine staining , 10 μL of brefeldin A ( GolgiPlug; BD Biosciences , San Diego , CA ) was added , and the final concentration of brefeldin A was 10 μg/mL . After incubation for additional 4 hours , cells were stained with APC-Cy7-conjugated anti-CD3 , PE-Cy5-conjugated anti-CD161 , and APC-conjugated anti-TCR Vα7 . 2 mAbs for 20 minutes at 4°C , fixed in 4% paraformaldehyde for 15 minutes at room temperature , and permeabilized using Perm/Wash solution ( BD Biosciences ) for 10 minutes . Cells were then stained with FITC-conjugated anti-IFN-γ , PE-conjugated anti-IL-17 and PE-Cy7-conjugated anti-TNF-α mAbs for 30 minutes at 4°C and analyzed by flow cytometry . To determine changes in expression levels of CD69 , annexin V and PD-1 in MAIT cells after stimulation with IL-12 and IL-18 , freshly isolated PBMCs ( 1 × 106/well ) were incubated in 1 mL complete media for 24 hours in the presence of IL-12 ( 50 ng/mL; Miltenyi biotec , Bergisch Gladbach , Germany ) and IL-18 ( 50 ng/mL; Medical and Biological Laboratories , Woburn , MA ) . Cells were then stained with FITC-conjugated anti-CD3 , FITC-conjugated annexin V , APC-conjugated anti-TCR Vα7 . 2 , PE-conjugated anti-CD3 , PE-conjugated anti-CD69 , PE-conjugated anti-PD-1 and PE-Cy5-conjugated anti-CD161 mAbs for 20 minutes at 4°C . CD69+ , annexin V+ , and PD-1+ MAIT cell levels were determined by flow cytometry . All comparisons of percentages and absolute numbers of MAIT cells , their cytokine levels , and expression levels of CD69 , PD-1 and annexin V were analyzed using the Mann-Whitney U tests or paired t test . Linear regression analysis was used to test associations between MAIT cell levels and clinical or laboratory parameters . The Wilcoxon matched-pairs signed rank test was used to compare changes in MAIT cell levels and functions according to disease activity . P values less than 0 . 05 were considered statistically significant . Statistical analyses and graphic works were performed using SPSS version 18 . 0 software ( SPSS , Chicago , IL ) and GraphPad Prism version 5 . 03 software ( GraphPad Software , San Diego , CA ) , respectively .
The clinical and laboratory characteristics of scrub typhus patients are summarized in Table 1 . Thirty-eight patients were included in this study . Of the 38 scrub typhus patients , 26 patients ( 68 . 4% ) had mild disease; 9 patients ( 23 . 7% ) had moderate disease; and 3 patients ( 7 . 9% ) had severe disease . Eighteen patients were monitored longitudinally from the active state ( before antibiotic therapy ) to the remitted state ( defined as resolution of all presenting symptoms after antibiotic therapy ) . The percentages and absolute numbers of MAIT cells in the peripheral blood samples of 38 patients with scrub typhus and 53 HCs were determined by flow cytometry . MAIT cells were defined as CD3+TCRγδ- cells expressing TCR Vα7 . 2 and CD161high ( Fig 1A ) . Percentages of MAIT cells were significantly lower in scrub typhus patients than in HCs ( median 0 . 69% versus 1 . 37% [p < 0 . 05] ) ( Fig 1B ) . Absolute numbers of MAIT cells were calculated by multiplying MAIT cell percentages by the CD3+TCRγδ- T cell percentages and the total lymphocyte numbers ( per microliter of peripheral blood ) . Scrub typhus patients had significantly lower absolute numbers of MAIT cells than HCs ( median 1 . 89 cells/μL versus 11 . 0 cells/μL [p < 0 . 001] ) ( Fig 1C ) . To evaluate the clinical relevance of MAIT cell levels in 38 patients with scrub typhus , we investigated the correlation between MAIT cell percentages in the peripheral blood and clinical parameters by regression analysis ( Table 2 ) . The univariate linear regression analysis showed that circulating MAIT cell percentages were significantly correlated with age , alanine aminotransferase level , alkaline phosphatase level , and severity ( p = 0 . 001 , p = 0 . 043 , p = 0 . 009 , and p = 0 . 047 , respectively ) . However , no significant correlation was observed between MAIT cell percentages and leukocyte count , lymphocyte count , hemoglobin level , neutrophil count , platelet count , total bilirubin level , total protein level , albumin level , aspartate aminotransferase level , lactate dehydrogenase level , C-reactive protein level and erythrocyte sedimentation rate ( Table 2 ) . Hepatocytes and endothelial cells stimulated by proinflammatory cytokines ( e . g . , IFN-γ and TNF-α ) have been known to kill intracellular rickettsiae via inducible nitric oxide synthase expression and nitric oxide-dependent mechanism [22 , 23] . To investigate the expression of these cytokines in MAIT cells , we incubated PBMCs from 23 patients with scrub typhus and 14 HCs for 4 hours in the presence of PMA and IM; then the expressions of IFN-γ , IL-17A , TNF-α in the MAIT cell populations were examined at the single-cell level by intracellular flow cytometry ( Fig 2A ) . Percentages of TNF-α+ MAIT cells were found to be significantly lower in scrub typhus patients than in HCs ( median 15 . 6% versus 45 . 0% [p < 0 . 05] ) . However , IFN-γ+ or IL-17A+ MAIT cell levels were comparable between scrub typhus patients and HCs ( Fig 2B ) . The relationship between the expression of immune activation markers and the loss of circulating MAIT cells has been reported in HIV-infected patients [24] . To determine whether MAIT cell deficiency in scrub typhus patients is correlated with activation-induced cell death , CD69+ and annexin V+ cell levels in circulating MAIT cells were determined by flow cytometry . Percentages of CD69+ MAIT cells were found to be significantly higher in scrub typhus patients than in HCs ( median 36 . 8% versus 6 . 0% [p < 0 . 0001] ) ( Fig 3A and 3B ) . However , annexin V+ cell levels were comparable between scrub typhus patients and HCs ( Fig 3C and 3D ) . PD-1 and its ligands , PD-1 L1 and PD-L2 , are known to deliver inhibitory signals that regulate the balance among T cell activation , tolerance and immunopathology [25] . To determine whether impaired TNF-α production by MAIT cells is related to PD-1 , we examined the expression levels of PD-1 in the peripheral blood samples of 17 scrub typhus patients and 14 HCs . The expression levels of PD-1 in MAIT cells were similar between scrub typhus patients and HCs ( Fig 3E and 3F ) . To determine whether MAIT cells can be activated by IL-12 and IL-18 , PBMCs from HCs were cultured with IL-12 and IL-18 for 24 hours and then CD69+ , annexin V+ , and PD-1+ cell levels in MAIT cells were determined by flow cytometry . Percentages of CD69+ MAIT cells were found to be significantly higher in IL-12- and IL-18-treated cultures compared with untreated cultures ( mean ± SEM 59 . 9 ± 10 . 29% versus 6 . 5 ± 0 . 48% [p < 0 . 005] ) ( Fig 4A and 4B ) . However , annexin V+ and PD-1+ MAIT cell levels were comparable between the treated and untreated cultures ( Fig 4C and 4F ) . Based on our observation that circulating MAIT cell levels and TNF-α production are reduced in scrub typhus patients , we investigated the changes in circulating MAIT cell levels and functions in relation to disease activity . Eighteen and eleven patients were available for follow-up examination of MAIT cell levels and functions , respectively . As shown in Fig 5A , no significant changes in circulating MAIT cell levels were found according to disease activity . However , TNF-α+ MAIT cell levels were found to be greater when the disease was in remission than when it was active ( median 56 . 8% versus 34 . 1% [p < 0 . 05] ) ( Fig 5B ) .
Although the importance of an immune response of MAIT cells in host protection against certain mycobacterial and enterobacterial infections is well established [11 , 12 , 15–19] , the role of MAIT cells in O . tsutsugamushi infection remains unclear . To the best of our knowledge , this is the first study to measure the levels and functions of circulating MAIT cells in scrub typhus patients and to examine the clinical relevance of MAIT cell levels . The present study showed that percentages and numbers of circulating MAIT cells were lower in scrub typhus patients than in HCs . We also demonstrated that MAIT cell deficiency reflects disease severity . In particular , in vitro experiments showed poor production of TNF-α by MAIT cells during active disease , but TNF-α production was found to be increased during remission . Finally , our study showed that the impaired production of TNF-α by MAIT cells was associated with elevated CD69 expression in scrub typhus patients . These findings possibly contribute to immune system dysregulation in scrub typhus infection and have important implications for the development of cell-based immunotherapy . In this study , circulating MAIT cell levels were found to be reduced in scrub typhus patients . In contrast , frequencies of other T cell subsets ( e . g . , CD3 T cells , αβ T cells and γδ T cells ) were found to be similar between the patients and HCs ( S1 Fig ) , suggesting that the decline in cell levels is specific to MAIT cells . MAIT cell deficiency in peripheral blood has also been reported in several other infectious diseases , including Vibrio cholerae O1 infection , Pseudomonas aeruginosa infection in cystic fibrosis , and severe bacterial sepsis , in particular non-streptococcal infection [26–28] . Our previous study revealed that MAIT cells were numerically and functionally deficient in patients with pulmonary tuberculosis and nontuberculous mycobacteria lung disease [19] . In the present study , circulating MAIT cells were more reduced in patients with organ dysfunction than in patients without organ dysufunction ( S2 Fig ) . It is well known that scrub typhus occurs when the host is bitten by an O . tsutsugamushi-infected trombiculid mite and it can spread to other organs via the bloodstream ( e . g . , kidney , liver , lung and brain ) [2] . Thus , the decline in circulating MAIT cell numbers might be due to migration of MAIT cells from the peripheral circulation to infected tissues and organs , for defending against O . tsutsugamushi infection . The present study revealed that MAIT cell percentages in the peripheral blood were significantly correlated with age , alanine aminotransferase level , alkaline phosphatase level , and disease severity . Consistent with our data , a recent study demonstrated that MAIT cell deficiency was associated with increased disease severity in cystic fibrosis patients with P . aeruginosa infection [27] . Furthermore , these findings are supported by our previous studies which showed that MAIT cell deficiency is related to the disease severity or extent in patients with chronic obstructive pulmonary disease and mycobacterial infection [19 , 29] . Collectively , circulating MAIT cell levels may reflect the inflammatory activity or severity of infectious diseases . However , after multivariate analysis , only age was found to be a significant independent predictors of MAIT cell deficiency in scrub typhus patients ( Table 2 ) . Similarly , circulating MAIT cell levels were found to be considerably affected by age , irrespective of the healthy or disease state [21 , 30 , 31] . Therefore , age-dependent changes in MAIT cell levels could be explained partially by the fact that old age is one of the risk factors affecting the complication and mortality in scrub typhus or other infectious diseases [32 , 33] . Interestingly , we also found that liver function abnormalities , especially in alanine aminotransferase and alkaline phosphatase , were positively correlated with the MAIT cell levels . Liver is known to be a site of accumulation of MAIT cells and abnormal liver function is frequently observed in scrub typhus patients [34 , 35] . Therefore , further studies are needed to investigate the integrated role of MAIT cells in hepatic injury . The fundamental role of TNF-α , one of the cytokines secreted by the Th1 subset , in control of intracellular growth of O . tsutsugamushi is well established [36 , 37] . In the present study , the production of TNF-α by MAIT cells was found to be diminished in scrub typhus patients , whereas no significant differences were found between scrub typhus patients and HCs in the production of IFN-γ or IL-17 . These findings suggest that the dysregulation of TNF-α production may have a certain role in the pathogenesis of scrub typhus . However , it is not clear why TNF-α secretion by MAIT cells was reduced in response to PMA/IM stimulation . One possible explanation is that impaired TNF-α production by MAIT cells may be due to anergy or exhaustion of these cells during infection . These findings have also been previously observed in other infectious diseases that showed upregulation of coinhibitory receptors [19 , 24 , 38] . Another possibility is that the impairment of TNF-α production represents the susceptibility of individuals to O . tsutsugamushi infection . Interestingly , a previous study revealed that O . tsutsugamushi inhibited TNF-α production by inducing IL-10 secretion in murine macrophages [39] . Third possibility is that high levels of proinflammatory cytokines can induce such apparent reduced responsiveness of MAIT cells . This hypothesis is supported by our supplementary data showing that plasma levels of proinflammatory cytokines , such as IFN-γ and TNF-α , were found to be significantly higher in scrub typhus patients than in HCs ( S3 Fig ) , which was consistent with the results of previous study [40] . Moreover , these similar findings have also been observed in our previous study treating another invariant T cells ( e . g . , natural killer T cells ) with IL-1β , IL-6 , IL-8 , IL-18 , IFN-γ and TNF-α [41] . Further studies are needed to prove this hypothesis in MAIT cells . Altogether , these results indicate that impaired TNF-α production by MAIT cells is due to a negative-feedback mechanism that allows the pathogen to survive in the hostile environment . MAIT cells have been known to be stimulated either in an MR1-dependent manner or in an MR1-independent manner [42] . Our study showed that MAIT cells were activated in scrub typhus patients , indicated by CD69 up-regulation . According to the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) at http://www . kegg . jp/kegg/pathway . html , none of O . tsutsugamushi Boryong and Ikeda strains harbor the riboflavin biosynthesis pathway . Until proven otherwise , this means that this bacterial strain ( especially , Boryong , most common strain in South Korea ) does not provide the MAIT cell MR1 ligand , suggesting that MAIT cells can be activated in MR1-independent manner . It has been previously reported that MAIT cells highly express IL-18R and IL-12R [43 , 44] . In accordance with the results of previous studies [40 , 43] , the present study demonstrated that MAIT cells were activated after stimulation by IL-12 and IL-18 . Furthermore , a previous study showed that plasma IL-12p40 and IL-18 levels were elevated in scrub typhus patients [40] . Collectively , these findings suggest that MAIT cells can be activated by IL-12 and IL-18 , in an MR1-independent manner , in scrub typhus . CD69 is known to be early activation marker , whereas PD-1 is considered as relative late activation marker . Following TCR activation , CD69 is upregulated within ~4 hours [45] , whereas PD-1 is upregulated within 24–72 hours [46] . PD-1 expression in circulating MAIT cells is also known to be upregulated in chronic infectious diseases , such as chronic human immunodeficiency virus , tuberculosis , and chronic hepatitis C virus infections [19 , 47 , 48] . In addition , recent studies reported that the PD-1 plays a complex role during acute infection [46] . However , little is known about the role of PD-1 in acute bacterial infection , particularly in scrub typhus . Investigation of CD69 and PD-1 expression on MAIT cells can provide information on the time course of disease activation during O . tsutsugamushi infection . In the current study , circulating MAIT cells were found to be markedly activated , as indicated by the upregulation of CD69 expression , while marginal expression of PD-1 was observed in a subset of acute scrub typhus patients , indicating that MAIT cells may play an important role in early phase during scrub typhus infection . In line with our data , Doe et al recently reported that CD4+ T cells from mice infected with Plasmodium parasites expressed PD-1 as early as 6 days after infection , while Listeria monocytogenes induced marginal expression of PD-1 [49] . These results imply that the mode and function of PD-1 expression might differ between acute infection and chronic infection , i . e . , PD-1 expression may occur in the late stage of infection or may depend on the microbes . In addition , our data showed that PD-1+ MAIT cell levels were inversely correlated with the corresponding TNF-α production by MAIT cells from the patients ( S4 Fig ) , suggesting that functional impairment of MAIT cells may partially be due to the negative regulation including PD-1 . Another interesting finding from our study is that the levels of apoptotic T cells , defined as annexin V+ cells , were comparable between scrub typhus patients and HCs . These results indicate that reduced MAIT cells in peripheral blood might be due to their migration to the infected tissue or organs , rather than MAIT cell death . In the present study , impairment in TNF-α production of MAIT cells in the acute phase of scrub typhus infection was found to be restored during the remission phase , while numerical deficiency of MAIT cells did not change over time . Consistent with our data , a previous study demonstrated that the function of MAIT cells was restored partially after effective antiretroviral therapy , although levels of MAIT cells in peripheral blood were not restored [24] . A similar finding about MAIT cell levels in peripheral blood has also been reported in pediatric patients with Vibrio cholerae O1 infection [26] . Circulating MAIT cell levels were significantly reduced at day 7 and this deficiency persisted up to 90 days after onset of cholera . In a recent study , circulating MAIT cell numbers started to increase as early as 4 days after severe sepsis , but persistent depletion of MAIT cells was found in some patients , depending on their clinical condition [28] . Thus , further long-term follow-up studies are needed to determine when circulating MAIT cell levels are restored after scrub typhus infection . In summary , the present study demonstrates that circulating MAIT cells are activated , numerically deficient , and functionally impaired in TNF-α production in patients with scrub typhus . In addition , MAIT cell deficiency reflects disease severity . These findings provide important information for predicting the prognosis of scrub typhus infection .
|
Scrub typhus is a mite-borne bacterial infection in humans caused by Orientia tsutsugamushi , an obligate intracellular bacterium , prevalent in Asia , Northern Australia , and the Indian subcontinent . The pathogenesis of O . tsutsugamushi infection is known to be not only related to the virulence of O . tsutsugamushi , but also to the host immune response . Mucosal-associated invariant T ( MAIT ) cells are an evolutionarily conserved antimicrobial MR1-restricted T cell subset . Upon antigen recognition , MAIT cells rapidly produce proinflammatory cytokines , maintain an activated phenotype throughout the course of an infection , and have the potential to directly kill infected cells; thus , playing an important role in controlling the host response . However , little is known about the role of MAIT cells in Orientia tsutsugamushi infection . To the best of our knowledge , this is the first study to measure the levels and functions of circulating MAIT cells in scrub typhus patients and to examine the clinical relevance of MAIT cell levels . The present study demonstrates that circulating MAIT cells are activated and numerically deficient in patients with scrub typhus . Notably , impairment of TNF-α production represents the susceptibility of individuals to O . tsutsugamushi infection . These findings provide important information for predicting the prognosis of scrub typhus infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"blood",
"cells",
"flow",
"cytometry",
"typhus",
"medicine",
"and",
"health",
"sciences",
"innate",
"immune",
"system",
"immune",
"cells",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"cytokines",
"body",
"fluids",
"pathogens",
"immunology",
"microbiology",
"bacterial",
"diseases",
"developmental",
"biology",
"molecular",
"development",
"cell",
"enumeration",
"techniques",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"orienta",
"tsutsugamushi",
"infectious",
"diseases",
"white",
"blood",
"cells",
"animal",
"cells",
"medical",
"microbiology",
"t",
"cells",
"microbial",
"pathogens",
"scrub",
"typhus",
"hematology",
"spectrophotometry",
"immune",
"system",
"cytophotometry",
"blood",
"cell",
"biology",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"total",
"cell",
"counting",
"spectrum",
"analysis",
"techniques"
] |
2016
|
Activation, Impaired Tumor Necrosis Factor-α Production, and Deficiency of Circulating Mucosal-Associated Invariant T Cells in Patients with Scrub Typhus
|
Animal models are needed to better understand the pathogenic mechanisms of Zika virus ( ZIKV ) and to evaluate candidate medical countermeasures . Adult mice infected with ZIKV develop a transient viremia , but do not demonstrate signs of morbidity or mortality . Mice deficient in type I or a combination of type I and type II interferon ( IFN ) responses are highly susceptible to ZIKV infection; however , the absence of a competent immune system limits their usefulness for studying medical countermeasures . Here we employ a murine model for ZIKV using wild-type C57BL/6 mice treated with an antibody to disrupt type I IFN signaling to study ZIKV pathogenesis . We observed 40% mortality in antibody treated mice exposed to ZIKV subcutaneously whereas mice exposed by intraperitoneal inoculation were highly susceptible incurring 100% mortality . Mice infected by both exposure routes experienced weight loss , high viremia , and severe neuropathologic changes . The most significant histopathological findings occurred in the central nervous system where lesions represent an acute to subacute encephalitis/encephalomyelitis that is characterized by neuronal death , astrogliosis , microgliosis , scattered necrotic cellular debris , and inflammatory cell infiltrates . This model of ZIKV pathogenesis will be valuable for evaluating medical countermeasures and the pathogenic mechanisms of ZIKV because it allows immune responses to be elicited in immunologically competent mice with IFN I blockade only induced at the time of infection .
Zika virus ( ZIKV , Flaviviridae , Flavivirus ) is an arthropod-borne virus ( arbovirus ) that is closely related to dengue , West Nile , Japanese encephalitis and yellow fever viruses [1 , 2] . ZIKV was first isolated in Uganda in 1947 from a febrile sentinel rhesus monkey in the Zika forest [3 , 4] . No significant outbreaks of ZIKV infection involving more than a few persons were detected until 2007 , when ZIKV caused an explosive outbreak in Micronesia [5–8] where approximately 75% of the population on the island of Yap became infected during a four-month period [5] . In subsequent years , ZIKV continued to spread throughout Oceania [9–12] . In early 2015 , ZIKV first emerged in the Western Hemisphere with an outbreak detected in Brazil [13 , 14] . The virus spread rapidly throughout Latin America and the Caribbean and within one year most countries in the region reported local transmission [15 , 16] . ZIKV is expected to continue to spread and imported cases from travelers returning from Latin America and the Caribbean have already been reported in several countries including the U . S . and Europe [15 , 17–19] . In fact , numerous locally acquired mosquito-borne cases have recently been reported in Florida . Historically , infections with ZIKV are asymptomatic and have been associated with a self-limiting febrile illness with no long-term sequelae , but more severe complications have become apparent during the recent outbreaks in the South Pacific and Latin America . In particular , significant concern is growing about the association of ZIKV infection and the development of fetal abnormalities such as microcephaly . ZIKV was isolated from the brains and cerebrospinal fluid of neonates born with microcephaly and identified in the placental tissue of mothers who had symptoms consistent with ZIKV infection during pregnancy [20–22] . An additional concern is the association of ZIKV infection and Guillain-Barré syndrome ( GBS ) . GBS is an autoimmune polyradiculoneuropathy that can result in weakness , paralysis , and death [23–25] , and was first associated with ZIKV infection during the 2013–2014 outbreak in French Polynesia . Cases of a diffuse demyelinating disorder consistent with GBS that are temporally associated with ZIKV infection have been reported in Brazil , El Salvador , Colombia , and Venezuela [26] . As ZIKV continues to spread , so does concern about the association of ZIKV infection and the development of severe clinical complications . Therefore , the development of medical countermeasures for ZIKV is a high research priority . Animal models are needed to better understand the pathogenic mechanisms of ZIKV and to evaluate candidate medical countermeasures . Early ZIKV mouse models have relied on the use of juvenile animals and/or intracerebral inoculations [3 , 4 , 27–35] . These initial studies suggest that in mice ZIKV can replicate and cause injury in cells of the CNS . In contrast , other animals to include cotton rats , guinea pigs , rabbits , and rhesus monkeys did not develop CNS disease even when infected by intracerebral inoculation [3] . In mice , neuronal degeneration and cellular infiltration were observed in regions of the spinal cord and brain [3] . Neuronal injury was also evident in the pathological evaluation of a human fetus infected in utero with ZIKV . Diffuse astrogliosis and activation of microglia were observed and damage extended to the brain stem and spinal cord [22] . Recently , mice deficient in the type I or type II interferon response developed severe neurological disease due to ZIKV infection [36–40] . ZIKV-infected Ifnar1-/- mice ( C57BL/6 background mice lacking the IFN α/β receptor ) developed disease that was associated with high viral titers in the brain and spinal cord [39] . Similar results were described for A129 mice ( 129Sv/Ev background mice lacking the IFN α/β receptor ) , which were highly susceptible to ZIKV and developed neurological disease [36 , 38] . AG129 mice ( 129Sv/Ev background mice lacking the IFN α/β and γ receptors ) were found to be more susceptible to ZIKV-induced disease compared to A129 mice [36] . Collectively , these efforts underscore the importance of innate immunity in modulating ZIKV infection and disease outcome . The major limitation of these recently described ZIKV mouse models is that they utilize immunodeficient mice . These mouse models lack a key component of antiviral immunity which impairs comprehensive evaluation of medical countermeasures . In an attempt to produce infection models that do not rely upon knockout mice , several groups , including ours , have explored the temporal blockade of IFN-I in immune intact mice using polyclonal and monoclonal antibodies targeting either IFN-Is directly or the IFN-I receptor . The major advantage of this approach is that it allows immune responses to be elicited in immunologically competent mice with IFN I blockade only induced at the time of infection . A murine non-cell depleting monoclonal antibody ( MAb ) that efficiently targets the IFNAR-1 subunit of the mouse IFN-α/β receptor ( MAb-5A3 ) was developed and shown to prevent type I IFN-induced intracellular signaling in vitro and to inhibit antiviral , antimicrobial , and antitumor responses in mice [41] . MAb-5A3 has been used to explore the role of IFN-I in the infection and pathogenesis of several viruses including West Nile virus ( WNV ) [42] , lymphocytic choriomeningitis virus , and vesicular stomatitis virus ( VSV ) [41] . For VSV and WNV , treatment of mice with this antibody results in a severe and lethal infection model similar to that produced in IFN-I receptor knockout mice . Here , we describe the use of MAb-5A3 antibody to block IFN-I signaling in immune intact , wild-type mice at the time of ZIKV infection . We demonstrate that these mice develop severe ZIKV-mediated disease accompanied by significant neuroinflammation and mortality when infected by multiple exposure routes . While we were completing this study , another group also reported the use of this system for ZIKV studies [39 , 43] . Although in the first report , these authors were unable to demonstrate ZIKV lethality , in the second study , they did find lethality using an African lineage strain that was derived from a brain homogenate by passage of the virus in Rag1-/- mice . Our report not only expands on those findings , but also provides the first comprehensive description of the pathologic changes associated with ZIKV infection using this model . This model of ZIKV pathogenesis will be valuable for evaluating medical countermeasures because it allows an immune response to be elicited in immunocompetent mice and infection is enhanced at the time of virus challenge .
This work was supported by an approved USAMRIID IACUC animal research protocol . Research was conducted under an IACUC approved protocol in compliance with the Animal Welfare Act , PHS Policy , and other Federal statutes and regulations relating to animals and experiments involving animals . The facility where this research was conducted is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals , National Research Council , 2011 . Approved USAMRIID animal research protocols undergo an annual review every year . Animals are cared for by a large staff of highly qualified veterinarians , veterinary technicians , and animal caretakers . All personnel caring for and working with animals at USAMRIID have substantial training to ensure only the highest quality animal care and use . Humane endpoints were used during all studies and mice were humanely euthanized when moribund according to an endpoint score sheet . Mice were euthanized by terminal exsanguination or CO2 exposure using compressed CO2 gas followed by cervical dislocation . However , even with multiple observations per day , some animals died as a direct result of the infection . ZIKV strain DAK AR D 41525 isolated in 1984 from Aedes africanus mosquitoes in Senegal and was obtained from the World Reference Center for Emerging Viruses and Arboviruses ( R . Tesh , University of Texas Medical Branch ) where it was amplified once in AP61 and C6/36 cells , and two times in Vero cells prior to our receipt . We then amplified the virus once more in Vero cells ( ATCC , CCL-81 ) prior to use in this study and sequenced [44] . Female C57BL/6 mice ( n = 10/group; Jackson Laboratories ) five weeks of age were injected IP with a total of 3 . 0 mg ( 2 . 0 mg first dose , 0 . 5 mg subsequent doses ) of MAb-5A3 ( produced by Leinco Technologies , St . Louis , MO ) [41 , 45] or PBS on day -1 , day +1 , and day 4 . A prior study characterizing this antibody indicated that a large bolus is needed to saturate the IFNAR-1 receptor pool and the half-life of a 2 . 0 mg injected dose is 5 . 2 days [41] . On day 0 , mice were infected with 6 . 4 log10 PFU of ZIKV strain DAK AR D 41525 by the SC ( in between the shoulder blades ) or IP exposure route in a total volume of 200 μL . Mice were monitored for signs of disease and bled on day 4 post-infection ( PI ) or when euthanized to evaluate viremia . A cohort of Mab-5A3-treated , uninfected control mice ( n = 3 ) was included for histopathology assessment . These control mice were treated with the antibody as described above and euthanized on day 10 . Mouse serum samples were inactivated using a 3:1 ratio of TRIzol LS Reagent ( Thermo Fisher Scientific , Waltham , MA ) . Tissues were homogenized in 1X Minimum Essential Medium with Earle’s Salts and L-glutamine ( MEM ) with 1% penicillin/streptomycin and 5% heat-inactivated fetal bovine serum ( FBS-HI ) using a gentleMACS dissociator ( Miltenyi Biotec , San Diego , CA ) followed by centrifugation at 10 , 000 x g for 10 minutes and the supernatant was stored at -80°C until further evaluation . Supernatant was inactivated using a 3:1 ratio of TRIzol LS . Total nucleic acid from all samples was purified using the EZ1 Virus Mini Kit v 2 . 0 ( Qiagen , Valencia , CA ) and the EZ1 Advanced XL robot ( Qiagen ) according to the manufacturer’s recommendations . Samples were eluted in 60μL . Viral load was determined using a real-time RT-PCR assay specific to the 5’-untranslated region of ZIKV . Specific amplification detection was accomplished using a forward primer ( 5’-GARTCAGACTGCGACAGTTCGA ) , reverse primer ( 5’-CCAAATCCAAATTAAACCTGTTGA ) , and probe ( 5’-ACTGTTGTTAGCTCTCGC–MGBNFQ ) . A standard curve was generated using serial dilutions of the challenge virus having PFU/mL titers determined by plaque assay . Five μL of extracted nucleic acid were run in triplicate on the LightCycler 480 ( Roche Diagnostics , Inc . , Indianapolis , IN ) using SuperScript One-Step RT-PCR ( Thermo Fisher Scientific ) , and samples were considered negative if the cycle of quantification ( Cq ) was greater than 40 cycles . This Cq cutoff value was selected because when Cq's are greater than 40 cycles , you are outside of the linear dynamic range of real-time PCR , and thus , can negatively impact data reproducibility . The virus titers were calculated using the standard curve and the LightCycler 480 software , and the final PFU equivalents/mL ( PFUe/mL ) calculations were determined based on the sample input volumes and the upfront sample dilutions . Vero cells were plated at 3x105 cells/well in a six-well plate and incubated overnight at 37°C , 5% CO2 . Serial dilutions of samples were made in 1X MEM with 1% penicillin/streptomycin and 5% heat-inactivated FBS . Uninfected-control and serially-diluted samples were incubated with the Vero cells for one hour at 37°C , 5% CO2 for virus adsorption . The inoculum was removed and a 1:1 mixture of 0 . 8% ( w/v ) Seaplaque agarose and 2X Basal Medium Eagle with Earle’s Salts ( EBME ) solution containing 2X EBME , 10% FBS-HI , 2% penicillin/streptomycin , 50 μg/mL gentamicin , and 2 . 5 μg/mL Fungizone/Amphotericin B was added . After addition , the 0 . 4% Seaplaque agarose/2X EBME overlay was incubated at room temperature for 30 minutes to allow the overlay to solidify . Vero cells were incubated with the overlay at 37°C , 5% CO2 for five days before the overlay was removed . Cells were fixed and plaques were visualized by a 20 minute addition of 10% formalin with 50% Crystal Violet solution followed by a wash with water . A necropsy was completed to collect the spleen , liver , head ( to include brain ) , heart , kidney and spinal cord . All collected tissues were immersion fixed in 10% neutral buffered formalin for at least 2 days . The tissues were trimmed and processed according to standard protocols [46] . Histology sections were cut at 5 to 6 μM on a rotary microtome , mounted onto glass slides , and stained with hematoxylin and eosin ( HE ) . Unblinded histological examination was performed by a board-certified veterinary pathologist . In situ hybridization was performed using RNAscope 2 . 5 HD RED kit according to the manufacturer’s recommendations ( Advanced Cell Diagnostics , Hayward , CA ) . Briefly , 20 ZZ probes set targeting the 1550–2456 fragment of the ZIKV polyprotein gene [47] were synthesized . After deparaffinization and peroxidase blocking , the sections were heated in antigen retrieval buffer and then were digested by proteinase . The sections were covered with ISH probes and incubated at 40°C in a hybridization oven for two hours . They were rinsed and the ISH signal was amplified by applying Pre-amplifier and Amplifier conjugated with HRP . A red substrate-chromogen solution was applied for 10 minutes at room temperature . The slides were further stained with hematoxylin , air dried , and mounted . Formalin-fixed , paraffin-embedded mouse brain sections on slides were deparaffinized in xyless and rehydrated through graded ethanol ( 100% , 95% , 90% , and 70% ) . Antigen was retrieved by citric acid-based antigen unmasking solution ( Vector Laboratories ) during 10 minute boiling . After three washes with PBS ( pH 7 . 4 ) , the sections were blocked with 10% normal donkey serum in PBS-tween ( 0 . 1%; PBS-T ) for one hour at room temperature . The sections were incubated with primary antibodies , goat anti-Iba1 ( 3 μl/mL; Novus Biotechnology ) and Rabbit anti-GFAP ( 1:5000; Abcam ) , diluted in 10% normal donkey serum in PBS-T overnight at 4°C . After washing in PBS-T ( 3x5 min ) , the sections were incubated for 2 h at room temperature with secondary antibodies diluted in 10% normal donkey serum in PBS-T . For standard immunofluorescence , the secondary antibodies were donkey anti-goat Alexa Fluor 488 ( 1:300; Invitrogen ) and donkey anti-rabbit Rhodamine-Red-X ( 1:200; Jackson ImmunoResearch ) . The nuclei were stained with Hoecht’s . For infrared analysis , the secondary antibodies were donkey anti-goat IRDye 680RD ( 1:1500; Li-cor Biosciences ) and donkey anti-rabbit IRDye 800CW ( 1:1500; Li-cor Biosciences ) . The sections were subsequently washed in PBS-T ( 3x10 min ) , PBS ( 3x5 min ) , and water ( 2x5 minutes ) . For immunofluorescence , the sections were cover slipped with Fluoromount-G ( SouthernBiotechnology ) . For double-fluorescence labeling , primary rabbit anti-Zika Envelope ( E ) glycoprotein ( 1:400 , IBT Bioservices ) and mouse anti-alpha-Smooth Muscle Actin ( 1:200 Clone 1A4 , R&D Systems ) and secondary Alexa Fluor 488 conjugated goat anti-rabbit and Alexa Fluor 561 conjugated goat anti-mouse antibody were used . Rabbit IgG isotype ( 1:500; cat# MA5-16384 , ThermoFisher Scientific ) was used as an immunofluorescence staining control . The nuclei were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Images were captured on a Zeiss LSM 780 confocal system and processed with Zen 2011 confocal , Photoshop , or ImageJ software . Sections for infrared analysis were air-dried overnight . A Licor-Odyssey CLx ( Li-cor Biosciences ) scanned sections at 21 μm/pixel resolution . The average intensities of GFAP and Iba1 on each slide were obtained from fields-of-interest draw around each section with the Li-cor-Odyssey analysis software on at least two sections per slide and three slides per brain were scanned . Negative control staining , for which the primary antibodies were omitted , showed no detectable labeling in immunofluorescence or infrared imaging . Survival analysis was completed by Kaplan Meier estimate . A one-way ANOVA by Kruskal-Wallis test with Dunn’s multiple comparisons was used to analyze differences in viremia . An unpaired t-test was used to compare Iba1 and GFPA expression . SAS version 9 . 1 . 3 ( SAS Institute Inc . , Cary NC ) was used for all analyses .
Immunocompetent mice were treated with MAb-5A3 to block IFN-I signaling or PBS prior to and after challenge with 6 log10 PFU of ZIKV given by IP or SC injection . All of the PBS-treated mice challenged with ZIKV by either route survived and no apparent signs of disease , including weight loss were observed ( Fig 1A and 1B ) . Mice exposed to ZIKV by the IP route and treated with MAb-5A3 began to succumb or were euthanized on day 7 PI at which time the mice presented with ruffled fur , hunched posture , and were poorly responsive . On day 8 PI , a mouse exposed SC and another mouse exposed IP exhibited right-side , hind-limb paralysis and were euthanized . Mice treated with MAb-5A3 and exposed to ZIKV by the IP route continued to succumb or were euthanized through day 12 PI where 100% ( n = 10/10 ) mortality was observed . MAb-5A3-treated mice challenged with ZIKV by the SC route continued to succumb through day 19 PI and resulted in 40% ( n = 4/10 ) mortality . The mean times-to-death ( MTD ) for mice exposed IP was 9 . 7 days and for mice exposed SC was 14 . 75 days , which were significantly different ( P<0 . 0001 ) . Weight loss corresponded with survival for both challenge routes for mice treated with MAb-5A3 . Mice treated with MAb-5A3 and exposed to ZIKV IP or SC began to lose weight on day 4 PI and 6 PI , respectively . Weight loss continued for MAb-5A3treated mice exposed to ZIKV IP through day 12 PI when all mice had succumbed or were euthanized . MAb-5A3-treated mice exposed to ZIKV SC continued to lose weight until day 8 PI and then slowly began to regain weight and return to baseline values relative to day 0 PI by day 19 PI when mortality was no longer observed . However , we cannot determine if weight loss occurred only in mice succumbing from SC ZIKV infection because mice were weighed by group and not individually . These findings indicated that ZIKV can cause high mortality in mice with intact immune systems when IFN-I is blocked and that IP exposure was more lethal than SC exposure . The viral titers in the sera were determined on day 4 PI and when mice were euthanized ( Fig 1C and 1D ) . All mice treated with MAb-5A3 and exposed to ZIKV developed viremia , however these levels were slightly ( but not significantly ) higher on average ( 0 . 6 log10 PFUe/mL higher ) in IP exposed animals versus SC exposed animals . While all PBS-treated mice exposed to ZIKV by IP injection developed viremia , it was significantly ( p<0 . 0001 ) lower on average ( 3 . 6 log10 PFUe/mL lower ) compared to MAb-5A3-treated mice exposed to ZIKV IP . Most mice treated with PBS and exposed SC to ZIKV had viremia below the limit of detection for our assay ( 5/10 ) , however , one mouse had a titer of >5 log10 PFUe/ml . There was no significant difference in the viremia of mice treated with PBS and exposed SC vs . IP . In addition to analyzing day 4 viremia in all mice , we also measured the viremia in animals succumbing to infection . At the time of euthanasia , viremia was generally lower compared to levels observed on day 4 PI . At time of euthanasia or death , unperfused tissues were collected and viral titers in the liver , spleen , heart , kidney , brain , and spinal cord were measured by qRT-PCR ( Fig 2 ) . Virus was detected in all tissues collected in mice treated with MAb-5A3 and exposed IP and SC to ZIKV . The tissues were collected from moribund mice on different days PI , which does not warrant direct comparison of the results by statistics . The viral titers here represent the detection of viral RNA and not infectious virus , so we completed plaque assays on a subset of samples and confirmed the presence of infectious virus in the brain ( a key target tissue in this study ) where 3 . 9 or 3 . 8 PFU/g was detected in antibody treated mice exposed IP or SC to ZIKV , respectively . Collectively , these findings demonstrated that ZIKV infection in mice with IFN-I blockade results in viremia and tissue titers . However , these mice were not perfused so some of the virus detected in the tissues may be from the blood . We completed ISH and IFA coupled with histopathological analysis in tissues from ZIKV-infected mice that succumbed or were euthanized due to severe disease . Significant histopathological changes occurred in the CNS of all ZIKV-infected animals treated with the IFN-I blocking MAb-5A3 ( Fig 3 , S1 Table ) . The most notable microscopic lesions attributable to ZIKV infection in these mice included evidence of minimal to mild inflammation and necrosis in the brain and spinal cord of all animals that succumbed on days 7 and 8 , and to a lesser extent , in at least 5 of the 6 animals that succumbed between days 11 and 12 . The presence and /or extent of CNS lesions were difficult to ascertain in a few of the animals ( not included in the assessment ) due to artifactual damage to the tissue ( S1 Table ) and autolysis precluded the assessment of three animals that succumbed after day 12 PI and were removed from the histopathology results . Findings in the CNS included perivascular infiltrates of mononuclear cells ( “perivascular cuffing" ) and multifocal to diffuse gliosis with activated microglia ( Fig 3A ) . ZIKV RNA was frequently detected in the same regions by ISH ( Fig 3B ) . Perivascular cuffing was a more consistent finding in mice that succumbed earlier ( days 7–8 ) than in those that died or were euthanized later in the course of disease . In the sections examined , 5 of 5 animals from days 7–8 , and 2 of 5 animals from days 11–12 exhibited perivascular cuffing in the brain; 4 of 4 animals from days 7–8 , but none from days 11–12 exhibited perivascular cuffing in the spinal cord . In most animals , the perivascular cuffing was subtle , consisting of few mononuclear inflammatory cells; in 2 animals exposed to ZIKV IP that succumbed or were euthanized on day 7 PI , few neutrophils were admixed with the mononuclear cells . Microgliosis was a fairly consistent finding that was observed in the examined CNS sections of all animals from days 7 through 12 . The severity was mild to occasionally moderate in animals that succumbed on days 7–8; whereas , microgliosis was minimal to occasionally mild in animals that succumbed on days 11–12 ( Fig 3C ) . Additional findings in the CNS included multifocal areas of neuropil vacuolation ( edema ) and scattered necrotic cellular debris , most notably in the cerebrum ( Fig 3C ) , hippocampus ( Fig 4A ) and thalamus . Again , ZIKV RNA was detected by ISH in the same regions as these histopathological changes ( Figs 3D and 4B ) , which is in contrast to the uninfected control mice ( Figs 3E , 3F , 4E and 4F ) . Multifocal areas of edema occurred in 5/5 animals from days 7–8 , and in 2/5 animals from days 11–12 . Edema frequently surrounded blood vessels and was most prevalent in areas with the most abundant gliosis and neuronal necrosis . Scattered necrotic cellular debris often occurred adjacent to blood vessels and was observed in the brain of 5/5 animals from days 7–8 , and in 5/5 animals from days 11–12 . Although necrotic debris was often observed in multiple CNS sections , it was most frequently observed in the hippocampus , thalamus , cerebrum and less so in the cerebellum , pons and spinal cord . It is difficult to determine the cell type from which this necrotic debris originated—possibilities include neurons , resident glia , or infiltrating leukocytes . An additional notable finding in the CNS was neuronal necrosis , characterized by hypereosinophilic neurons and pyknosis , karyolysis , and replacement of neurons with necrotic debris . Neuronal necrosis occurred most frequently and extensively in the hippocampal pyramidal and granule layers and less frequently in the thalamus , cerebrum , and least frequently in the cerebellum , pons and spinal cord ( Fig 4C ) . Once again , RNA was consistently detected in corresponding regions of the brain by ISH ( Fig 4D ) . In the sections examined , neuronal necrosis was observed in the brain of 4/5 animals from days 7–8 and 3/5 animals from day 11–12 . Neuronal necrosis was also observed in the spinal cord of 4/4 animals examined from days 7–8 and 2/3 animals examined from day 11–12 . Less frequently there was neuronal degeneration and satellitosis . Another , although less consistent finding , was minimal neutrophilic infiltrates scattered within the brain parenchyma , which was observed in only 2/5 animals from days 7–8 , and 3/5 animals from days 11–12 . Histopathological analysis of the spinal cord showed evidence of one or more of the following: gliosis , neuronal satellitosis , and perivascular inflammatory infiltrates , in all mice observed ( Fig 5A ) . The detection of ZIKV RNA by ISH in the spinal cord suggests that these lesions are also due to infection ( Fig 5B and 5C ) . In general , cells in the spinal cord were only occasionally observed to be positive for ZIKV RNA by ISH; however , in some animals the spinal cord was more severely affected , and massive ZIKV infection was detected by ISH in some animals as depicted in Fig 5C . Additionally , a focally extensive area of necrosis affecting a spinal ganglion was observed in a mouse exposed to ZIKV IP that succumbed on day 7 PI ( Fig 5D ) . The noted pathologic changes and ISH findings in the spinal cord of ZIKV-infected mice is in contrast to what was observed in uninfected control mice ( Fig 5E and 5F ) . It has been known that both ionized calcium binding adaptor molecule 1 ( Iba1 ) expressed by microglia and glial fibrillary acidic protein ( GFAP ) expressed by astrocytes are upregulated in activated microglia and astrocytes , respectively , during neuroinflammation [48 , 49] . Infrared and immunofluorescent imaging was used to assess the effect of ZIKV on neuroinflammation by detecting the expression of Iba1 and GFAP in brain sections from 10 of the mice that succumbed to ZIKV infection ( brains from four of the mice were not analyzed due to artifactual damage ) . Compared to brains of control mice treated with IFNAR1-blocking MAb-5A3 , Iba1 and GFAP levels were significantly increased in the brains of ZIKV-infected mice ( Fig 6A and 6B ) and these findings were confirmed by immunofluorescent labeling of Iba1 and GFAP in these brains ( Fig 6C ) . Collectively , the results suggest that in our model , CNS infection by ZIKV results in significant neuroinflammation . Other significant findings outside of the CNS include necrotic and/or apoptotic cellular debris within the splenic lymphoid follicles ( white pulp ) interpreted as lymphocytolysis ( Fig 7A ) . This was a fairly consistent finding in these mice , with similar lesions being observed in all ( 8/8 ) spleens examined ( S1 Table ) . The severity of the lymphocytolysis varied from mild in the earliest stages ( 3/3 animals from day 7 ) to minimal in all subsequent animals . Additionally , several animals ( 2 from day 7 , 1 from day 8 and 1 from day 11 ) exhibited minimal lymphoid hyperplasia in the spleen , presumably in response to ZIKV infection . ZIKV RNA was also consistently detected by ISH in cells in the white pulp of the spleens from these animals ( Fig 7B ) , which is in contrast to the histopathology and ISH findings observed in uninfected control mice ( Fig 7C and 7D ) . No significant histopathological findings were observed in the liver , kidney , and heart of antibody treated , ZIKV-infected mice; however , ISH staining was observed in the smooth muscle cells within the tunica media of blood vessels in the kidney and heart , which was not observed in uninfected control mice ( S1 Fig ) . IFA confirmed the presence of ZIKV in the smooth muscle of blood vessels in the kidney , which was not observed in uninfected control mice or by isotype control antibody staining ( S1 Fig ) . The lack of concurrent microscopic evidence of tissue injury despite the presence of ZIKV in these mice suggests that , although virus is present in the smooth muscle , no direct damage to these cells is occurring; however , further studies are needed to fully elucidate the significance of ZIKV presence in smooth muscle of these blood vessels . Also , it is unclear why ZIKV appears to be present in the tunica media of vessels of these two organs but not in other organs examined . Additional findings of interest outside the CNS include the observation of degeneration , inflammation , and less consistently , regeneration , of skeletal muscles of the head and vertebral column . Myocyte degeneration , inflammation , and nuclear rowing were evident by hematoxylin and eosin staining ( S2 Fig ) . However , ZIKV was not detected in the skeletal muscle by ISH when the mice were moribund . Interestingly , ZIKV RNA was detected in a small subset of mice ( n = 5 ) from an additional independent study where the mice were euthanized on day 3 PI ( S2 Fig ) . ZIKV RNA was not detected by ISH in uninfected control mice where the histopathology appears normal ( S2 Fig ) . It appears that ZIKV RNA is present in the skeletal muscle before mice become moribund , but is then cleared at later time points and inflammation persists . Inflammation and degeneration was a fairly common finding , and was noted in at least one anatomic site from all 11 animals examined . Regeneration was observed less frequently , with minimal regeneration occurring in 8 animals . Although lesions were observed in skeletal muscles from multiple anatomic sites , no similar lesions were observed in cardiac muscle . The skeletal muscle observed in this study was limited to the head and vertebral column regions . Future investigations should include examination of the skeletal muscles from the rear limbs , particularly since hind limb paralysis was observed in two of the mice .
The unexpectedly frequent and severe clinical complications of ZIKV infection , including GBS and congenital ZIKV syndrome , have prompted intense research on host-virus interactions . Key to these efforts is the development of well-characterized animal models that recapitulate human disease . Adult wild-type mice infected with ZIKV can develop viremia in some instances [39 , 50] , but do not reflect the severe neurological disease seen in humans . However , transplacental and vaginal infection has been described in wild-type mice [51 , 52] . Knockout mice , deficient in type I or type II IFN responses , are permissive for viral replication in several organs including the brain [36–40]; however , infection in these mice does not provide an adequate means to test the efficacy of medical countermeasures or to study pathogenic events after infection . For example , in addition to its role in controlling viral infection through antiviral gene induction , type I IFN plays a role in priming of B and T cell responses [reviewed in [53 , 54] . Therefore , a more immunologically competent mouse model is essential for evaluating vaccines and treatments for ZIKV . The major advantage of this approach is that it allows immune responses to be elicited in immunologically competent mice with IFN I blockade only induced at the time of infection . We developed and characterized a partially immunocompetent murine model of ZIKV infection replicating the pathologic changes noted in genetically modified mice through antibody blockade of the type I IFN receptor . We are using this model to evaluate vaccines and therapeutics and as reported herein , have used the model to establish a baseline of ZIKV pathogenesis . As we were completing our pathogenesis study , another group reported a similar model . In these studies , a non-lethal model of ZIKV infection was described in wild-type mice injected IP with 1 mg or 2 mg of the same IFN-1 blocking antibody that we used and then exposed SC to 3 log10 focus-forming units of ZIKV strain H/PF/2013 , which is a human isolate from the 2013 French Polynesia outbreak [39] or strain Paraíba 2015 , an isolate from Brazil [55] . ZIKV replication was observed in several organs of mice treated with the antibody; however , the mice did not lose weight , succumb to infection , or develop neuropathology . The same group established an in utero transmission model of ZIKV infection using wild-type mice treated with this IFNAR-1 blocking monoclonal antibody [56] . In another report , IFN blockade by the same monoclonal antibody followed by SC infection with a higher dose of an African lineage strain of ZIKV ( Dakar 41519 ) that was derived from a brain homogenate via passage in Rag1-/- mice resulted in lethal disease [43] . Our mouse model results are similar to those in this report in that we found that antibody blockade of the type I IFN receptor recapitulates the severity of ZIKV disease observed in Ifnar1-/- mice . Further , we demonstrated that route of exposure is a factor in lethality in this mouse model in that mice exposed by the IP route began to succumb on day 7 PI and 100% mortality was observed by day 12 PI . In contrast , 40% mortality was observed when mice were exposed SC and they succumbed as late as day 19 PI . However , the disease was similar in mice that succumbed to ZIKV regardless of the route of exposure where significant pathologic changes were observed in the CNS . Since vaccination studies requiring boosting would utilize older mice , we confirmed that lethality is observed in mice that are 10 weeks old where 80–100% mortality was observed in mice exposed to 6 log10 PFU IP of ZIKV strain DAK AR D 41525 used in this study . These results are being submitted in a separate publication that also evaluates the susceptibility of this mouse model to multiple ZIKV strains . Zhao et al . report higher lethality by SC infection which may be due in part to passaging their virus in mice [43] . Our results and those described by Zhao et al . and Lazear et al . suggest that the virus strain ( African vs . Asian lineage ) , passage history , and exposure route can affect susceptibility to infection . More studies are needed to evaluate the pathogenesis of African vs . Asian lineage strains ( and the effect of passage history ) in the various infection models . Mortality is not a common feature of human infection with ZIKV , which is generally asymptomatic and self-limiting in most individuals . However , severe disease in humans is characterized by neurological complications associated with ZIKV infection . In adults , reported neurological complications include GBS [24 , 25] or in a few cases , encephalopathy [57] , meningoencephalitis [58] , and acute myelitis [59] have been described . In the fetus , intrauterine infection can cause congenital abnormalities to include severe fetal brain injury [22] . The neuropathology that we observed in our studies offers a model for dissecting the pathological consequences of ZIKV infection . We observed significant pathologic changes in the CNS of all mice that succumbed to ZIKV infection . Overall , the CNS lesions in these mice represent an acute to subacute encephalitis/encephalomyelitis that is characterized by neuronal death , astrogliosis , microgliosis , scattered necrotic cellular debris , and a minimal to mild mononuclear ( and less frequently neutrophilic ) inflammatory cell infiltrate . In the brain , lesions were most evident in the hippocampus , particularly affecting the pyramidal and granule cell layers , followed by thalamus and cerebrum , and less often affecting the cerebellum and pons . The presence of ZIKV RNA as detected via ISH suggests these lesions are attributable to ZIKV infection . It is likely that encephalitis/encephalomyelitis contributed to the morbidity and mortality in these animals , particularly those that succumbed early , between days 7 to 11 PI . Lesions , particularly neuronal necrosis and inflammation , appear to be less severe in animals from day 12; however , it is possible that CNS injury played a role in the deaths of these animals as well . The neurotropism of ZIKV was demonstrated in early studies where neonatal mice intracerebrally infected with ZIKV showed evidence of nuclear fragmentation , perivascular cuffing , and degenerative cells in the hippocampus of the brain [3 , 28] . Bell et al . also observed enlarged astrocytes with extended processes and containing cytoplasmic virus factories throughout the cortex of ZIKV-infected mouse brains [28] . More recent studies characterizing ZIKV murine models in immunodeficient mice also showed that microscopic lesions resulting from ZIKV infection were found primarily in the brain [37 , 38 , 40] . Our findings are most consistent with those reported by Dowall et al . [38] where inflammatory and degenerative changes were observed in the brains of A129 mice challenged with ZIKV . The CNS lesions observed in ZIKV-infected mice may be relevant for brain-related pathologies in some ZIKV-infected humans . However , the histopathology of ZIKV-associated microcephaly has been limited to only a few reports thus far ( reviewed in [60] ) . The major findings have mostly been in the brain and include diffuse grey and white matter involvement consisting of dystrophic calcifications , gliosis , microglial nodules , neuronophagia , and scattered lymphocytes [60] . Astrocyte pathology was observed in post-mortem analysis of a neonatal brain with microcephaly associated with ZIKV infection where diffuse astrogliosis was apparent with focal astrocytic outburst into the subarachnoid space . Activated microglial cells were also found to be present throughout most of the cerebral gray and white matter [22] . Outside of the CNS , the most consistent histopathological lesions were observed in the spleen . We observed lymphocytolysis in the splenic white pulp which correspond to areas with ZIKV ISH signal . Dowall et al . also noted similar lesions in the spleen although detection of viral RNA was not described [38] . Other histopathological observations indicated a significant inflammatory response resulting from ZIKV infection . Lymphoid hyperplasia and myeloid ( granulocytic ) hyperplasia were noted and are indicative of an inflammatory response to antigenic stimulation and an increased demand for leukocytes . Although these are both somewhat non-specific findings , in these cases they most likely represent a systemic immune reaction to ZIKV infection and are related to the significant neuroinflammation noted in the animals . A previous study indicated that ZIKV infection in AG129 mice led to a systemic inflammatory response , where multiple pro-inflammatory cytokines were found to be increased in the sera [40] . It is unknown how this relates to human disease since the cytokine response during the acute phase of ZIKV infection in humans has not been studied . Inflammation was also observed in the skeletal muscles of the head and vertebral column . Skeletal muscle degeneration and inflammation observed in these animals are thought to be attributable to ZIKV infection since direct infection of the myocytes was observed on day 3 PI . ZIKV is presumably cleared following day 3 PI , but inflammation persists in the skeletal muscle . Interestingly , Ailota et al . described similar pathologic changes in the musculature from the posterior rear limb of a ZIKV-infected mouse where multi-focal myofiber degeneration and necrosis with inflammatory cell infiltration , nuclear rowing , and attempted regeneration were observed . Further investigation into whether the skeletal muscle inflammation is viral or immune mediated is warranted . In summary , we described a murine model for ZIKV that mimics the severe neurological disease previously described in mice deficient in the type I or II IFN response [37 , 38 , 40] . Our detailed description of the ZIKV-associated pathology in this model , much of which mirrors what is known about neuropathogenesis in humans , will provide a baseline for evaluating medical countermeasures to prevent or treat ZIKV infections .
|
Research addressing the severe clinical complications associated with ZIKV infection , including GBS and congenital ZIKV syndrome , are urgently needed . Key to this effort is development of well-characterized animal models that recapitulate human disease . Adult wild-type mice infected with ZIKV can develop viremia in some instances , but they do not emulate the disease associated with the severe congenital and adult neuropathology . Several groups have recently described type I or type II IFN-deficient murine models that are permissive for viral replication in several organs including the brain . The major limitation of these models is they utilize immunodeficient knockout mice lacking key components of the innate antiviral response . We describe the use of a lethal murine model for ZIKV where the innate response of immunocompetent mice is suppressed only at the time of infection . We show that the mice develop severe neurological disease similar to that previously demonstrated in mice deficient in the type I or II IFN response . Using this model , we provide a detailed description of the ZIKV-associated pathologic changes , which mirrors the neuropathogenic properties of ZIKV in humans . These studies provide a baseline for assessment of medical countermeasures that can prevent or treat such pathogenic effects caused by ZIKV infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"group-specific",
"staining",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"hematoxylin",
"staining",
"nervous",
"system",
"pathogens",
"skeletal",
"muscles",
"immunology",
"microbiology",
"neuroscience",
"animal",
"models",
"viruses",
"model",
"organisms",
"rna",
"viruses",
"signs",
"and",
"symptoms",
"experimental",
"organism",
"systems",
"veterinary",
"science",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"spinal",
"cord",
"musculoskeletal",
"system",
"inflammation",
"medical",
"microbiology",
"microbial",
"pathogens",
"mouse",
"models",
"muscles",
"immune",
"response",
"veterinary",
"pathology",
"diagnostic",
"medicine",
"neuroanatomy",
"anatomy",
"flaviviruses",
"central",
"nervous",
"system",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"organisms",
"zika",
"virus"
] |
2017
|
Neuropathogenesis of Zika Virus in a Highly Susceptible Immunocompetent Mouse Model after Antibody Blockade of Type I Interferon
|
Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology . However , the majority of existing studies have focussed on emergence at the population level , and not within a host . In particular , the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish . Here , we extend previous work for measuring emergence probabilities in non-equilibrium populations , to within-host models of acute infections . We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term . We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured . Further analysis of the model shows that , in the short-term , mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate ( ‘immune tolerance’ ) , as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition . We end by discussing the model in relation to within-host evolution of human pathogens ( including HIV , hepatitis C virus , and cancer ) , and how ongoing immune growth can affect their evolutionary dynamics .
Parasites and pathogens pose a continuous threat to human , livestock , and plant health since new strains can readily emerge , via mutation or recombination , from pre-existing strains . Generally , the focus has been on detection of emerging diseases at the population level , in order to track and control their spread [1 , 2] . Modelling approaches to predicting emergence have therefore primarily concentrated on detecting infections arising between individual hosts [3 , 4] , and the contribution of within-host processes to pathogen emergence has often been overlooked . It is now well known that within-host evolution has strong effects on the epidemiology of many pathogens ( reviewed in [5] ) , and can substantially affect the course of an infection , as illustrated by the cases of HIV [6] and hepatits C virus ( HCV ) [7] . Each step of the within-host evolutionary dynamics consists of the emergence of a rare mutant that takes over the pathogen population . Since mutated infections always initially appear as a few copies , they are prone to extinction so their emergence is best captured using stochastic dynamics ( as opposed to deterministic approaches ) . Only a handful of previous models have investigated this stochastic within-host process . A widespread use of within-host models in static populations is to calculate the probability that infections will evolve drug resistance , and what regime is needed to avoid treatment failure [8–11] . One exception [12] studied viral emergence within a host , if most mutations were deleterious , in order to determine how different viral replication mechanisms affected the establishment of beneficial alleles . Parasite and pathogen evolution can radically affect the course of infections in hosts able to mount immune responses . For instance , HIV is known to successfully fix mutations within individuals which enable it to evade immune pressures [6] . This is in line with evidence that target cell limitation cannot account for HIV dynamics , and that immune limitation also needs to be present [13] . Arguably , this continual evasion of immunity prompts the chronic nature of HIV infections ( see [14 , 15] for an illustration of these ‘Red Queen’ dynamics ) . In the case of HCV , it has also been found that chronic infections were associated with higher rates of within-host evolution [16] . Concerning a different chronic disease , it is now well-known that the ability of malignant cells to rapidly expand in size , ignoring biological signals to arrest growth ( e . g . through mutating the p53 tumour suppression gene ) , and escaping the patient’s immune system are key steps in cancer development [17] . All these scenarios can be analysed in the larger framework of evolutionary rescue [18 , 19] , where a change in the environment ( in this case , the activation of an immune response ) will cause the population to go extinct unless it evolves ( develops an increased replicative ability , then subsequently rise to a large enough size to avoid stochastic loss ) . Although chronic infections are those most likely to evolve strains that evade immune pressures , evidence exists that such immune escape also plays a role in acute infections , like influenza [20] . Note that the latter evidence arises from serial passage experiments; although these tend to maximise selection at the within-host level , they still include a slight selective pressure for increased transmission . Modelling mutant emergence in an immune evasion context raises a technical challenge , as it is a non-equilibrium process . In most of the within-host models presented above [8–10 , 12] , it is sufficient to know the initial state of the system to calculate emergence probability of immune or treatment escape . A non-equilibrium model was studied by Alexander and Bonhoeffer [11] , who accounted for the reduction in available target cells when determining the emergence of drug resistance using an evolutionary rescue framework . With immune-escape however , the problem is that the immune state when the mutant infection appears is only temporary . In this case , there will be an initial strain present within the host , which triggers immunity . This strain can then mutate into a faster-replicating form , but if the mutated strain arises at a low frequency , immunity can destroy it before it has a chance to spread . This effect can be exacerbated by the fact that the mutated strain also prompts an increased immune response , so the emerging infection has a stronger defence to initially compete with ( assuming immune growth is proportional to the total size of the pathogen population ) . This feedback , where increased immune growth prevents emergence of mutated strains with higher replication rates , can strongly affect the appearance of mutated strains within-host , and needs to be accounted for . Existing emergence models have not yet accounted for such within-host population feedbacks , especially those arising from the immune system . Recently , Hartfield and Alizon [21] tackled a related problem , regarding how epidemiological feedbacks affect disease emergence at the host population level . In their model , a faster-replicating strain emerged via mutation from a pre-existing infection; however , the continuing outbreak caused by the initial strain removed susceptible individuals from the population , which limited the initial spread of the mutated strain . It was shown that the ongoing depletion of susceptible individuals due to the initial strain spreading has a stronger effect on reducing pathogen emergence , than assumed by just scaling down the reproductive ratio by the frequency of susceptible individuals present when the mutated infection appears . That is , the feedback produced by the first strain in removing susceptible individuals caused a drastic decrease in the emergence probability of the mutated strain . Building on this previous study , we derive here an analytical approximation for the probability that an immune-escape mutant will emerge and maintain itself within a host . We use ‘immune-escape’ in the sense that while the mutated strain can be killed by immunity , it can ultimately outgrow immune growth and chronically persist . Furthermore , we use an acute infection setting , which are commonly used to study the within-host dynamics of ‘flu-like’ diseases [22–28] . Analysis of the model demonstrates how the ongoing proliferation of immune cells acts to decrease the emergence probability of mutated strains . Acute infection models are also useful in studying the first stages of chronic infections , where one observes exponential growth of the virus population , followed by a decline in the first weeks of infection . In addition , there are two questions that are worth investigating with this model from a biological standpoint . First , what is the fittest evolutionary strategy for an escape mutant: is it to overgrow the immune response ( that is , increase its inherent replication rate to enable its persistence , even when immune cells are at capacity ) , or to tolerate it ( prevent immunity from killing as many pathogens per immune cell ) ? Both these actions will lead to an increase in the pathogen’s net reproduction rate and can thus be described as immune evasion , but it is unclear if one process is favoured over the other . In addition , note that disabling the immune response is not possible , since the wild-type infection activates it . Second , to what extent do we need to account for the ongoing proliferation of the immune response ? In other words , if we calculate the emergence probability based on the system state when the mutation occurs , how inaccurate would this estimate be ? We end by discussing our results in the light of what is known about within-host evolution for several human infections .
In order to find an analytical solution for the within-host emergence of a mutated strain , we follow the approach of Hartfield and Alizon [21] , which investigated the appearance of a new infectious pathogen from a pre-existing strain at the population level . To consider the within-host case , we construct and subsequently analyse a specific scenario of acute , immunising infections . Here , the first strain will go extinct because it does not replicate at a high enough rate . However , before vanishing , it can mutate into a form that grows unboundedly; we are interested in calculating the probability of this event occurring . We focus on this case to cover a general range of within-host evolution scenarios , which is important since there is yet no consensus on how to best model within-host infections ( reviewed in [29] ) . Although it is feasible that the mutated strain has a maximum population size , mathematical analysis of pathogen emergence only needs to consider the dynamics of mutants when they are present in a few copies , rather than the long-term behaviour once they have already established . Therefore , the general results outlined in this paper are also broadly applicable to cases where the infection population sizes are bounded . Our analytical approach involves using a set of deterministic differential equations to ascertain pathogen spread in a stochastic birth-death process , where an infection ( or immune cell ) can only either die or produce 1 offspring . This process is one of the most common ways of investigating stochastic disease spread [30] . A list of nomenclature used in the model is outlined in Table 1 . Assume there exists an initial pathogen , or infected cell-line , the size of which at time t is denoted x1 ( t ) . This line grows in size over time according to the following equation , which is well-used for within-host infection models [29]: d x 1 d t = x 1 ( φ 1 - σ 1 y ) ( 1 ) Here , φ1 is the growth rate of the infection , σ1 is the rate of destruction of the pathogen per immune cell , and y ( t ) is the number of immune cells ( i . e . lymphocytes ) . For simplicity , we assume that there is complete cross-immunity between the various pathogen strains , so it is not necessary to model immune cell diversity . The growth of the immune cell population is modelled using a logistic-growth curve: d y d t = r x 1 y 1 - y K ( 2 ) Here , r is the proliferation rate of immune cells , and K is the maximum population size they can achieve . This formulation is an extension of the model developed by Gilchrist and Sasaki [24] to study acute infections . The main difference in that in their model , the density of immune cells is allowed to reach any value in order to clear the infection . Here , we impose that immune density does not go above a maximum threshold K , which correspond to an intrinsic limitation in the host resources allocated to immunity . Profile plots of typical infection responses are shown in Section 1 of S1 Text . If φ1/σ1 < K , then the first strain will increase in size until the immune-cells reach a maximum . After this point , the infection will decrease towards eventual extinction , while the immune response will be maintained at a non-zero size . However , if φ1/σ1 ≥ K then the first strain will continue to expand . A formal rational for this behaviour will be shown below . To proceed with finding an analytical solution for the emergence probability , we proceed as in previous analyses [21 , 24 , 31] , and note that since y is monotonically increasing , we can use the immune cell population size as a surrogate measure of time . By dividing Equation 1 by Equation 2 , we obtain a differential equation for x1 as a function of y: d x 1 d y = ( φ 1 - σ 1 y ) x 1 r x 1 y ( 1 - y K ) ( 3 ) To simplify subsequent analyses , we make the following substitutions . We define the reproductive rate of the infection , where there is a single immune cell ( y = 1 ) equals R1 = φ1/σ1 . We also set ρ = r/σ1 ( this can be formally shown by rescaling time by τ = σ1t ) . We use the notation R1 to draw parallels between the scaled pathogenic replication rate , and the reproductive ratio R0 in population-level , epidemiological models [32] . After making the required substitutions , Equation 3 can be rewritten as: d x 1 d y = K ( R 1 - y ) ρ y ( K - y ) ( 4 ) Equation 4 formally shows that the infected cell line increases in size ( dx1/dy > 0 ) if y < R1 = φ1/σ1 , and decreases if y > R1 . A corollary of this result is that if R1 of a infection exceeds K , then it cannot go extinct in the long term . This differential equation is straightforward to solve ( Section 1 of S1 Text ) , and yields the following function for x1 ( y ) : x 1 ( y ) = x i n i t + 1 ρ log y y i n i t R 1 K - y K - y i n i t ( K - R 1 ) ( 5 ) where xinit and yinit are , respectively , the number of pathogen and immune cells at the start of the process ( time t = 0 ) , and log is the natural logarithm . It is clear from Equation 4 that the maximum value of x1 occurs for y = R1 . By substituting this value into Equation 5 , we obtain the maximum value of x1 ( denoted xM ) as: x M = x i n i t + 1 ρ log R 1 y i n i t R 1 K - R 1 K - y i n i t ( K - R 1 ) ( 6 ) Note that ρ has no effect on the position of the peak ( that is , the value of y leading to the maximal infection level ) . Since it affects the growth rate of the immune response ( and therefore also of the pathogen ) , it does determine the maximum value itself . As this maximum is inversely proportional to ρ , smaller immune growth rates lead to larger peaks . Finally , the maximum infection time ( as a function of y ) needed for the first infection to go extinct can be determined by solving x1 ( y ) = 0 numerically ( Section 1 of S1 Text ) . Our goal is to calculate the probability of ‘evolutionary rescue’ . That is , the initial strain has R1 < K so is guaranteed to go extinct in the long-term . However , a mutated form ( with reproductive ratio R2 ) could arise with R2 > K , and if it does not go extinct when rare , it can outgrow immune proliferation . Previous theory on the emergence of novel pathogenic strains [21] showed that if mutated strains arise at rate μ per time step , the overall emergence probability P is given by: P = 1 - exp - μ ∫ y i n i t y M x 1 ( y ) Π ( y ) d y ( 7 ) for yM the maximum immune size for which it is possible for immune escape to arise , and Π ( y ) the emergence probability of an escape mutation were it to appear . The formulation of each of these will be discussed in turn . We verified our analytical solution by comparing it to simulation data . Simulations were written in C and based on the Gillespie Algorithm with tau-leaping [35 , 36]; source code has been deposited online in the Dryad data depository ( doi:10 . 5061/dryad . df1vk ) . The time step was set to be very low: Δτ = 0 . 00005 . This is because the tau-leaping algorithm is accurate only if the expected number of events per time step is small [37]; since the growth rates of the pathogen strains and the lymphocytes are both large , a small time step is needed to make the simulation valid . The growth of both the original and mutated strains are simulated using scaled parameter rates . That is , the birth rate per time step for each pathogen is Poisson-distributed with mean ( Ri ⋅ xi ⋅ Δτ ) , and death rate with mean ( xi ⋅ y ⋅ Δτ ) for i = 1 , 2 . This is done to reduce the number of parameters in the model , and also enables accurate comparison with the scaled results . The change in size of the immune response is determined by standard logistic-growth dynamics for the Gillespie algorithm . That is , the Poisson mean number of births per time step equaled ρb ( x1 + x2 ) y , for ρb is the birth rate parameter , and the mean number of deaths equalled y ( x1 + x2 ) ( ρd + ρ ( y/K ) ) , where ρd is the death rate and ρ = ρb − ρd . Since there are no distinct ρb and ρd terms in the model ( just ρ ) , then we set ρd = 1 and varied ρb , so ρ = ρb − 1 . The net growth rate ρ was varied , generally between 0 . 5 and 19 depending on the size of the other parameters . Note that in the analytical solution , it is assumed that the immune response does not die off . In order to maintain this assumption , we set yinit = 20 in the simulations . This also makes intuitive sense , because it is unlikely that the initial immune response is limited to just one cell . If the immune response goes extinct before both infected strains go extinct , or the mutated strain emerges , then that run is discarded , the simulation is reset and restarted . In simulations , R1 is either set to 60 if K = 100 , R1 = 100 if K = 250 or 1000 , or R1 = 2 , 000 for K = 10 , 000 . R2 was varied , ensuring that R2 > K so the infected strain can outgrow the immune response ( see Equation 4 ) . The mutation rate was also varied over several orders of magnitude . The first strain is reintroduced ( from an initial frequency of 1 cell ) 10 , 000 , 000 times as separate replication runs . Since the emergence probabilities were predicted to be low , a large number of runs were needed in order to produced a meaningful estimate of emergence probability . The second strain is said to have emerged once it exceeded 20 cells; since meaningful values of R2 were large , only a handful of cells would have been needed to guarantee emergence , hence a low outbreak threshold could be used [38] .
The first results we checked were individual profiles of the stochastic simulations , since these can be used to demonstrate the behaviour of within-host emergence . In the majority of cases , the first strain increase in size , but once immune proliferation also reaches its maximum then the first strain goes extinct soon after ( Fig . 2 ( a ) ) . However , emergence of the mutated strain can occur even if the immune cells are at their maximum size ( Fig . 2 ( b ) ) . This is to be expected , given the form of Π ( Equation 10 ) , which demonstrates that emergence is likeliest once the immune cells reach a steady-state , so ongoing proliferation does not restrict their establishment . Conversely , emergence probability is reduced if the immune response is spreading; this is because when the mutated infection is rare , it is less likely to reproduce as immune cells increase in number . Since the mutated infection population is only present in a few copies , this negative effect this immune growth has on reproductive ability will be drastic ( see also equivalent population genetics results by Otto and Whitlock [39] ) . Fig . 3 compares the full analytical solution ( Equation 7 , with Π given by Equation 10 ) against simulation data . If K = 100 , R1 = 60 , we see that there is an accurate overlap between the two for a variety of mutation rates that span several orders of magnitude ( Fig . 3 ( a ) and ( b ) ) . This match demonstrates how our analytical solutions can be used to accurately predict emergence probabilities in the face of different scenarios , including antibiotic resistance and tumour formation , as both these processes are characterised by high mutation rates . We also tested a parameter set where the carrying capacity and initial growth rate was much higher ( K = 1 , 000 and R1 = 100 , or K = 10 , 000 and R1 = 2 , 000 ) . Fig . 3 ( c ) - ( f ) demonstrates that the analytical results slightly underestimate the simulation results to a small degree , especially if the mutated strain’s growth rate is high and R2 is close to K , but becomes more accurate as R2 increases and generally provides a good approximation . These inaccuracies probably arise due to our analytical solution not fully accounting for the increased variance in both infection and immune growth rates that can arise if parameters are large , as in this model [30] . However , the error does not appear to be great , so the model can still be used to provide accurate estimates of emergence probability for this parameter set . Finally , we also tested how well analytical solutions work for cases where R1 < R2 < K . Although such a mutated strain replicates more quickly , Equation 4 shows that it will die out in the long-term . Hence our analytical solutions might not correctly reflect the emergence probability of these infections . Nevertheless , even in this case , Equation 7 accurately matches up with simulation results in this parameter range , although some inaccuracies arise for K = 1 , 000 ( S1 Fig ) . To exemplify why it is important to account for ongoing immune growth , we compared our full solution for the emergence probability ( Equation 7 , with Π equal to Equation 10 ) , to a ‘naive’ estimate that does not assume ongoing immune proliferation ( Π = 1 − 1/R2 ) . We see that our result leads to a greatly reduced emergence probability , with values from our model being ∼1 , 000 times lower than the naive estimate ( Fig . 4 , and Section 3 of S1 Text ) . Similar results apply if feedbacks only affect the mutant after it has arose ( that is , Π = ( 1 − P e x t * ) as given by Equation 9 ) . Hence , as in previous non-equilibrium models [21] , one needs to account for dynamical feedbacks , both before and after the mutated strain appears , in order to account for the reduced emergence probability in these scenarios . We next studied what process has a larger effect on pathogen emergence . Immune escape can either be achieved by overgrowing the immune response ( increasing the intrinsic pathogen growth rate φ ) , or by tolerating it ( reducing the immune-mediated death rate σ ) . One might expect that the two processes would lead to similar increase in escape probability , since both affect the effective reproductive rate R* . However , this intuition need not hold in the face of immune-mediated feedbacks . We commence with a heuristic analysis based on the deterministic model to predict general behaviour for a newly-emerging strain , then use numerical analyses to check this reasoning . If there are two strains spreading concurrently , the deterministic rate of change of immunity , y , and the second strain x2 , is given by the following set of differential equations: d x 2 d t = x 2 ( φ 2 - σ 2 y ) ( 11a ) d y d t = r ( x 1 + x 2 ) y 1 - y K ( 11b ) Further recall that that the basic pathogen growth rate with only one immune cell is R2 = φ2/σ2 . In order to augment its spread , the infection can either increase its intrinsic growth rate by a certain factor ( i . e . change φ2 → φ2 c , where c > 1 is a numerical constant ) , or instead tolerate the immune response ( mathematically equivalent to the transformation σ2 → σ2/c ) . We can investigate the effect of both these rescaled variables on the rate of change of pathogen spread by substituting them into Equation 4 . After making the substitution φ2 → φ2 c , the pathogen rate of change becomes: d x 2 d y = K ( c R 2 - y ) ρ y ( K - y ) x 2 x 1 + x 2 σ 2 σ 1 ( 12 ) The x2/ ( x1 + x2 ) term arises due to the presence of the pre-existing initial strain x1 , and the death-rate ratio σ2/σ1 appears since ρ was initially scaled by σ1; this term disappears if we assume equal death rates . Apart from these terms , Equation 12 is conceptually the same as Equation 4 , but with R scaled by a factor c , as expected . However , if we instead scale σ2 → σ2 / c , a different result emerges: d x 2 d y = K ( c R 2 - y ) c ρ y ( K - y ) x 2 x 1 + x 2 σ 2 σ 1 ( 13 ) Here , not only is the reproductive ratio R2 scaled , but the immune growth term ρ is also altered . Mathematically , this is a simple consequence of the fact that the growth rate is scaled by 1/σ1 , so any rescaling of σ2 also affects ρ through its effect on the σ2/σ1 ratio . Biologically , this outcome reflects the fact that a rescaling of immune-mediated death would not have the same effect on the growth rate than changing φ2 by the same ratio , since pathogen death is also a function of the immune population , y ( see Equation 1 ) . So although tolerating immunity might increase the infection growth rate , it comes at a cost of increasing the effective growth rate of immunity , as each immune cell would have a larger average impact on pathogen death . Therefore , while a rise in growth rate ( φ2 ) would only affect R2 , reducing the death rate ( σ2 ) comes at a cost of increasing the effective proliferation rate of immune cells . Hence , one expects that it is more advantageous for an infection to increase φ2 and outgrow the immune response , instead of reducing σ2 and tolerating it . Furthermore , note that the emergence probability Π in the presence of epidemiological feedbacks contains a term of order 1/ρ ( as with dx2/dy ) , so the effective rise in immunity will further lead to a negative overall effect on emergence probability . We verified this intuition by comparing the analytical results for cases where φ2 is increased by a set factor ( so that only R2 is changed by this rescaling ) , to outcomes where σ2 is scaled ( which will not just affect R2 but will also increase ρ by the same factor , as outlined above ) . Fig . 5 ( a ) demonstrates how , for large parameter values , increasing φ2 only produces a higher overall emergence probability . Qualitatively similar results arise if using different parameter values .
Our model can also be used to shed light on different processes of within-host emergence that have been observed in clinical studies . Different diseases show varying outcomes with regards to the production of immune-escape mutations . Two extensively-studied human diseases , HIV and HCV , are both characterised by a successive emergence of new strains over time . In particular , HIV is well-known to produce ‘escape mutations’ that evade T cell-mediated immunity [43 , 44] . On a within-host phylogeny , this behaviour is characterised by creation of new subclades [5–7] . This behaviour can be intuitively explained by noting that HIV has extremely high mutation rates , estimated at ∼0 . 2 errors made per replication cycle and the ability to produce 1010 – 1012 virions per day [6] ( although mutation creation might be limited by a lower Ne , which has been estimated to lie between 1 , 000 [50] and 100 , 000 [51] ) . Furthermore , a sizeable proportion of mutations are beneficial , with estimates of adaptive substitutions in the env gene placed at ∼55% [52] ( although the actual proportion of spontaneous beneficial mutations will be less than this , as observed from mutagenesis studies with viruses [53 , 54] ) . What may seem surprising is that given this evolutionary potential , we do not see a more pronounced increase in HIV replication rate throughout the course of an infection . Yet , conversely , immune escape is very strongly selected for [14] . One possibility could be that given the constrains on RNA virus genomes [55] , evading the immune response might be easier to achieve than increasing the replication rate . Furthermore , if the maximum immunity population size is high , it could be too complicated mechanistically to outgrow it , rather than than simply evading it . HCV also shows a similar propensity to produce immune-escape mutations , with an estimated substitution rate of 1 . 2×10−4 per replication cycle and 1012 virions generated each day [7] ( although , as with HIV , the effective population size is greatly lower than this value [7] ) . Acute hepatitis C infections are characterised by little diversity accumulating over time , except in one specific genetic region ( NS5B; [56] ) . Chronic infections accumulate much more diversity , in line with the hypothesis that they continuously evolve to evade the immune system [16] . These infections are also characterised by different immune profiles: acute infections lead to a high , sustained immune response , which tends to be greatly lowered in chronic infections [57] . Our model suggests that if the immune response naturally increased rapidly ( i . e . a higher intrinsic ρ in the model ) , it can greatly limit the emergence of new pathogens as it rises , preventing immune escape and a chronic illness . There exists evidence that mutations in hosts correlate with infection outcome , mainly due to SNPs at the IL28B loci , which could cause this effect [58–60] . However , it remains controversial as to what determines virus clearance , especially since there exist evidence for virus control over infection outcome [61] . Therefore , the effect on immune response on the creation of escape mutations requires further study . Other diseases are characterised by low probability of emergence despite frequent mutation , for which immune feedbacks could be the cause . Cancer growth is characterised by evading pre-programmed cell death and extremely rapid cell replication [17] . Furthermore , genomes present in cancer cells usually carry ‘mutator’ alleles , causing additional cell instability [62] . Therefore , cell mutation can be extremely common , but can generally be stopped by the immune response . Yet it is known that tumours can mutate immune-checkpoint networks to generate protection from the immune system . One prominent example is the up-regulation of ligands for the programmed cell death protein 1 ( PD1 ) pathway , which can block antitumour immune responses [63] . Our model suggests that due to the rapid replication of tumour cells , they could also strongly trigger a heightened immune rate , the increased spread of which will greatly prevent cancer emergence . This mechanism could explain why tumour emergence is rare relative to the potential mutation rate . By accounting for the ongoing spread of immunity , we have quantified how this particular effect can feedback onto emergence of mutated pathogens intra-host , and inhibit the appearance of mutated strains . Further analysis of the model demonstrates how it is beneficial for a pathogen to increase its replication rate and attempt to outgrow immunity , as opposed to tolerate it . This model can therefore shed light on expected within-host evolutionary dynamics of infections , as well as determine why extremely rapidly replicating pathogens do not emerge as often as expected .
|
The ongoing evolution of infectious diseases provides a constant health threat . This evolution can either result in the production of new pathogens , or new strains of existing pathogens that escape prevailing drug treatments or immune responses . The latter process , also known as immune escape , is a predominant reason for the persistence of several viruses , including HIV and hepatitis C virus ( HCV ) , in their human host . As a consequence , the within-host emergence of new strains has been the intense focus of modelling studies . However , existing models have neglected important feedbacks that affects this emergence probability . Specifically , once a mutated pathogen arises that spreads more quickly than the initial ( resident ) strain , it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads . Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection . Analysis of this model suggests that , in order to enlarge its emergence probability , it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate . Our model can be readily applied to existing within-host data , as demonstrated with application to HIV , HCV , and cancer dynamics .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Within-Host Stochastic Emergence Dynamics of Immune-Escape Mutants
|
The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses ( phenotypes ) . Unfortunately , this information is distributed throughout the unstructured text of over 23 million articles . The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions . Extracting these relationships from natural language sentences on such a large scale , however , requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas . Here we describe a novel algorithm , Ensemble Biclustering for Classification ( EBC ) , that learns the structure of biomedical relationships automatically from text , overcoming differences in word choice and sentence structure . We validate EBC's performance against manually-curated sets of ( 1 ) pharmacogenomic relationships from PharmGKB and ( 2 ) drug-target relationships from DrugBank , and use it to discover new drug-gene relationships for both knowledge bases . We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline , revealing unexpected structure that challenges current notions about how these relationships are expressed in text . For instance , we learn that newer experimental findings are described in consistently different ways than established knowledge , and that seemingly pure classes of relationships can exhibit interesting chimeric structure . The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining .
Biomedical research generates text at an incredible rate . Each year , several hundred thousand new articles enter Medline from over 5 , 500 unique journals [1 , 2] . The literature’s rapid growth and the rise of interdisciplinary domains like bioinformatics and systems biology are changing how the scientific community interacts with this important resource . Knowledge bases like OMIM [3] , DrugBank [4] and PharmGKB [5] manually curate and restructure information from the literature to increase its accessibility to researchers and clinicians . These knowledge bases capture cross-sectional “slices” of the literature , drawing connections among facts reported in different journals , at different times , and in different research domains . Often , they examine the literature in ways not easily captured by current indexing strategies , such as MeSH terms or key words . As the literature grows and the information we need to extract increases in complexity , full manual curation of these knowledge bases is rapidly becoming infeasible . Progress in natural language processing ( NLP ) has encouraged the development of automated and semi-automated methods for enabling more efficient curation of biomedical text [6–9] , especially as biomedical research begins to explore even larger text-based resources , such as electronic medical records ( EMRs ) [10 , 11] . However , tasks that are simple for human readers , such as recognizing when two different-looking statements mean the same thing , or when one statement is a more general version of another statement , are often extremely challenging for NLP algorithms . One way around this problem is to infer the meaning of words and phrases by examining their usage patterns in large , unlabeled text corpora , an approach called “distributional semantics” [12–14] . If two words or phrases are used in similar contexts , they are likely to be semantically related . Here we introduce a novel algorithm , called Ensemble Biclustering for Classification ( EBC ) , that applies this strategy to uncover relationships between biomedical entities , such as drugs , genes and phenotypes . We focus on the problem of drug-gene relationship extraction and characterization from unstructured biomedical text , using statistical dependency parsing to extract descriptions of drug-gene relationships from Medline sentences and applying EBC to recognize when two drug-gene pairs share a similar relationship , even when they are described differently in the text . We show that EBC significantly improves our ability to extract both pharmacogenomic and drug-target relationships , and use it to discover new drug-gene relationships for PharmGKB and DrugBank . Finally , we combine EBC and hierarchical clustering to map the global “landscape” of drug-gene interactions , revealing much unforeseen complexity in how these relationships are described in text . We learn , for example , that there are subtle differences in how static knowledge ( past discoveries ) and new experimental discoveries are described , even when they refer to similar phenomena like inhibition , and that seemingly well-defined relationship classes ( such as pharmacogenomic and drug-target relationships ) often exhibit much more detailed chimeric structure than anticipated . More generally , we demonstrate that extracting biomedical relationships based on corpus-level usage patterns , rather than on the properties of individual sentences , helps bypass the need for large , annotated biomedical training corpora–an important property in a domain where few such corpora are available .
The full set of abstracts from the 2013 edition of Medline contains approximately 184 , 000 sentences in which at least one drug name and at least one gene name are present . Many of these sentences contain multiple drug and gene names; the total number of unique drug-gene-sentence combinations is approximately 236 , 000 . As described in the Methods , we use dependency parsing to prune away irrelevant terms and phrases and focus attention on the parts of a drug-gene sentence most relevant to the relationship between a drug and a gene . The pruned versions of drug-gene sentences are called dependency paths . Fig 1 illustrates how dependency paths are constructed from raw sentences . Table 1 provides some common drug-gene dependency paths and associated example sentences . Details about the meanings of the individual grammatical dependencies , with examples , can be found in [15] . We can quantitatively estimate the diversity of drug-gene descriptions in Medline by considering the space of all unique drug-gene dependency paths . The vast majority of dependency paths are rare , indicating high variability in how drug-gene relationships are described . The total number of unique drug-gene dependency paths in Medline is approximately 197 , 000 , of which 7 , 272 ( 4% ) connect at least two different drug-gene pairs . The total number of unique drug-gene pairs co-occurring in Medline sentences is 49 , 564 , of which 14 , 052 ( 28 . 4% ) share a dependency path with at least one other drug-gene pair . Table 2 describes the two datasets used in this paper , which consist of matrices , M , in which the rows are drug-gene pairs and the columns are dependency paths . A cell of M , Mij , contains “1” if drug-gene pair i is connected by dependency path j somewhere in Medline and “0” otherwise . Both of the datasets are over 99% sparse . An important goal , therefore , must be to recognize when different-looking statements are saying the same thing . Otherwise , we can only recognize that two drug-gene pairs share a relationship if their dependency paths are identical . The details of how EBC builds connections among different dependency paths can be found in the Methods . We evaluated EBC’s ability to mine the literature for drug-gene pairs exemplifying two specific types of drug-gene relationships . The algorithm was given only the full , unlabeled text of Medline and a small number of drug-gene pairs that exemplified each type of relationship . We refer to the small sets of labeled drug-gene pairs ( sizes 1 , 2 , 3 , 4 , 5 , 10 , 25 , 50 , and 100 ) as “seed sets” . No text was annotated and no specific sentences were marked as “evidence” for any particular type of relationship . The two relationship types we examined were: Pharmacogenomic ( PGx ) relationships . PharmGKB’s relationships database [5] contains 6283 manually-curated drug-gene associations in which polymorphisms in the gene are known to impact drug response . Drug-target relationships . DrugBank [4] maintains a list of known drug-gene relationships in which the protein product of the gene is a known target of the drug . This list contains 14 , 594 known relationships . Fig 2 shows EBC’s performance extracting PGx and drug-target drug-gene pairs on the two datasets described in Table 2 , and compares EBC to two alternative classifiers that do not account for the semantic relatedness of different dependency paths . On both datasets , and on both tasks , EBC outperforms the other classifiers by a significant margin . On the dense dataset , using seed sets of only 10 labeled drug-gene pairs as input , EBC accurately ( AUC > 0 . 7 ) ranks 89 . 6% of test sets for the PGx task and 96 . 5% of test sets for the drug-target task . In comparison , using the same seed and test sets , the best-performing non-EBC classifier accurately ranks only 31 . 3% of test sets for the PGx task and 49 . 6% for the drug-target task . On the sparse dataset , EBC’s increased performance is even more pronounced . Again using only 10 labeled pairs , EBC accurately ranks 54 . 4% of test sets on the PGx task and 90 . 4% on the drug-target task , compared to 1 . 1% and 6 . 3% for the best-performing non-EBC classifier . EBC’s raw assessments of the similarity of all drug-gene pairs in both datasets can be found in S1 Data . The backbone of EBC is a biclustering algorithm called Information-Theoretic Co-Clustering ( ITCC; [16] , see Methods ) . Fig 3 shows the result of one ITCC run on a small sample dataset consisting of dependency paths that connect different drugs to the gene CYP3A4 ( a liver cytochrome involved in the pharmacokinetic pathways of many drugs ) at least five times in Medline . This dataset contains 62 drug-gene pairs ( where the gene is always CYP3A4 ) and 14 unique dependency paths . As with the datasets in Table 2 , these are arranged in a matrix , M , where an element Mij is “1” if drug-gene pair i is connected by path j somewhere in Medline , and “0” otherwise . We used ITCC to bicluster this matrix into four row clusters and six column clusters . Besides biclustering the matrix , ITCC produces a “smoothed” version of the matrix where certain elements that were not observed in the original dataset are filled in . Fig 3 illustrates that the rows fragment into four clusters that reflect distinct ways that drugs can interact with CYP3A4 . Row cluster 1 contains CYP3A4 inhibitors , a few of which are also substrates . Row cluster 2 contains CYP3A4 inducers . Row clusters 3 and 4 contain substrates of CYP3A4 that are not known inhibitors . EBC combines information from thousands of different biclusterings like this one to assess the relationship similarity of any two drug-gene pairs ( rows ) in the matrix , by looking at how frequently they cluster together . It is also interesting to examine which columns of the matrix cluster together , as this provides insight into how the method is working . Fig 3 shows that the dependency paths naturally fragment into clusters reflecting known biomedical properties . All of the paths referring to inhibition , for example , appear together in column cluster 2 . The sole path referring to induction appears by itself in column cluster 6 . The other four clusters include paths describing situations where the drug is a substrate of CYP3A4 , or is metabolized by it . We see a similar pattern emerge when we examine co-clustering frequencies of the columns on a larger dataset: the dense dataset from Table 2 . Table 3 shows some dependency paths from this dataset that frequently cluster together over 2000 separate runs of ITCC . Paths that frequently cluster together appear to be semantically related . EBC provides a measure of relationship similarity between every drug-gene pair and every other pair ( the frequency with which each pair of rows in the data matrix cluster together ) . By combining these assessments with hierarchical clustering , we created the dendrogram shown in Fig 4 , the details of which are described in the figure caption . Table 4 summarizes the general “themes” of the clusters from Fig 4 and includes the size of each cluster and the density of known PGx and drug-target relationships within that cluster . The cluster assignments for different slices of the dendrogram are provided in S3 Data . Cluster 8 , the largest cluster , contains drug-gene pairs whose descriptions mainly refer to inhibition . This cluster is highly enriched for both PGx and drug-target relationships . When cluster 8 is subdivided by cutting the dendrogram at a lower height , a subcluster ( 8a ) of antagonists and their protein targets splits off from the main cluster . EBC has learned that antagonism is a subclass of inhibition . Cluster 10 , which is a close relative of cluster 8 in the dendrogram , contains drug-gene pairs where the drug is both an inhibitor and a substrate of the protein , such as verapamil/P-glycoprotein . Cluster 3 , another large cluster , is almost exclusively devoted to metabolism and substrate relationships , and is highly enriched for PGx relationships , though not drug-target relationships . Cluster 3 contains three subclusters with slightly different properties . Cluster 3a involves mainly substrate relationships where the concept of “metabolism'' is not mentioned . These include , for example , transport relationships like aminopterin/hOAT1 . Cluster 3b contains most of the metabolic relationships , many of which involve liver cytochromes like CYP3A4 and CYP2D6 . Cluster 3c includes substrate relationships where the drug is often also described as having an effect on the activity of the protein . Other clusters enriched for drug-target relationships include cluster 12 , where the protein is described as the receptor for the drug , cluster 14a , where the drug is described as an agonist of the protein , and cluster 15 , which refers to protein binding . Notably , cluster 14a ( agonists ) is part of a larger cluster , cluster 14 , that encompasses activation and stimulation relationships . Here , EBC has learned that agonism is a subclass of activation . Interestingly , cluster 14b , the part of cluster 14 that refers to activation more broadly and does not specifically refer to agonism , is not enriched for drug-target relationships . Clusters 1–16 , which comprise 3 of the 4 main high-level groups within the dendrogram , are relatively easy to interpret: in general , each displayed a consistent theme . Clusters 17–25 , however , involve descriptions of experimental methods or results about drug effects on gene expression or protein activity . Here , the dendrogram reveals a distinction between past and present knowledge . Drug-gene pairs that are already well-studied are often reported in a static context–“D is an inhibitor of G” , or “D is a G agonist”–whereas other pairs are reported primarily in an experimental context–“we investigated the effect of D on G expression” , “G was activated by D” , or “exposure to D significantly increased G activity” . Depending on the relative frequency of different types of descriptions , a drug-gene pair exemplifying an inhibitory relationship might end up in cluster 8 ( mostly static descriptions ) or cluster 21 ( mostly experimental descriptions ) . Interestingly , drug-gene pairs from cluster 21 appear together in the literature significantly fewer times than drug-gene pairs from cluster 8 ( median 9 times for cluster 21 vs . 16 times for cluster 8; maximum 66 times for cluster 21 vs . 2722 times for cluster 8; p < 0 . 0001 , Mann-Whitney test ) , which seems to corroborate our assertion that the drug-gene pairs from cluster 21 represent more tentative experimental findings as opposed to well-established static knowledge . Finally , the dendrogram reveals that PGx and drug-target relationships do not constitute distinct classes of relationships , but are chimeras . PGx relationships are composed of relatively distinct subgroups corresponding to ( a ) situations where the drug inhibits the gene/protein ( and therefore , mutations in the gene could be expected to impact response to the drug ) , and ( b ) situations where the protein is involved in the metabolism or transport of the drug . Drug-target relationships overlap with ( a ) but not ( b ) , and include other non-PGx subclasses , such as receptor binding and agonism . EBC reliably detects new drug-gene pairs reflecting relationships of interest to PharmGKB and DrugBank , so we attempted to discover new examples from our corpus . We built seed sets containing all known relationships from PharmGKB and DrugBank and incorporated these into EBC to rank the remaining drug-gene pairs according to EBC’s certainty that they represented PGx or drug-target relationships . There was 13 . 6% overlap between the two seed sets , with 84 drug-gene pairs in both , 206 in PharmGKB only , and 326 in DrugBank only , and 2898 pairs that were unknown to both . The dendrogram shown in Fig 5 is identical to that in Fig 4 , except that the clusters are replaced by vertical bars , the heights of which correspond to EBC's relative certainty that the pairs in question represent PGx relationships ( shown in orange ) or drug-target relationships ( shown in blue ) . The raw prediction data can be found in S4 Data . Known PGx or drug-target pairs are excluded from the bar graphs , but are denoted beneath the bars with orange or blue dots . As expected , we see high prediction certainty for drug-target and PGx relationships among the inhibitors in cluster 8 , and high certainty for PGx relationships among the metabolic/substrate relationships in cluster 3 . We also observe an interesting area of high enrichment for both types of relationships among clusters 21–23 , where inhibition is mostly reported in an experimental context , but the density of known PGx and drug-target relationships is quite low . These could represent new experimental findings that will be discussed as static knowledge in a few years . Table 5 shows the top 20 predictions of new PGx candidate pairs for PharmGKB , and Table 6 shows the top 20 candidate drug-target pairs for DrugBank . Among the top 20 PGx predictions , five are already known to PharmGKB and have been demonstrated experimentally ( one or more variants of the gene have been shown to impact response to the drug ) , but were coded in the PharmGKB relationships file in such a way that they were not included in the seed set . One is brand new: polymorphisms in ABCB1 ( P-glycoprotein ) do impact clinical response to fentanyl , but this relationship is currently unknown to PharmGKB . An additional eight pairs represent likely PGx relationships , such as known inhibitory or metabolic relationships , but no experiments have yet been conducted that might relate polymorphisms in the gene to drug response . And finally , in five cases , the potential for a PGx association was considered likely enough that it was investigated experimentally , but no significant clinical association between genotype and drug response was found . Among the top 20 predictions for new drug-target relationships for DrugBank , four are already known but were listed in DrugBank under alternate gene names . An additional seven are new , proven drug-target relationships . Of these , five involve drugs that are themselves unknown to DrugBank ( there are no entries for ketanserin , cangrelor , nutlin-3 , or tropisetron in DrugBank ) . There are also several interesting , yet erroneous findings arising from parser and lexicon errors in which a molecule , such as IL-1 , is mistaken for its receptor , and that receptor is the true target of the drug . These are explored further in the Discussion .
Although a great deal of research effort has been directed at the problem of relationship extraction in pharmacogenomics [17–19] , and in the biomedical domain in general [20–25] , high-quality biomedical knowledge bases like OMIM , DrugBank and PharmGKB still rely almost entirely on human curators , who comb the literature manually in search of new relationships . The authors of BioGraph , a new biomedical knowledge base incorporating data from 21 different sources , recently decided to exclude databases that were not manually curated , citing data quality issues [26] . Why is biomedical relationship extraction so challenging ? We believe that one key stumbling block lies in how the problem has historically been defined . Biomedical relationship extraction is usually thought of as a sentence-level problem–does a particular sentence describe a specific type of relationship or not ? However , as we have seen , sentence-level descriptions are highly erratic . Faced with a bewildering array of possibilities for how similar relationships can be described , sentence-level relationship extraction algorithms often rely on manually-constructed rules or ontologies that map diverse surface forms onto common semantics [17 , 27–29] . These systems require a non-trivial amount of human maintenance and must be rebuilt for each new domain . Machine learning algorithms for sentence-level relationship extraction avoid rules but face another serious problem: the need for annotated training sentences . Recently , researchers have begun to produce annotated training sets for the biomedical domain [30 , 31] but manual annotation is almost as expensive as manual curation , both in time and human effort . As a result , little to no annotated training data exist for many classes of biomedically interesting relationships . These are important problems for NLP , but they only exist because we think of biomedical relationships at the level of individual sentences . From a biomedical research standpoint , there is no need to do so—we are most interested in the true relationship between a drug and a gene , not in the meaning of any particular sentence . As a result , we have taken a corpus-level approach where all of the information about a drug-gene pair from all of its available sentence-level descriptions is combined . Latent connections among different-looking descriptions are then discovered in an unsupervised fashion from structure inherent in the raw text , requiring no human effort and boosting our ability to extract relationships of interest . We contend that biomedical relationships should be considered properties of biomedical entities like drug-gene pairs , not individual sentences . A description like “D decreased G levels” does not constitute an inhibitory relationship; it is simply an experimental finding that increases the likelihood of such a relationship . This allows the same sentence to provide evidence for or against multiple types of relationship , the exact definitions of which are application dependent . It also allows drug-gene pairs to exhibit multiple relationship types at once . We see evidence for such an approach when we contrast EBC’s performance at extracting PGx relationships with its performance extracting drug-target relationships . EBC was uniformly worse at extracting PGx relationships , even though these two sets of experiments used the same data matrices . We see why in Fig 4: it turns out that what we originally considered to be well-defined relationship classes ( PGx and drug-target relationships ) are actually composites of several finer-grained sub-classes . A high percentage of PGx relationships reside in cluster 3 , the metabolism/substrate cluster , which inhabits a region of the dendrogram far from the inhibition clusters . In cases where the seed set consists mostly of metabolic relationships and the test set mostly of inhibition relationships , we would not expect EBC to perform well , even though both groups are still technically PGx relationships . We initially believed that PGx relationships would be expressed in sentences relating specific polymorphisms to changes in drug efficacy , such as , “The CYP3A4 C3435T polymorphism influences rifampicin exposure in human hepatocytes” . In reality , however , relatively few such sentences exist . Most evidence for PGx relationships comes instead from descriptions of other types of relationships , such as inhibition and metabolism . So we see that although a PGx relationship can be considered a property of a drug-gene pair , it is not generally a property of any particular sentence describing that pair . EBC is part of a subfield of NLP called distributional semantics , in which patterns in large , unlabeled text corpora are used to create feature representations of words , phrases , or other entities ( in our case , drug-gene pairs ) based on how they are used in context . The similarity of these representations then serves as a proxy for semantic relatedness [12] . Distributional semantics algorithms’ theme of discovering semantic relatedness by looking at large-scale usage patterns inspired our corpus-level approach to drug-gene relationship extraction . For example , in EBC , these representations are the co-clustering frequencies of each drug-gene pair with every other pair , and the contextual features are the dependency paths . EBC builds on a long history of distributional semantics work in the NLP literature , much of which focuses on assessing the semantic similarity of individual words [12 , 13 , 32] , and some of which has tackled relationship extraction outside the biomedical domain [33–36] . EBC is most similar in spirit to matrix factorization techniques like Latent Semantic Analysis ( LSA ) [13]; ITCC forms a low-rank approximation of the original drug-gene-pair-by-dependency-path matrix , and EBC stacks thousands of slightly different ITCC-based approximations on top of each other to make its similarity assessments . LSA uses the singular value decomposition ( SVD ) [37] instead of ITCC to accomplish a similar goal , and has been applied in at least one case to corpus-level relationship extraction ( a technique called Latent Relational Analysis , or LRA ) [36] . We compare EBC to LSA on the PGx relationship extraction task in S2 Text . There are dozens of other clustering and matrix factorization methods available , and some have already been applied to text mining tasks like relationship extraction . Several methods cluster textual patterns to discover latent groupings of entity pairs corresponding to distinct relations [38–41] . Others use the entity pairs flanking different textual patterns to group the patterns themselves into semantically related classes [33] . Some methods , like EBC , address both problems simultaneously [42–45] . The issue of textual “entailment”–finding the degree to which one statement implies the existence of another–is also an active area of research in NLP and is closely related to several of the methods described above [46] . Although these techniques have already shown great promise on related tasks in web and newswire data , to our knowledge none has yet been applied to relationship extraction in the biomedical domain . In our analysis of drug-gene relationships , we made several choices about ( a ) how to identify drugs and genes in text , ( b ) the type of text to use as our corpus , and ( c ) what constitutes a “feature” ( a single column in the data matrix ) . In all cases , we made the simplest choices possible , both to enable others to reproduce our results , and to distinguish EBC’s own limitations from errors/omissions in the preprocessing steps and text itself . We identify drugs and genes in the text based on simple string matching to single-word drug and gene names from PharmGKB [5] . Named entity recognition ( NER ) is its own area of NLP , and identifying biomedical entity names in text is itself a nontrivial proposition . We can see one obvious disadvantage of this approach in cluster 24 of Fig 4 and Table 4 , which includes “gene names” like COPD ( a . k . a . chronic obstructive pulmonary disease ) and NIDDM ( non-insulin-dependent diabetes mellitus ) . Table 6 also reflects a lexicon error where the term “leukotriene” is listed as a synonym for the leukotriene B4 receptor . Some such errors might be avoided if we used a more elaborate NER system [47 , 48] , though such systems themselves are not perfect and can introduce new sources of error . Our stipulation that the entity names be single words also led to errors in cases ( see Table 6 ) where a molecule , such as IL-1 , is mistaken for its receptor , the “IL-1 receptor” , because “IL-1 receptor” is a multi-word phrase not allowed in the lexicon , while “IL-1” is allowed . We also made no attempt to normalize gene names , so in our results , ABCB1 , MDR-1 , and P-gp are all different . Again , this was done to avoid introducing normalization errors , and because genes and their corresponding proteins are often described in different contexts . To construct dependency paths from raw Medline sentences , we used the Stanford Parser [49] , a free and open-source statistical parser . The Stanford Parser was trained using labeled text from newswire corpora , so it sometimes fares poorly on biomedical text . For example , the parser often mistakes gene names for adjectives ( “CYP3A4” in the phrase “CYP3A4 polymorphism” is frequently labeled as an adjective ) . We used the out-of-box implementation of the Stanford Parser and did not perform any manual review or correction of parses to improve its performance ( again in the interest of simplicity ) . Because EBC operates at the level of drug-gene pairs and not individual sentences , its performance is generally robust to parsing errors as long as the parser makes the same errors consistently . There are some errors that do lead to incorrect conclusions , however . For example , we observe some situations where dependency paths bypass important details about relationships , such as a sentence where a drug is described as “transcriptionally up-regulating G expression” and the dependency path only captures the effect on expression , not its directionality . These are usually generalizations rather than errors , but they do result in some loss of information from the sentence . Finally , our corpus consisted of all abstracts from the 2013 edition of Medline . Including information from the full text of the research articles could help discover relationships not mentioned in the abstracts , but many journals do not provide access to the full text , and we did not wish to bias our results in favor of relationships reported in a subset of journals . Our approach would remain the same regardless of the corpus . The combination of EBC and dependency path features described here allows us to reliably extract biomedical relationships of interest from Medline sentences , smoothing over differences in how these relationships are described . This finding opens the door to many interesting possible future applications . For example , EBC could be used to extract relationships spanning multiple sentences or entire abstracts by using features such as individual dependencies , words , or phrases in place of dependency paths . As new gold-standard sets of biomedical relationships become available ( such as all drug-gene pairs reflecting inhibitory relationships or specific collections of drug-gene pairs relevant to particular laboratories’ research efforts ) these can seamlessly be incorporated into EBC to extract these relationships at scale . EBC could also potentially be used for lexicon or ontology expansion in a manner similar to LSA or random indexing [50 , 51] . At its core , EBC is not relationship extraction-centric . The algorithm itself is agnostic to the type of data contained in its input matrix . EBC simply allows us to use latent structure in large , unlabeled datasets to boost our ability to extract new information from those datasets , even when our access to labeled training examples is limited . Datasets like these occur throughout biomedical research , even beyond NLP . We look forward to seeing how EBC fares on some other classes of related problems , in NLP and elsewhere .
When applied to drug-gene relationship discovery , the EBC algorithm operates on a data matrix where the rows are drug-gene pairs and the columns are dependency paths that connect them in the literature . The algorithm has two steps , the first unsupervised and the second supervised . First , unsupervised biclustering is used to simultaneously discover ( a ) latent connections among dependency paths ( columns ) that appear different but connect similar drug-gene pairs , and ( b ) latent similarities among different drug-gene pairs ( rows ) that are connected by similar dependency paths . Over multiple iterations of ( a ) and ( b ) , the algorithm can infer that two drug-gene pairs share a similar relationship , even when they share no dependency paths in common . To make its similarity assessments , EBC uses an ensemble of biclustering runs where the cluster centers are initialized randomly on each run , providing many different guesses about which dependency paths and drug-gene pairs are related . In the second step , EBC incorporates a small seed set of drug-gene pairs ( rows ) reflecting some known relationship , and ranks other pairs based on their similarity to the pairs in the seed set . The specific steps of the EBC algorithm are as follows: Preprocessing ( drug-gene relationship extraction task ) : Identify all drug-gene pairs co-occurring in sentences within a corpus of text . ( In our experiments , these were drug-gene pairs co-occurring in Medline sentences . ) Call the number of drug-gene pairs n . Extract all dependency paths connecting these drug-gene pairs in the corpus . Call the total number of observed paths m . Arrange the data in an n x m matrix where the rows represent drug-gene pairs and the columns dependency paths . A cell with coordinates ( i , j ) in this matrix contains “1” if drug-gene pair i has been connected by path j somewhere in the corpus , and “0” otherwise . EBC algorithm: We identified drug and gene entity names in the text using simple string matching to lexicons , though any type of named entity recognition software could be incorporated at this stage [47 , 48] . We obtained drug and gene lexicons from PharmGKB [5] and filtered them against a dictionary of common English words to remove promiscuous terms ( such as “CAT” , which is both a gene name and an animal ) . We included only drug and gene entities with one-word names , as these names mapped to single nodes in the dependency graphs . The final drug lexicon contained 4008 unique terms , and the final gene lexicon contained 109 , 597 terms ( many genes/proteins had multiple names ) . We used the Stanford Parser [49] to generate dependency graphs for all sentences in Medline 2013 between 4 and 50 words in length ( roughly 95% of all sentences in Medline ) . The input to the parser is a raw Medline sentence , and the output is a dependency graph . A dependency graph ( see Fig 1 ) is one way to represent the grammatical architecture of a sentence; the nodes are words , and the edges are grammatical dependencies ( grammatical relationships between pairs of words , described in detail in [15] ) . A dependency path is a path through a dependency graph that connects two entities of interest . Considering a dependency path , instead of an entire sentence , can help “prune out” irrelevant terms and phrases and focus our attention on the part of the sentence directly relevant to the relationship between the two entities . We extracted all dependency paths linking drugs to genes . It was possible for a single sentence to generate more than one dependency path if multiple drug or gene names were present in the sentence . We oriented our paths so that they always started at the drug and ended at the gene , and we eliminated edge directions . ( We never observed a single situation where we accidentally collapsed paths with different meanings in doing so , since most pairs of words can only be connected by a particular dependency type , like amod or nn , in one direction . ) We eliminated paths containing dependencies of type conj [15] , because these were usually errors arising from inadequacies in how the dependency parser represents lists . Note that because the dependency graphs are trees , there is one unique dependency path for each drug-gene pair in a sentence . ITCC forms a low-rank approximation of a matrix by iteratively clustering the rows and columns . ITCC treats the data matrix , M , as a joint probability distribution over its rows ( Y , drug-gene pairs ) and columns ( X , dependency paths ) . Given fixed numbers of row ( k ) and column ( l ) clusters , ITCC finds a set of cluster assignments for the rows and columns that captures most of the mutual information between X and Y with the stipulation that X and Y only interact via their cluster assignments , X^ and Y^ . Mathematically , ITCC replaces the joint distribution of X and Y , p ( x , y ) =p ( x^ , y^ ) p ( x , y|x^ , y^ ) , with an approximate distribution of the form q ( x , y ) =q ( x^ , y^ ) q ( x|x^ ) q ( y|y^ ) , and assigns rows and columns to clusters so that q ( x , y ) captures most of the mutual information between X and Y in p ( x , y ) ( equivalent definition: the Kullback-Leibler divergence between p ( x , y ) and q ( x , y ) is minimized ) . We implemented ITCC in Java . Some technical details about our implementation can be found in S3 Text . There are two unknown input parameters to ITCC: the numbers of row ( k ) and column ( l ) clusters . The optimal choices for k and l must be decided heuristically . We describe our heuristic for choosing k and l in S1 Text A . Due to random initialization of the row and column cluster centers , ITCC generally converges to a different locally-optimal biclustering on each run; this diversity is what guarantees optimal performance of the EBC algorithm . We ran ITCC N = 2000 times at the optimal k and l and recorded the number of runs in which each pair of rows shared a cluster . We observed that on our data matrices , EBC’s performance increased monotonically with N , stabilizing at approximately N = 1000 . Once EBC’s unsupervised step is performed and appropriate seed ( S ) and test ( T ) sets identified , test set items can be ranked as follows: EBC’s scoring function . For each test set member , Ti , rank all n rows of the data matrix based on how often they co-cluster with Ti . This produces a ranking Ri of length n in which pairs that frequently co-cluster with Ti are assigned high ranks and those that seldom co-cluster get low ranks . The score for Ti is the rank sum of the members of the seed set , S , within this list , or: score ( Ti ) =∑j=1nj∙I{Rij∈S} where I{Rij∈S}={1ifRij∈S0otherwise Using ranks instead of absolute co-clustering frequencies produces a score that does not depend on how often , on average , a given drug-gene pair co-clusters with other pairs , since this baseline “promiscuity” changes from pair to pair . For some applications , those differences might not matter ( or they might be informative ) but we normalized to ranks so promiscuous pairs ( which are often well-known or frequently mentioned pairs ) would not consistently receive higher scores than less promiscuous pairs . EBC’s scoring function will assign a high score to a test set member as long as the seed set rows tend to cluster with it more frequently than other rows do . Ties are broken randomly . We compared EBC’s performance to two other ranking methods that did not take the semantic similarity of different dependency paths into account: AvgCosine . Let vTi be the row vector in the data matrix associated with test set member i . This vector contains m elements: one for each dependency path . Let vSj be the row vector associated with seed set member j . Here we score each test pair Ti based on the average cosine similarity of vTi with all of the row vectors from the seed set , or: score ( Ti ) =1|S|∑j=1|S|vTi⋅vSj‖vTi‖‖vSj‖ where ‖⋅‖ denotes the Euclidean norm . RankSum . In keeping with the spirit of EBC’s scoring function , for each Ti we rank all n rows of the data matrix based on cosine similarity to vTi . This produces a ranking Ri of length n in which rows with high cosine similarity to vTi are assigned high ranks and those with low cosine similarity to vTi get low ranks . The score for Ti is the rank sum of the members of S within this list , and looks identical to that for EBC; the only difference is that the rankings Ri are produced using cosine similarity and not EBC . For both the PGx and drug-target tasks , and for seed set sizes |S| = 1 , 2 , 3 , 4 , 5 , 10 , 25 , 50 , and 100 , we generated 1000 random seed sets and 1000 corresponding test sets , ensuring that the seed sets and test sets did not overlap . The test sets were all composed of 100 drug-gene pairs , 50 of which had known PGx or drug-target relationships and 50 of which did not . All three ranking methods were used to rank the members of each test set , using its associated seed set for scoring . We also explored the impact of data sparsity by performing these evaluations on two separate datasets . In the “dense” dataset , we included only drug-gene pairs and dependency paths that occurred at least five times in Medline . In the “sparse” dataset , we included dependency paths occurring at least twice , and any drug-gene pairs they connected ( even if they only co-occurred in a single sentence ) . More information about the two datasets can be found in Table 2 , and the data matrices themselves can be found in S2 Data . We evaluated the quality of each ranking by calculating the area under the receiver operating characteristic curve ( AUC ) [52] , a measure of how likely it is that a positive element of the test set will be ranked higher than a negative element . We elected to use AUC instead of precision or recall because we wanted a threshold-independent measure of the overall quality of the ranking . We used R’s ROCR package to calculate the AUCs . From a practical standpoint , we were concerned mainly with the following scenario: Given that I have a seed set about whose quality I know nothing , what is the chance I can accurately prioritize the knowledge I am looking for within my [unlabeled] corpus ? Our evaluation metric was , therefore , the fraction of the 1000 seed sets that ranked their corresponding test sets with AUC > 0 . 7 . To investigate how similar EBC’s performance was to a more established method designed to solve a similar problem , we used the singular value decomposition ( SVD ) [37] to decompose our two data matrices , creating “compressed” feature vectors of reduced dimensionality for each drug-gene pair and incorporating these , rather than the raw row vectors , into the two non-EBC ranking methods described above . This approach is identical to the famous text mining technique Latent Semantic Analysis ( LSA; [13] ) which was originally applied to overcome issues of data sparsity in document retrieval . The results of these experiments are described further in S2 Text . EBC provides a natural measure of similarity for each drug-gene pair and every other pair: the number of times the rows corresponding to those two pairs clustered together over the N biclustering runs . However , as we have seen , these raw values are not fair measures of distance for all pairs , since some drug-gene pairs tend to cluster frequently with many other pairs , and others cluster less frequently . EBC’s rank-based scoring function accounts for this by normalizing to ranks: each drug-gene pair ranks all other pairs by co-clustering frequency , and these ranks are used in place of the raw co-clustering values in the scoring function . To implement EBC's scoring function in an unsupervised manner to construct our dendrogram , we started with our n x n matrix of co-occurrence values , C , in which Cij was the number of runs ( out of N total ) in which drug-gene pair i co-clustered with drug-gene pair j . We then converted C into a correlation matrix , ρ , also n x n , where ρij contained the Spearman correlation of Ci⋅ and Cj⋅ , the ith and jth rows of C ( note that C is symmetric , so we could just as easily have used columns ) . These correlations are , as in EBC's scoring function , measures of how similarly drug-gene pair i and pair j rank all other pairs in the matrix , and are not biased in favor of promiscuous pairs . We then used 1 − ρ as the distance measure for hierarchical clustering using minimax linkage [53] to produce the dendrogram shown in Fig 4 . Using a different linkage function or distance metric , obviously , would produce a different-looking dendrogram . We used several R packages to produce the dendrogram figures , including ape ( a library for making phylogenetic trees ) , and protoclust ( a library for hierarchical clustering using minimax linkage ) . To achieve the radially-spaced tip markers , we used a separate package [54] .
|
Virtually all important biomedical knowledge is described in the published research literature , but Medline currently contains over 23 million articles and is growing at the rate of several hundred thousand new articles each year . In this environment , we need computational algorithms that can efficiently extract , aggregate , annotate and store information from the raw text . Because authors describe their results using natural language , descriptions of similar phenomena vary considerably with respect to both word choice and sentence structure . Any algorithm capable of mining the biomedical literature on a large scale must be able to overcome these differences and recognize when two different-looking statements are saying the same thing . Here we describe a novel algorithm , Ensemble Biclustering for Classification ( EBC ) , that learns the structure of drug-gene relationships automatically from the unstructured text of biomedical research abstracts . By applying EBC to the entirety of Medline , we learn from the structure of the text itself approximately 20 key ways that drugs and genes can interact , discover new facts for two biomedical knowledge bases , and reveal rich and unexpected structure in how scientists describe drug-gene relationships .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Learning the Structure of Biomedical Relationships from Unstructured Text
|
Recent studies have suggested that a sub-complex of RNA polymerase II composed of Rpb4 and Rpb7 couples the nuclear and cytoplasmic stages of gene expression by associating with newly made mRNAs in the nucleus , and contributing to their translation and degradation in the cytoplasm . Here we show by yeast two hybrid and co-immunoprecipitation experiments , followed by ribosome fractionation and fluorescent microscopy , that a subunit of the Ccr4-Not complex , Not5 , is essential in the nucleus for the cytoplasmic functions of Rpb4 . Not5 interacts with Rpb4; it is required for the presence of Rpb4 in polysomes , for interaction of Rpb4 with the translation initiation factor eIF3 and for association of Rpb4 with mRNAs . We find that Rpb7 presence in the cytoplasm and polysomes is much less significant than that of Rpb4 , and that it does not depend upon Not5 . Hence Not5-dependence unlinks the cytoplasmic functions of Rpb4 and Rpb7 . We additionally determine with RNA immunoprecipitation and native gel analysis that Not5 is needed in the cytoplasm for the co-translational assembly of RNA polymerase II . This stems from the importance of Not5 for the association of the R2TP Hsp90 co-chaperone with polysomes translating RPB1 mRNA to protect newly synthesized Rpb1 from aggregation . Hence taken together our results show that Not5 interconnects translation and transcription .
The life of an mRNA molecule in eukaryotic cells is considered to be the sum of distinct events separated in time and space . Precisely , this separation seems to constitute the characteristic difference distinguishing eukaryotes from prokaryotes , where translation is co-transcriptional and occurs in a single cellular compartment . Several studies in recent years , however , have challenged this simple view . First , the heptapeptide repeat-containing C-terminal domain ( CTD ) of the largest subunit of eukaryotic RNA polymerase II ( RNA Pol II ) was found to direct post-transcriptional RNA processing events . It serves as a landing platform for components of the machines involved in mRNA capping , splicing , and mRNA export [1] , [2] , [3] . More recently and provocatively , an RNA Pol II subunit , Rpb4 , has been suggested to play roles not only in the nucleus during the transcription process , but also subsequently in the cytoplasm , contributing to both the RNA degradation and translation processes [4] , [5] . The conserved eukaryotic Ccr4-Not complex also contributes to both transcription and mRNA decay and is found both in the cytoplasm and nucleus ( for reviews see [6] , [7] ) . The complex consists of 9 subunits in the yeast Saccharomyces cerevisiae ( Ccr4 , Caf1 , Caf40 , Caf130 , and Not1-5 ) . The single CNot3 protein of higher eukaryotes , whether human [8] or fly [9] corresponds to yeast Not3 and Not5 , which share 44% identity in their N-termini . In these eukaryotes , the complex also carries additional subunits CNot10 and CNot11 , and lacks Caf130 [10] , [11] , [12] , [13] . The Ccr4-Not complex plays roles at several stages of gene expression . Many subunits of Ccr4-Not can be cross-linked to genes being transcribed [14] , [15] , [16] , the complex interacts with RNA Pol II and contributes to transcription elongation [17] , and the Not subunits impact on the distribution of general transcription initiation factors across the genome [14] , [18] . The Ccr4 and Caf1 subunits comprise the major eukaryotic deadenylase and catalyze the first and rate-limiting step of RNA degradation ( [19] and for review see [20] ) . Recent studies in yeast have established that some subunits of the Ccr4-Not complex are present at translating ribosomes ( polysomes ) [21] , [22] and that the level of polysomes is reduced in certain Ccr4-Not deletion mutants . This coincides with an accumulation of aggregated proteins in the mutants [21] , [23] , and with the importance of the Ccr4-Not complex for the assembly of the multi-subunit proteasome complex [24] . The functional implication of both the Ccr4-Not complex and the Rpb4 subunit of RNA Pol II to all stages of the mRNA life cycle was supported by a recent study revealing that transcription and mRNA degradation rates have co-evolved oppositely and that this coincides with single nucleotide changes in either RPB4 or CCR4-NOT genes [25] . These studies indicate that Rpb4 , which connects transcription to downstream events , may somehow connect the polymerase also to the Ccr4-Not complex . Intriguingly however , it has been reported that the interaction of polymerase with the Ccr4-Not complex , does not require Rpb4 [17] . RNA Pol II consists of 12 subunits , 10 of which compose the catalytic core . These are shared with RNA Pol I and RNA Pol III or related to subunits of these other polymerases [26] . Several recent studies have provided insight into the mechanism by which the 12-subunit RNA Pol II is assembled ( reviewed in [27] ) . The finding that partially assembled polymerase complexes accumulated in the cytoplasm under conditions of imbalanced levels of different RNA Pol II subunits suggested cytoplasmic assembly of this enzyme . For instance , treatment of cells with α-amanitin leads to specific degradation of Rpb1 from elongation-stalled polymerase and to the accumulation of a cytoplasmic Rpb2 sub-complex containing Rpb3 , Rpb10 , Rbp11 and Rpb12 [28] . In contrast , inhibition of the de-novo synthesis of any Pol II subunit besides Rpb1 by siRNA leads to the accumulation of cytoplasmic Rpb1 . This pool of Rpb1 is mostly unphosphorylated and insensitive to α-amanitin suggesting that it is newly synthesized Rbp1 , which has not been engaged in transcription . The cytoplasmic assembly complexes have been characterized by mass-spectrometry-based proteomics [28] and found to represent two intermediates . The Rpb2 sub-complex contains the Gpn1/Npa3 , Gpn2 and Gpn3 GTP binding proteins and several chaperones . Another sub-complex contains Rpb1 with the Hsp90 chaperone and its R2TP co-chaperone . In yeast , R2TP is composed of the Tah1 tetratricopeptide repeat ( TPR ) protein , Pih1 ( protein interacting with Hsp90 ) and the two AAA+ ATPases , Rvb1 and Rvb2 . Pih1 binds Tah1 , the Rvb proteins and the yeast Hsp90 chaperones Hsp82 and/or Hsc82 [29] , [30] . The two RNA Pol II assembly intermediates join , and then enter the nucleus mainly via the nuclear transport of fully assembled polymerase [28] . The transport requires association of the assembled polymerase with an NLS-containing protein Iwr1 [31] . GTP binding might also play a role in assembly and/or nuclear import of assembled RNA Pol II , since depletion of the human GTP binding protein Gpn1 , or mutation of its yeast ortholog Npa3 , leads to cytoplasmic accumulation of polymerase subunits [32] , [33] . Another protein recently identified as playing a role in assembly of RNA Pol II and also RNA Pol I and RNA Pol III is the Bud27 prefoldin [34] . It is required for the correct integration of Rpb5 and Rpb6 into all three polymerases and acts prior to nuclear import . Rpb4 forms a hetero-dimeric sub-complex with Rpb7 , which extends like a stalk from the core of RNA Pol II . The Rpb4/7 dimer can dissociate from the rest of RNA Pol II and is in excess to the other RNA polymerase II subunits . It shuttles between the cytoplasm and the nucleus , interacts with the translation scaffold factor eIF3 and is required for wild-type levels of translating polysomes [4] , [35] , [36] and for review see [37] . Curiously , while Rpb4 plays an essential role in connecting transcription to downstream steps in gene expression , it is not essential for yeast viability . In this work we investigated how Rpb4 and the Ccr4-Not complex are functionally connected . We determined that Rpb4 shows a very tight two-hybrid interaction with the Not5 subunit of the Ccr4-Not complex . In addition , we found that the presence of Rpb4 in translating ribosomes , and more globally the association of Rpb4 with mRNAs , was dependent upon nuclear Not5 . We observed that not only Rpb4 , but also several other RNA Pol II subunits were present in polysomes . These findings are consistent with cytoplasmic assembly of RNA Pol II occurring on translating ribosomes , as suggested for protein complexes quite generally [38] . Moreover our data indicates that cytoplasmic Not5 contributes to RNA Pol II assembly at least in part by supporting interaction of de-novo synthesized Rpb1 with Hsp90-R2TP co-chaperone and preserving a soluble pool of Rpb1 that is apt to interact with Rpb2 to form new RNA Pol II complexes . We determined that this role of Not5 for Rpb1 solubility is conserved in Drosophila melanogaster . Hence , Not5 is in a central position for the bidirectional communication between transcription and translation .
The importance of the Ccr4-Not complex during the entire life of mRNAs ( for review see [6] ) is reminiscent of the extended function described for the Rpb4 subunit of RNA Pol II [37] . We hence investigated by the two-hybrid assay whether the Ccr4-Not complex interacts with Rpb4 . We used Rpb4 as bait and tested its interaction against each of the Ccr4-Not subunits as preys . As positive control we used Nip1 , an eIF3 subunit with which Rpb4 has been shown to interact [4] . We observed strong two-hybrid interactions of Rpb4 with both Not3 and Not5 . In both cases the detected interaction was more remarkable than that between Rpb4 and its known partner Nip1 ( Fig . 1A ) . A weaker interaction between Rpb4 and the other Ccr4-Not subunits was also evident ( Fig . S1A ) and they could be confirmed by co-immunoprecipitation experiments ( Fig . S1B ) even in the presence of RNase indicating that the interaction is not bridged by RNA . Not3 and Not5 share extended sequence homology and may have evolved in yeast from a common ancestral gene , since in higher eukaryotes there is a single gene encoding this subunit of the Ccr4-Not complex ( reviewed in [39] ) . The deletion of either Not3 or Not5 is lethal when combined with the deletion of Not4 [40] so we tested whether this genetic interaction was shared by Rpb4 that is not a subunit of the Ccr4-Not complex , but interacts with both Not3 and Not5 . Indeed , the deletion of Rpb4 displayed a striking slow growth phenotype when combined with Not4 ( Fig . 1B ) . A synthetic slow growth phenotype was also detected when rpb4Δ , like not5Δ [40] , was combined with ccr4Δ , but not when it was combined with the deletion of Caf40 , another Ccr4-Not subunit ( Fig . S1C ) . Rpb4 has been connected to translation and found in polysomes [4] , and this has also been established for certain Ccr4-Not subunits ( Not4 and Not5 ) [21] , [22] , [23] . This led us to test whether the presence of Rpb4 and Ccr4-Not subunits in polysomes was interdependent . Not2 and Not5 were essential for the detection of Rpb4 in polysomes ( Fig . 1C ) . When trying to do the reverse experiment we were unable to create an rpb4Δ strain expressing tagged Not5 , probably because of synthetic lethality issues ( see below ) . Hence instead we followed the fractionation of tagged Not3 in cells lacking Rpb4 . Not3 was present in polysomes even in cells lacking Rpb4 ( Fig . 1D ) . Rpb4 was first connected to translation through its interaction with the translation initiation factor eIF3 [4] . We could recapitulate this interaction by co-immunoprecipitating a subunit of eIF3 , Prt1 , with Rpb4 ( Fig . 1E ) . Since we observed that Rpb4 was not present in polysome fractions in not2Δ and not5Δ , it was of interest to determine whether Rpb4 could still interact with eIF3 in these mutants . We could not detect co-immunoprecipitation of Prt1 with Rpb4 in not5Δ cells , suggesting that the interaction of Rpb4 with eIF3 is dependent upon Not5 ( Fig . 1E ) . Consistent with a role for the Ccr4-Not complex in mediating the interaction of Rbp4 with eIF3 , we found that Prt1 co-immunoprecipitates with Not1 ( Fig . 1F ) . In addition two-hybrid experiments revealed interactions between another eIF3 subunit , Nip1 and many Ccr4-Not subunits ( Fig . S1D ) . The Not1-eIF3 interaction was independent of Not5 ( Fig . 1F ) in good correlation with the observation that Not1 association with polysome fractions does not depend upon Not5 but is dependent upon intact polysomes ( Fig . S1E ) . Rpb4 has been shown to shuttle between the nucleus and the cytoplasm and accumulates in the cytoplasm under stress conditions that can be mimicked by fixing yeast cells with formaldehyde [41] . Under such conditions , we could confirm a largely cytoplasmic localization of Rpb4 in wild-type cells ( Fig . 1G ) . In contrast , in cells lacking Not5 , Rpb4 was present in the cytoplasm in a lesser amount and accumulated in nuclei ( Fig . 1G ) . Moreover , the nuclei tended to display aberrant morphologies when cells lacked Not5 and expressed tagged Rpb4 . This synthetic phenotype was consistent with our observation that no viable spores lacking both Not5 and Rpb4 germinated upon dissection of diploids obtained from crossing rpb4Δ with not5Δ and that attempts to create an rpb4Δ strain expressing a C-terminally tagged Not5 was unsuccessful as mentioned above . The expression of a derivative of Not5 maintained in the cytoplasm by a nuclear export signal ( NES ) in the not5Δ strain expressing tagged Rpb4 ( Fig S2 ) could not rescue the stress-induced cytoplasmic localization of Rpb4 , in contrast to its wild-type counterpart ( Fig . 1H ) . The presence of Rpb4 in the cytoplasm was reported to result from its interaction with mRNAs occurring at the completion of transcription [36] . We thus analyzed the interaction of Rpb4 with a couple of mRNAs , namely RPB1 and NIP1 , in wild-type cells or in cells lacking Not5 . Rpb4 is expressed at similar levels in both strains , and is immunoprecipitated to similar extents in both strains ( Fig . S3 ) . RPB1 mRNA was significantly enriched in the Rpb4 immunoprecipitates from wild-type cells but not from not5Δ cells , and in parallel significant binding of Not5 to RBP1 mRNA was detected ( Fig . S4 ) . In contrast , no significant enrichment of NIP1 mRNA could be detected in the Rpb4 immunoprecipitates from either strain , but it is nevertheless noteworthy that less NIP1 mRNA was immunoprecipitated with Rpb4 from not5Δ than from the wild-type ( Fig S4 ) . The relative presence of NIP1 mRNA in the immunoprecipitate versus total mRNA was similar for Rpb4 and Rpl17 , a ribosomal protein expected to be associated with all translated mRNAs ( Fig . S4 ) . This indicates that Rpb4 is probably associated with NIP1 mRNA , but that the representation of NIP1 mRNA in the pool of mRNAs associated with Rpb4 is not higher than its representation in the pool of total mRNAs . This is in contrast to RPB1 that is more enriched in the immunoprecipitates of Rpb4 than Rpl17 , and significantly enriched in the immunoprecipitate versus total mRNA of Rpb4 , but not Rpl17 . Neither RPB1 nor NIP1 mRNAs were immunoprecipitated from an untagged strain confirming that the immunoprecipitations were specific . Previously not only Rpb4 , but also its partner protein Rpb7 has been implicated in translation and cytoplasmic mRNA degradation [4] , [42] . The Rpb4 and Rpb7 proteins are thought to shuttle between nucleus and cytoplasm as a heterodimer [36] and are believed to act together in mRNA degradation and translation , connecting different stages of gene expression [4] . Consistently , Rpb7 was also detectable in polysome fractions ( Fig . 2A ) . However , its presence in these fractions was not dependent upon Not5 in contrast to that of Rpb4 , and it was less extensively localized in polysomes than Rpb4 ( compare Fig . 2A to Fig . 1C ) . Furthermore , localization of Rpb7 under the stress conditions , which resulted in a mostly cytoplasmic localization of Rpb4 in wild-type cells , was mostly nuclear and was not affected by Not5 ( Fig . 2B ) . These findings suggest that the presence of Rpb7 in the cytoplasm does not follow the same rules as the presence of Rpb4 , neither in the extent of its cytoplasmic localization nor in the Not5-dependence of its polysome association . Moreover , in contrast to Rpb4 , Rpb7 does not display a significant two-hybrid interaction with Not5 ( Fig . S5 ) . The different behavior of Rpb4 and Rpb7 concerning the extent of their polysome association and its dependence upon Not5 led us to study other RNA Pol II subunits in this respect . The other subunits that we investigated , including the two largest subunits , Rpb1 and Rpb2 , as well as the RNA pol II-specific subunit Rpb11 , were present in polysome fractions regardless of the presence or absence of Not5 but dependent upon polysome integrity as defined by polysome disruption with EDTA ( Fig . 3A ) . The exception was Rpb3 , which was not detectable in polysome fractions ( Fig . 3B ) as has previously been described [4] . These unexpected findings led us confirm that RNA Pol II subunits interact with ribosomes . We treated cellular extracts with RNase to disrupt polysomes in a manner distinct from EDTA treatment , and confirmed that loss of the heaviest polysomes resulted in the disappearance of Rpb2 from the heaviest fractions ( Fig . 3C ) . We also immunoprecipitated Rpb2 from cells expressing a tagged ribosomal subunit Rpl25 and could co-immunoprecipitate Rpl25 with Rpb2 ( Fig . 3D ) . The finding that in addition to Rpb4 and Rpb7 , core polymerase subunits are present in polysomes is compatible with reports that RNA Pol II assembly takes place in the cytoplasm , and with the claim that many protein complexes are assembled co-translationally [38] . In addition , since in absence of Not5 Rpb4 fails to localize to polysomes , and does not even accumulate in the cytoplasm , Rpb4 may not assemble optimally with RNA Pol II . To determine whether Rpb4 might dissociate more readily from RNA Pol II in the absence of Not5 , we analyzed Rpb4 complexes from total extracts of wild-type or mutant cells on a native gel ( Fig . 4A ) . An Rpb4 complex of the size of core RNA Pol II ( around 700 kDa ) was detected in both extracts . In addition , many faster migrating forms of Rpb4 were detected , compatible with our knowledge that this subunit readily dissociates from RNA Pol II . However , the extent of these additional smaller complexes was greater in the absence of Not5 . These findings led us to question whether Not5 might be globally affecting RNA Pol II assembly . We looked at complexes of several other RNA Pol II subunits ( Rpb2 , Rpb3 , Rpb9 and Rpb11 ) from wild-type and mutant cell extracts on native gels . In all cases , a complex of the size of core RNA Pol II was observed with the 4 subunits ( Fig . 4B ) as with Rpb4 ( Fig . 4A ) . The 4 subunits were detected in at least one additional smaller complex of a similar size in extracts from not5Δ ( Fig . 4B ) . These two complexes could readily be purified via any of the 4 subunits as shown for Rpb9 on Fig . 4C . Western blotting revealed that the larger complex contains Rpb1 , as expected if it contains core RNA Pol II , but the smaller complex from not5Δ lacks Rpb1 ( Fig . 4C ) . This was also observed for the Rpb2 , Rpb3 and Rpb11 complexes ( Fig . S6 ) . Interestingly , both complexes were sensitive to RNase but not to DNase treatment of the extracts ( Fig . 4D ) suggesting that they could be newly assembled RNA Pol II or assembly intermediates stabilized with RNA rather than RNA Pol II extracted from chromatin or released after transcription . It is notable that RNase treatment of extracts had an impact on very large forms of the polymerase subunits that without treatment tended to remain at the top of the native gels , or did not even enter the gel ( see for instance Rpb11 complexes on Fig . 4B ) in both wild-type and mutant extracts . New large heterogeneous forms of the polymerase subunits ( shown for Rpb11 on Fig . 4D ) were detected in the native gels . It could be that RNase digestion of polysomes released RNA Pol II in forms that could then enter native gels . Taken together , these results suggest that RNA Pol II assembly is co-translational and that Not5 is important for co-translational assembly of RNA Pol II . This model could be confirmed by pulse labeling wild-type and not5Δ cells with 35S-methionine and purifying RNA Pol II via Tap-tagged Rpb2 immediately , and after 1 and 2 h chase . We followed the Rpb1 co-purifying with Rpb2 . This experiment showed delayed association of labeled Rpb1 with Rpb2 and then subsequently delayed chase of this newly labeled Rpb1 in the Rpb2 purification , in the not5Δ strain compared to the wild-type strain ( Fig . S7 ) . The Rpb2 , Rpb3 , Rpb9 and Rpb11 complexes lacking Rpb1 detected in extracts from cells lacking Not5 ( see above ) are reminiscent of an RNA Pol II assembly intermediate that has been described [28] . Its accumulation in not5Δ cells might indicate that the Rpb1 intermediate , with which it should join , is present in limiting amounts in mutant cells . To address this issue , we compared Rpb1 levels in wild-type and mutant soluble extracts . The level of Rpb1 was lower in extracts from not5Δ compared to wild-type , particularly Rpb1 in very large complexes ( Fig . 5A and B ) . Though the difference was not always as dramatic as shown on Fig . 5A and B , it was nevertheless very consistent . We thus compared the half-life of Rpb1 in wild-type and not5Δ cells . No significant reduction of Rpb1 levels after 2 h of protein synthesis arrest was observed suggesting that Rpb1 is not particularly unstable in either wild-type or not5Δ cells ( Fig . 5C ) . This finding is in accord with results of a previous study , which measured Rpb1 levels in wild-type cells up to 4 h after protein synthesis arrest [43] . We obtained a similar result after 8 h of protein synthesis arrest ( Fig . S8 ) . Hence , increased degradation of Rpb1 is unlikely to explain the low levels of Rpb1 in soluble extracts of not5Δ . RPB1 mRNA levels are not significantly altered in not5Δ cells ( [44] and Fig . S4 ) . To determine whether there might nevertheless be a difference in the levels of de-novo synthesized Rpb1 between wild-type and not5Δ that could explain the reduction of Rpb1 in total extracts of the mutant , we performed a pulse-chase experiment and immunoprecipitated Rpb1 from extracts of labeled cells ( Fig . 5D ) . Surprisingly , for a similar pulse , the amount of soluble proteins labeled was generally higher in not5Δ than in wild-type cells . Indeed , the same amount of label was provided by 4 times less total protein from not5Δ than from wild-type ( compare Coomassie panels of Fig . 5D ) . Moreover similar amounts of Rpb1 were immunoprecipitated from 4 times less total protein from not5Δ than from wild-type ( Fig . 5D , IP-Rpb1 upper panel ) , suggesting that immunoprecipitation of Rpb1 was more efficient from not5Δ than from the wild-type . In addition , more of the Rpb1 immunoprecipitated from the mutant was labeled ( Fig . 5D , IP-Rpb1 lower panel ) , indicating that de-novo synthesized Rpb1 was more efficiently immunoprecipitated from the mutant , possibly because there was less unlabeled Rpb1 to compete for antibody binding in the mutant . Finally , the amounts of labeled Rpb1 detected in the immunoprecipitate from wild-type or not5Δ were not much reduced after 60 min of chase under conditions of protein synthesis arrest . Taken together , these results indicate that newly synthesized Rpb1 is mostly stable and not limiting in not5Δ but that it seems to be more accessible to immunoprecipitation in the mutant . Because of the possible differential competing immunoprecipitation of labeled and unlabeled Rpb1 in the 2 strains in these experiments , we could not definitively define whether translation of RPB1 mRNA was altered or not in not5Δ . We thus checked the relative representation of RPB1 and other mRNAs in polysomes of not5Δ relative to the wild-type . We evaluated the level of RPB1 , NIP1 and several other mRNAs in the same amount of RNA prepared from total extracts and polysomes of wild-type and not5Δ . We found no significant difference in levels of RPB1 ( Fig . 5E ) or NIP1 ( Fig . S9 ) mRNAs in total extracts or polysomes between wild-type and not5Δ cells . In contrast , several mRNAs were significantly reduced in polysomes relative to their representation in total extracts in not5Δ compared to the wild-type . For instance levels of RPS8A and GAR1 mRNAs were higher in total extracts but lower in polysomes of not5Δ whilst RPS22A was expressed at equal levels in both strains , but much less present in polysomes of not5Δ ( Fig . 5E ) . In contrast UBP10 mRNA was under-expressed in not5Δ but present at equal levels in polysomes . Taken together , these results indicate that distribution of mRNAs in the translating pool of mRNAs is modified in not5Δ . If neither the stability nor the de novo synthesis of Rpb1 is reduced in not5Δ cells , then why are Rpb1 levels in cellular extracts from these cells reduced ? The majority of soluble Rpb1 in wild-type extracts is present in heterogeneous complexes , much larger than the major Pol II complex that can be purified via the other Pol II subunits ( Fig . 5B ) . These Rpb1 complexes were severely reduced in not5Δ extracts , while the Rpb1-containing complex that was purified via other RNA Pol II subunits ( such as Rpb9 , see above Fig . 4C ) was present in roughly equal amounts in wild-type and mutant cells . These heterogeneous Rpb1 complexes reduced in not5Δ are too large to be mature RNA Pol II , but might include newly synthesized assembly-competent and soluble Rpb1 associated with the Hsp90 chaperone ( Hsp82 and Hsc82 in yeast ) and the R2TP co-chaperone [28] . If such complexes are reduced in not5Δ , one might expect newly produced Rpb1 to fall out of soluble extracts and aggregate . Analysis of total soluble extracts and protein aggregates from wild-type and not5Δ showed that this is indeed the case ( Fig . 6A ) . To confirm these observations , we purified the R2TP complex via its subunits Rvb1 and Rvb2 , from wild-type and not5Δ . The R2TP subunits were expressed at equal levels in both strains , whereas , as expected , the levels of Rpb1 were lower in the mutant ( Fig . 6B , left panel ) . Like Rpb1 , the R2TP subunits were purified much more efficiently from not5Δ strains ( Fig . 6B , right lower panel ) . Nevertheless Rpb1 co-purified with both Rvb1 and Rvb2 from wild-type cells , but much less from not5Δ ( Fig . 6B , right upper panel ) . A similar observation was made when R2TP was purified via the Pih1 subunit ( Fig . S10 ) . Whilst interaction of R2TP with Rpb1 appeared reduced in cells lacking Not5 , in contrast Rpb1 similarly co-immunoprecipitated Hsp90 ( Fig . 6C ) , and we found that Hsp90 , like Rpb1 , accumulated in protein aggregates in not5Δ ( Fig . 6D ) . Expression of a Not5 derivative that carries a nuclear localization signal and complements the slow growth of not5Δ , failed to rescue aggregation of Rpb1 and accumulation of Hsp90 in the aggregates ( Fig . 6D ) , or accumulation of RNA Pol II assembly intermediates ( Fig . 6E , left panel ) , consistent with a role of Not5 in the cytoplasm , at the site of translation . It did however rescue the presence of Rpb4 in polysomes , and it partially rescued polysome levels ( Fig . S11A and S11B ) . In turn , expression of the Not5 derivative that carries a nuclear export signal and that could not rescue accumulation of Rpb4 in the cytoplasm ( see Fig . 1H ) did rescue levels of soluble Rpb1 and RNA Pol II assembly ( Fig . 6E , right panel ) , and it fully recovered wild-type levels of polysomes ( Fig . S11C ) but not presence of Rpb4 in polysomes ( Fig . S11D ) . If R2TP and Hsp90 need to associate with newly produced Rpb1 , one might expect that these proteins are also present at the site of translation . We hence tested for the presence of Rvb1 , Rvb2 and Hsp82 in polysome fractions of extracts separated on sucrose gradients ( Fig . 6F ) . Indeed , all three proteins were detected in heavy fractions containing polysomes . The presence of these proteins in heavy fractions was clearly dependent in part upon polysome integrity , as determined by disrupting polysomes with EDTA ( Fig S1E ) , supporting the idea that these proteins are associated to some extent with polysomes . Interestingly , in not5Δ , while Rvb1 and Hsp82 had sedimentation patterns similar to the wild-type , Rvb2's presence in polysomes was reduced . We also observed that Hsp82 associated significantly with RPB1 mRNA in wild-type cells , but even more so in the absence of Not5 ( Fig . S12 ) . These findings are consistent with a Not5-dependent role in co-translational assembly of R2TP , Hsp90 and Rpb1 . Since Not5 is important for cytoplasmic localization of Rpb4 , we wondered whether this could be indirectly the cause for Not5 relevance in appropriate interaction of Rpb1 with R2TP and expression of assembly-competent soluble Rpb1 . To address this question , we compared Rpb1 levels in wild-type , not5Δ and rpb4Δ and how it might affect complexes of other RNA Pol II subunits . The level of Rpb1 was increased in total extracts of rpb4Δ rather than decreased as in not5Δ and the deletion of Rpb4 had relatively little impact on Rpb9 complexes compared to not5Δ ( Fig . 6G ) . To test whether aggregation of Rpb1 and the role of Not5 could be demonstrated in living cells in higher eukaryotes , we studied Drosophila nurse cells . In these cells active transcription in multiple polythenic nuclei serves the production of the maternal mRNA dowry for the early development of the embryo . We analyzed cells that are heterozygous nulls for RPB2 and should therefore accumulate cytoplasmic Rpb1 in assembly intermediates , and determined the consequence of making such cells heterozygous nulls for CNOT3 ( the ortholog of yeast NOT5 ) . Wild-type cells were used as a control . All cells were stained with antibodies against Rpb1 ( Fig . 7 ) . As expected , the absence of one RPB2 allele led to accumulation of cytoplasmic Rpb1 , whilst in wild-type cells Rpb1 was mostly visible in nuclei . In CNOT3+/− cells , no major difference could be seen compared to wild-type cells . In trans-heterozygotes , however , cytoplasmic speckles , which correspond probably to Rpb1 aggregates , were clearly visible ( Fig . 7 and Fig . S13 ) . These data indicate that the role of Not5 for the association of newly synthesized Rpb1 with the R2TP co-chaperone to protect newly synthesized Rpb1 from aggregation is likely to be conserved from yeast to flies .
In this work we show that the Not5 subunit of the Ccr4-Not complex interacts with the Rpb4 subunit of RNA Pol II , and that Not5 is required for the interaction of Rpb4 with the eIF3 translation factor , for Rpb4 presence in polysomes and globally for Rpb4 cytoplasmic localization , because Not5 is required for Rpb4 association with mRNAs . These findings indicate that Not5 contributes importantly to the linkage of transcription to translation by Rpb4 . The mechanism by which Not5 exerts this effect on Rpb4 is unclear . It has been shown that Not5 is recruited to transcribed ORFs , and that the Ccr4-Not complex can interact with RNA Pol II and impact on elongation ( for review see [6] ) . This interaction was first reported not to require Rpb4 [17] , but this question is now revisited [45] . We find that localization of Not5 to the nucleus can rescue Rpb4 cytoplasmic accumulation . Therefore we imagine that Not5 contributes either to prevent Rpb4 from dissociating from transcribing RNA Pol II so that it can associate with mRNAs at the completion of transcription or to directly promote Rpb4 association with mRNAs . We demonstrate a very strong interaction of Not5 with Rpb4 in the two-hybrid assay . Similarly , we see strong interaction between Rpb4 and Not3 that is 44% similar to Not5 in its N-terminal domain . Hence , one can assume that Rpb4 interacts with the N-terminal domain of Not5 . Consistently , the deletion of this domain of Not5 , when combined with the deletion of Not3 , leads to a temperature sensitive growth phenotype [46] , as does the deletion of Rpb4 [47] . Besides being important for transcription under stress conditions , Rpb4 has also been reported to impact on mRNA export , mRNA degradation and translation [48] , [49] . Not5 shares all of these functions , and moreover a recent study reported that single nucleotide changes in RPB4 or NOT5 correlate with opposite co-evolution of transcription and mRNA degradation rates [25] . Our current study revealing that Not5 is important for cytoplasmic functions of Rpb4 provides now a good explanation for this functional similarity of the two proteins . Rpb4 is believed to fulfill its cytoplasmic function as part of the Rpb4-Rpb7 heterodimer . Our results argue against this idea . First , while the interaction of Not5 with Rpb4 is strong , the interaction between Not5 and Rpb7 is weak . Furthermore , while Not5 is important for the cytoplasmic and polysome localization of Rpb4 , it is not required for these localizations of Rpb7 , which in any event are less prominent than those of Rpb4 . It could be that the roles attributed to Rpb7 in the cytoplasm by the analysis of mutant phenotypes are due to its importance to connect Rpb4 with the core RNA Pol II , since this connection is important for the cytoplasmic functions of Rpb4 [4] , [35] , [36] . The presence of Rpb7 in polysomes , previously argued to indicate a translation function for Rpb7 [4] , might instead be related to cytoplasmic co-translational assembly of RNA Pol II . This issue obviously still needs to be clarified . Our study reveals that Not5 , besides playing a role in the nucleus for the cytoplasmic localization and cytoplasmic functions of Rpb4 , contributes in the cytoplasm to co-translational RNA Pol II assembly . Indeed , we observed that in the absence of Not5 , a complex consisting of Rpb2 , Rpb3 , Rpb9 and Rpb11 but lacking Rpb1 accumulates . This correlates with a critical importance of Not5 for the association of Rpb1 with the R2TP Hsp90 co-chaperone and a tendency of Rpb1 to aggregate in not5Δ cells . Interestingly , in the absence of Not5 , Hsp90 shows increased association to RPB1 mRNA and Hsp90 is detected in the not5Δ aggregates together with Rpb1 . This observation is compatible with a model in which Hsp90 associates with RPB1 mRNA during Rpb1 production , but in the absence of Not5 has a tendency to remain “stuck” to this mRNA . The inefficient formation of the soluble assembly-competent Rpb1 intermediate consistently correlates with an accumulation of an Rpb2 intermediate , with which it joins to form RNA Pol II . Though we did not specifically define the composition of this Rpb2 sub-complex , Gpn1/Npa3 that is known to participate in RNA Pol II assembly and to associate with Rpb2 , and Iwr1 that binds cytoplasmically assembled RNA Pol II for its nuclear import , were present in similar sub-complexes as Rpb2 in not5Δ ( Fig . S14 ) . The importance of Not5 for formation of soluble Rpb1 assembly intermediates does not result from the importance of Not5 for localization of Rpb4 to polysomes . Indeed , the former needs cytoplasmic Not5 that does not rescue presence of Rpb4 in polysomes . Moreover Rpb1 does not aggregate in cells lacking Rpb4 , and in fact is present at enhanced levels in extracts from rpb4Δ . It is interesting to note that the presence of Rpb4 in polysomes does not entirely rescue polysome levels if Not5 is expressed in the nucleus . Inversely , expression of Not5 in the cytoplasm fully rescues polysome levels despite the absence of cytoplasmic Rpb4 . Our findings argue that Not5 is important for translation at least in two different ways: one via its importance in the nucleus to support Rpb4 presence in the cytoplasm and a second one in the cytoplasm for co-translational events . The exact connection between these roles of Not5 in two separate cellular compartments remains to be determined . The importance of Not5 for translation is further exemplified by a change in specific mRNA translatability . The importance of Not5 for co-translational RNA Pol II assembly via production of soluble-assembly-competent de novo synthesized Rpb1 seems distinct from the reported role of the Bud27 prefoldin , since no reduction of soluble Rpb1 was described in cells lacking Bud27 [34] and it is conserved . Indeed , in Drosophila cells , a reduction of the Not5 ortholog , CNot3 , results in accumulation of cytoplasmic Rpb1 in speckles . Our findings raise the question of how Not5 contributes to the interaction of R2TP with newly synthesized Rpb1 , as neither the level of R2TP subunits nor that of newly made Rpb1 is reduced in absence of Not5 . We know that Not5 is present at polysomes and interacts with RPB1 mRNA , and that a component of the Rpb1 assembly complex , in particular Rvb2 , is not detectable in polysome fractions in the absence of Not5 . Our mass spectrometry analysis of proteins co-purifying with Not5 identified both Rvb1 and Rvb2 ( Table S1 ) . Hence Not5 might interact with R2TP subunits and bring them to productively interact with Hsp90 and newly synthesized Rpb1 in ribosomes translating RPB1 mRNA . This is compatible with the role of the Ccr4-Not complex in co-translational quality control that has been suggested by several recent studies ( for review see [50] ) . Rvb1 and Rvb2 are not only components of R2TP but also of several important protein complexes ( for review see [51] ) . We observed that both proteins were more accessible for immunoprecipitation in not5Δ extracts suggesting that R2TP may be globally more accessible in this strain . It could be that this co-chaperone is globally less well associated with its client proteins , not only with Rpb1 . This is compatible with our observation that protein aggregation in not5Δ is quite prominent [21] and does not only concern Rpb1 , and it is compatible with a reduced presence of Rvb2 in polysomes in the absence of Not5 . If true , this would indicate that the cytoplasmic function of Not5 will affect many different protein complexes besides RNA Pol II . At the same time , Not5 in the nucleus , by being critical for the association of Rpb4 with mRNAs and Rpb4 cytoplasmic functions , will globally affect many different cellular components because Rpb4 has wide-spread roles in translation and mRNA decay . Not5 itself is a component of the same complex as the major yeast deadenylase , and it remains to be defined if the cytoplasmic functions determined for Rpb4 are mediated via its interaction with Not5 in the cytoplasm . In any event , taken together our work identifies Not5 as an essential cellular regulator connecting transcription , mRNA degradation and translation in eukaryotic cells .
The Saccharomyces cerevisiae strains used in this work are listed in Table S2 . The plasmid encoding NLS-Not5 ( pJG4-5-NOT5 ) has already been described [46] . To generate a plasmid expressing Myc-tagged Not5 we used pGREG516 [52] and inserted the RPS7A promoter and NOT5 coding sequences by the drag and drop technique leading to pMAC763 . To fuse a NES ( LALKLAGLDI ) at the C-terminus of Not5 we digested pMAC763 with BsiWI and XhoI and amplified NOT5 sequences with a forward primer ( TTT GCC TCA CCC AAC GTC AAT C ) located prior to the BsiWI site and a reverse primer: ( GAG GTC GAC TTA TAT GTC CAA ACC AGC TAA TTT AAG TGC TAA CAG TTT TTC GAA ATC TTC TTC AT ) including a SalI site , a stop codon , the NES sequence and the end of the NOT5 ORF , digested this fragment with BsiWI and SalI and cloned it to the BsiWI and XhoI site of the digested pMAC763 . The plasmid obtained was verified by sequencing . Ribosomes were fractionated on a 12 ml 7–47% sucrose gradient as in [21] . To analyze comparable amounts of polysomes we applied 2 mg of wild-type and 4 mg of not5Δ extracts . The polysomes were disrupted by adding 25 mM EDTA or by 1 µg/ml RNaseA and incubation for 5 min at room temperature . RNasin Plus ( Promega ) at 0 . 2 unit/µl was added to stop the nuclease digestion . RNA was isolated from heavy polysome fractions by the Trizol reagent ( Invitrogen ) following the recommendations of the manufacturer , pellets were washed two times by 75% ethanol to remove sucrose , RNA concentration was measured by nanodrop . These assays were performed as described [53] , [54] . Relevant ORFs were amplified by PCR and cloned into pJG4-5 and pLex202 . The relevant combination of plasmids was transformed into MY290 . Aggregated proteins from total extracts were isolated as previously described [21] . One hundred ml of cells grown exponentially to an OD600 of 0 . 8 were resuspended in 50 ml of medium lacking methionine and incubated for 15 min at 30°C with agitation . Cells were pelleted and resuspended in 5 ml of the same medium and then incubated in the presence of 5 µCi/OD600 unit of 35S methionine for 5 min at 30°C . To stop the labeling reaction , 25 ml of ice cold H2O with ( for Rpb1 immunoprecipitation ) or without ( for Rpb2 purification ) 100 µg ml−1 of CHX was added to the reaction . Both labeling experiments were done in biological duplicates . Cells were pelleted , resuspended in YPD with or without 100 µg ml−1 of CHX and placed at 30°C . 10 ml of cells were pelleted and frozen at 30 and 60 min for Rpb1 immunoprecipitation or at 60 and 120 min for Rpb2 purification , for extract preparation . 5 µl of total extracts at 2 mg/ml were TCA precipitated with 300 µl of ice-cold 25% TCA containing 2% of casamino acids for 30 min on ice . The precipitate was collected by vacuum filtering of 250 µl the TCA reaction mix on Whatman GF/A glass fiber filters . Amino acids were removed by rinsing the filter 3 times with 1 ml of ice-cold 5% TCA . For determination of 35S incorporation into translation products the filter was put into scintillation fluid ( NOCS 104; Amersham ) and counted in a Wallac 1409 liquid scintillation counter . The same amount of labeled protein from each extract in a volume of 800 µl was incubated with 0 . 5 µg anti-CTD antibody and magnetic Protein G beads ( Invitrogen ) for Rpb1 immunoprecipitation or directly with IgG beads for Rpb2 purification , that were pretreated with 200 µl of 5 mg/ml not5Δ total protein extracts in IP buffer ( 40 mM HEPES-KOH pH 8 . 0 , 100 mM KCl , 150 mM potassium acetate , 1 mM EDTA , 10% glycerol and protease inhibitors ) to saturate unspecific binding . For Rpb1 immunoprecipitation , after 3 washes beads were boiled with 50 µl of SB , and for Rpb2 purification the beads were washed additionally 3 times with TEV cleavage buffer and exposed to TEV cleavage as described below . Both preparations were analyzed by western blotting and by Coomassie staining . Coomassie stained gels were dried and revealed by Phosphorimager ( Typhoon Phosphorimager 8600 ) . One hundred milliliter of cells collected at OD600 0 . 8–1 . 0 was treated with CHX ( 100 µg ml−1 ) for 10 min at 4°C and cells were harvested . Single-step affinity purification was done in the presence of CHX ( 100 µg ml−1 ) and 80 units/ml of RNase inhibitor ( RNasine , Fermentas ) . RNase inhibitor and CHX were applied in similar concentrations for all the subsequent steps of the RIP . TEV cleavage was done in IP buffer ( described above ) for 1 h at 30°C . One fourth of the TEV eluate and 25 µg of total protein were subjected to western blotting to verify the affinity purification . 0 . 5 mg of total extracts and the rest of the TEV eluates were treated with phenol-chloroform for nucleic acid extraction . The nucleic acids were precipitated at -20°C with ethanol upon addition of sodium-acetate ( 100 mM ) and 3 µl of linear acrylamide ( Fermentas ) . Pellets were resuspended in H2O and were DNaseI treated ( RQ1 RNase-free DNase , Promega ) . For RIPs , 500 ng of the TEV eluates and 500 ng of the RNA from the total extracts were reverse transcribed , for polysomal and total RNA comparisons 1 µg of RNA were reverse transcribed , with M-MLV RT ( Promega ) using oligo d ( T ) primers according to manufacturer's instructions . In performing the DNase and reverse transcription experiments we followed the manufacturer's instructions . qPCR primers were constructed to amplify approximately 200 bp long fragments close to the polyA tail of the mRNA . As a positive control we used Rpl17-TT to immunoprecipitate all translated mRNAs . Negative control was a wild-type strain without any protein tagged . We conducted qPCR on the reverse transcribed samples . For each 20 µl reaction , 9 µl first strand cDNA solution , 10 µl ABsolute qPCR SYBR Green Mix ( ABgene ) , 0 . 5 µl forward primer ( 10 µM ) and 0 . 5 µl reverse primer ( 10 µM ) were mixed together . PCR parameters were as follows; 95°C , 10 min for heat activation of DNA polymerase mix , followed by 94°C , 15 s ( denaturation ) ; 60°C , 1 min ( annealing and synthesis ) for 38 cycles . Relative enrichment ratios and relative mRNA abundances were determined by the Pfaffl method [55] , and normalized to the total RNA input in the case of the RIPs , and to wild-type RNA levels in the case of the polysome-total RNA comparisons in which NIP1 mRNA was used as a loading control . The primers used are: Rpb1 5′: GTC ACC AAG TTA CAG CCC AAC G; Rpb1 3′: AGA TCC TGG GCT GTA GCC TG; Nip1 5′: AGC TGA TGA GCG TGC TAG AC; Nip1 3′: AGG AAC GAC GAA TGG ATT TTG GAG . Cells were grown to a log phase ( OD600 of 0 . 5 ) , fixed by adding 1 ml of 37% formaldehyde and incubating for 2 h at RT , then pelleted , washed with PBS ( 10 mM sodium phosphate buffer pH 7 . 4 , 137 mM NaCl , 2 . 7 mM KCl ) and resuspended in 0 . 5 ml of spheroplasting buffer ( 20 mM potassium phosphate pH 7 . 4 , 1 . 2 M sorbitol ) . 0 . 2 ml of cell suspension was treated with 3 . 2 µl of 1 . 42 M β-mercaptoethanol and 5 µl of 5 mg/ml zymolyase 100T for 60 min at 30°C . Cells were washed once with 1 ml then resuspended in 100 µl of PBS +0 . 05% Tween 20 . 20 µl of spheroplasts were placed on polylysine coated microscope slides and dried . Slides were washed three times with PBS and cells were blocked in 20 µl of PBS containing 1 mg/ml BSA for 30 min . 20 µl of primary antibody diluted 1∶200 in PBS with BSA was placed on the cells and incubated for 1 h at RT . Cells were washed three times with PBS and incubated for 1 h at RT with secondary antibody diluted 1∶1000 in PBS with BSA . After 3 washes with PBS , cells were treated with 20 µl of 1 µg/ml DAPI in PBS and washed again 3 times with PBS . Glass slides were mounted in 90% glycerol containing PBS and analyzed with fluorescent microscopy using an Axio Vert 200 device supplied with cooled CCD camera or with a Nikon Ti-E motorized inverted microscope system equipped with an Orca-Flash 4 . 0 Digital CMOS camera . The Drosophila P-element insertion lines used for immunofluorescence were obtained from the Bloomington stock center ( stock numbers: 15271 for CNOT3 and 34754 for RPB2 ) . The trans-heterozygous flies were generated by crossing the two different P-element insertion lines . As a wild-type control we used the w1117 strain . For microscopy Drosophila ovaries were fixed in 4% paraformaldehyde . To block nonspecific staining embryos were incubated in 1% BSA ( Sigma ) in PBST ( 0 . 1% ( v/v ) Tween 20 in PBS ) for 120 min at 4°C . Ovaries were incubated with the primary antibodies and 1% BSA in PBST . After several rinses in PBST , ovaries were incubated in secondary antibodies for 3 h at room temperature . To detect DNA , ovaries were stained with DAPI following incubation with the secondary antibody . Following several rinses in PBST ovaries were mounted in Aqua Poly Mount ( Polysciences Inc ) . Optical sections were generated with an Olympus FV1000 confocal microscope . For small scale tandem affinity purifications of RNA Pol II or R2TP 100 OD600 units of cells were broken with 0 . 5 ml of glass beads in 0 . 6 ml of IP buffer during 25 min at 4°C . Beads were washed with 0 . 5 ml of IP buffer . After clarification , 0 . 8 ml of the supernatants containing 4 mg of total protein treated when indicated with 1 µg/ml RNaseA for 5 min at room temperature were incubated with 40 µl of IgG sepharose beads ( GE Healthcare ) . The beads were washed three times with 0 . 2 ml of IP buffer and then 3 times with 0 . 2 ml TEV buffer ( 10 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 0 . 1% Tween 20 , 0 . 5 mM EDTA ) . Beads were incubated for 1 h at 30°C in 40 µl of TEV buffer containing 1 µM DTT and 1 unit of TEV protease ( Invitrogen ) . Beads were sedimented and the supernatant was applied for native 3–12% Bis-Tris gel ( Invitrogen ) analysis or boiled with SDS sample buffer ( SB ) and separated by SDS-PAGE ( 4–12% ) followed in both cases by western blotting . RNA Pol II was single-step purified from 4 mg of total protein obtained from 100 OD600 units of cells . Eluates were concentrated to 25 µl and analyzed by Native PAGE 3–12% Bis-Tris gels ( Invitrogen ) . From total extracts 25 µg proteins were analyzed by Native PAGE . When indicated total extracts were treated either by 1 µg/ml RNaseA for 5 min at RT , or by 20 units/ml of DNase I ( New England BioLabs Inc . ) for 10 min at 37°C . After Native PAGE samples were analyzed by western blotting . Primary antibodies used for western blotting were anti CBP ( Anti-Calmodulin Binding Protein; DAM1411288; Millipore ) used at 1∶5000 , anti HA ( Anti influenza hemagglutinin; H3663; Sigma ) used at 1∶5000 , anti CTD which recognizes Rpb1 and will be referred to in the text as anti Rpb1 ( ab5408; Abcam ) used at 1∶500 , or finally anti PAP ( Peroxidase-Anti-Peroxidase; P1291; Sigma ) used at 1∶10000 and rabbit polyclonal anti Ccr4 which was generated in our laboratory and used at 1∶5000 . The secondary antibodies were anti-Mouse-HRP ( IgG-Peroxidase conjugate; A9044; Sigma ) used at 1∶10000 or anti-Rabbit-HRP ( IgG-Peroxidase conjugate; A8275; Sigma ) used at 1∶10000 . For detection of Rpb1 , ovaries were incubated with 7G5 mouse monoclonal antibody used at 1∶1000 ( a kind gift of Dr . László Tora , Strasbourg ) .
|
In this work we show that , both in the nucleus and in the cytoplasm , Not5 plays a “bridging” role for RNA Polymerase II . In the cytoplasm , Not5 interacts with the mRNA encoding the largest subunit of RNA polymerase II Rpb1 and supports the association of a co-chaperone to newly produced protein , to keep it soluble and assembly competent . In the nucleus , Not5 interacts with the Rpb4 subunit of polymerase that is known to readily dissociate from the rest of the polymerase , and it is essential for Rpb4 to associate with mRNAs at the completion of transcription to contribute to translation and mRNA degradation in the cytoplasm . Hence our data define Not5 as a key player in the cross-talk between different stages of eukaryotic gene expression: Not5 impacts on production of polymerase , hence transcription , during translation , and on Rpb4 mRNA association , hence translation and mRNA degradation during transcription .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"cell",
"biology",
"genetics",
"cell",
"biology",
"biology",
"and",
"life",
"sciences"
] |
2014
|
The Not5 Subunit of the Ccr4-Not Complex Connects Transcription and Translation
|
Human African trypanosomiasis ( HAT ) remains a major neglected tropical disease in Sub-Saharan Africa . As clinical symptoms are usually non-specific , new diagnostic and prognostic markers are urgently needed to enhance the number of identified cases and optimise treatment . This is particularly important for disease caused by Trypanosoma brucei rhodesiense , where indirect immunodiagnostic approaches have to date been unsuccessful . We have conducted global metabolic profiling of plasma from T . b . rhodesiense HAT patients and endemic controls , using 1H nuclear magnetic resonance ( NMR ) spectroscopy and ultra-performance liquid chromatography , coupled with mass spectrometry ( UPLC-MS ) and identified differences in the lipid , amino acid and metabolite profiles . Altogether 16 significantly disease discriminatory metabolite markers were found using NMR , and a further 37 lipid markers via UPLC-MS . These included significantly higher levels of phenylalanine , formate , creatinine , N-acetylated glycoprotein and triglycerides in patients relative to controls . HAT patients also displayed lower concentrations of histidine , sphingomyelins , lysophosphatidylcholines , and several polyunsaturated phosphatidylcholines . While the disease metabolite profile was partially consistent with previous data published in experimental rodent infection , we also found unique lipid and amino acid profile markers highlighting subtle but important differences between the host response to trypanosome infections between animal models and natural human infections . Our results demonstrate the potential of metabolic profiling in the identification of novel diagnostic biomarkers and the elucidation of pathogenetic mechanisms in this disease .
Human African Trypanosomiasis ( HAT ) is caused by infection with either of two subspecies of Trypanosoma brucei . Trypanosoma brucei ( T . b . ) gambiense causes chronic disease ( that can last months or years ) and is endemic in Western Africa while T . b . rhodiesiense causes a more acute illness in Eastern and Southern Africa , which is typically fatal within less than a year of infection if untreated [1 , 2] . Diagnosis is a critical challenge for treatment and control of this disease as patients display an array of non-specific inflammatory symptoms , often indistinguishable from other endemic illnesses such as malaria or enteric fever [1 , 3] . Infection with T . b . gambiense is routinely screened using the Card Agglutination Test for Trypanosomiasis ( CATT ) and also a new generation of lateral-flow rapid diagnostic tests are being deployed based on the host-response to commonly expressed variant surface glycoproteins ( VSG ) [4] . However no such serological approach has been successful for T . b . rhodesiense probably due to the greater diversity of VSG expression . New diagnostic techniques are urgently needed for this disease in order to meet the World Health Organization ( WHO ) target of elimination by 2020 [5 , 6] . In search for novel markers of disease , we have conducted global untargeted metabolic profiling of plasma from rhodesiense trypanosomiasis patients from Uganda and matched controls . Metabolic profiling ( also termed metabonomics/metabolomics ) can be used to characterise biochemical patterns associated with specific physiological and/or pathological states [7] . In the field of parasitic infections , this approach has shown capacity for characterising infection-induced metabolic changes in the host , within infected tissues and systemically ( as measured by plasma or urine profiles ) . As yet , parasitic metabolic profiling studies have largely focused on in vitro assays [8–10] and experimental rodent models [11–15] . There have been very few examples of identifying the metabolic signature of a specific parasitic infection in humans [16] , largely because human profiles are confounded by strong genetic and environmental variation , and are often superimposed upon a background of other concurrent endemic infections . In mice , infection with T . b . brucei ( a subspecies of T . brucei that is not infective to humans ) resulted in augmented plasma levels of lactate , acetylglycoproteins and creatine , and reductions in phosphatidylcholine and lipoproteins , as well as elevated pro-inflammatory cytokines [11 , 17] . Increases in plasma pro-inflammatory cytokines have also been measured in clinical samples [18 , 19] , although metabolic similarities between the mouse model and humans remain to be investigated . In this study we present the first characterisation the metabolic effects of T . brucei rhodesiense infection in humans , and highlight both similarities and differences to results from published mouse model infections with T . brucei brucei . Plasma samples were analysed by proton nuclear magnetic resonance ( 1H NMR ) spectroscopy and reversed-phase ultra-performance liquid chromatography , coupled to mass spectrometry ( RP-UPLC-MS ) . 1H NMR is a highly reproducible method optimal for analysing complex biological mixtures , such as plasma , with minimal sample preparation [20] . Evidence from a range of experimental models have highlighted marked changes in plasma lipids following T . brucei ssp infection , such as hypertriglyceridaemia [11 , 21–23] . Consequently , lipid profiles were separately characterised via UPLC-MS , to provide complementary information on the different lipid species altered in disease . We hypothesised that the metabolic phenotype of individuals with HAT rhodesiense infection would be distinct from that of uninfected individuals , yielding a range of discriminatory markers that may be of diagnostic value .
The human plasma samples in this study were collected under protocols approved by ethics committees in Uganda ( UNCST ) and UK ( North of Scotland Research Ethics Committee ) , conforming to the principles of the Declaration of Helsinki . Ethical consent forms and information sheets were designed in English and translated into local languages . Informed consent was given as a signature or a thumb-print ( as approved by the UNCST ) after verbal explanation . For those aged under 18 , affirmative consent as well as the consent of their legal guardian was obtained . A total of 46 HAT patients and 21 controls were recruited at Lwala Hospital , Kaberamaido District and Serere Health Centre , Serere District in Eastern Uganda , between November 2008 and March 2010 . Controls were healthy individuals who were confirmed as non-infected with either trypanosomes or Plasmodium sp . having undergone parasitological assessment . Study sites , recruitment protocols , treatment regimens , disease progression characteristics and clinical examination methods have been published elsewhere [24] . Patients with intercurrent infections of malaria , filariasis or schistosomiasis were excluded . Plasma and cerebral spinal fluid ( CSF ) samples were collected from all patients prior to treatment as part of normal diagnostic and staging procedures . Staging was carried out in accordance with WHO criteria [25] defining late stage by the presence of parasites in the lumbar CSF and/or a CSF white blood cell count ( WBC ) > 5/μl . Aliquots of 1–2 ml of plasma were immediately frozen and then maintained in liquid nitrogen until transfer to the UK . After air-freight ( 24 hours on dry ice ) samples were maintained at -80°C until analysis . A summary of the parasitological and demographic characteristics of the patients is presented in supplementary S1 Table . All sample preparation was performed in one batch in a randomised order . Prior to acquisition , plasma samples were diluted 1:1 with plasma buffer ( 0 . 142 M NaHPO4 , 2 mM NaN3 , 0 . 08% ( volume/volume i . e . v/v ) 3- ( Trimethylsilyl ) propionic-2 , 2 , 3 , 3-d4 acid sodium salt ( TSP ) solution , 20% ( v/v ) D2O; all SIGMA-Aldrich , Germany ) , and transferred into 5 mm NMR tubes . 1H NMR data were acquired ( over 3 days ) on a Bruker Avance 600 MHz ( 14 . 1 T ) NMR Spectrometer with BB probe head and refrigerated SampleJet autosampler ( Bruker , Germany ) , using Topsin ( 3 . 1 ) software ( Bruker BioSpin , Germany ) . Samples were acquired at 300 K ( ~27°C ) using a standard one-dimensional pulse with water suppression program for general metabolite screening [26]; sequence as follows: 2 second ( s ) relaxation delay ( RD ) – 90° pulse– 4 μs delay—90° pulse– 100 μs mixing time– 90° pulse– 2 . 73 s acquisition ( ACQ ) of free induction decay ( FID ) signal . Number of dummy scans ( ds ) was 4 , and number of acquired FID’s ( ns ) was 64 . To enhance the visualisation of low molecular weight molecules , a Carr-Purcell Meiboom-Gill ( CPMG ) spin-echo pulse spectrum was also acquired for each sample [26] , with the following pulse sequence: 2 s RD– 90° pulse– ( τ– 180°–τ ) n-ACQ , where spin echo time τ = 300 μs , number of loops n = 128 , total spin echo time 2nτ = 76 . 8 ms , ds = 4 and ns = 64 . All FID’s were multiplied by an exponential function with a 0 . 3 Hz line broadening prior to Fourier transformation into spectral data ( spectral width of 18 kHz collected over 131 , 072 data points ) . Spectra were processed using automatic phasing , baseline correction and calibrated to the TSP peak via an in-house algorithm in MATLAB ( version R2013a , Mathworks Inc . ) and Topspin . The residual water signal was removed prior to automatic spectral alignment [27] , and probabilistic quotient normalisation [28] , using an in-house MATLAB script . Additionally , artefactual peaks arising from EDTA buffer [29] present in blood collection tubes were removed from all plasma spectra for final analysis . The regions removed were 2 . 53–2 . 62 , 2 . 7–2 . 735 , 3 . 08–3 . 256 , 3 . 60–3 . 66 ppm which may have resulted in the loss of some endogenous metabolic information . Spectral assignments of metabolite peaks were identified using Chenomx NMR suite profiler 8 . 1 software and known assignments from in-house NMR databases and literature [30 , 31] . Due to sample limitations , additional aliquots of only a subset of plasma samples were used for lipid profiling ( n = 30 , see supplementary S1 Table ) . As for NMR , sample preparation for UPLC-MS was performed in one day in one batch , in a randomised manner . Samples underwent protein precipitation using isopropanol , as described by Sarafian et al , 2014 [32] . Briefly , 50 μl plasma was mixed 1:3 ( v/v ) with ice-cold isopropanol ( SIGMA-Aldrich ) , incubated for 10 mins at room temperature , and then overnight at -20°C . Samples were then centrifuged at 20 , 817 x g ( 14 , 000 rpm ) at 4°C for 20 mins and 100 μl of the supernatant ( subsequently referred to as test samples ) were transferred to MS vials ( Waters Corp . , UK ) for analysis and placed in the auto-sampler ( kept at 8°C ) . Additional aliquots from each plasma-isopropanol sample were pooled to generate a quality control ( QC ) master sample . The QC was injected before starting the run to optimise the injection volume and to condition the column , and then injected every 8 samples for assurance of a stable run . Lipid profiling was performed on an Acquity UPLC system ( Waters Ltd . Elstree , UK ) coupled to a Q-TOF Premier mass spectrometer ( Waters Ltd . , Manchester , UK ) . Mass spectra for all samples were acquired continuously , firstly in positive and then negative ion electrospray ( ESI+ and ESI- ) modes , over the course of 3 days . The separation conditions for reversed-phase UPLC have been previously established [33 , 34] . Gradient elution was performed using a CSH C18 ( 1 . 7 μm , 2 . 1 x 100 mm ) column ( Waters Corporation , Milford , USA ) kept at 55°C . The mobile phases consisted of 0 . 1% ( v/v ) formic acid and 10 mM ammonium formate in 60:40 ( v/v ) acetonitrile ( ACN ) /HPLC-grade water ( mobile phase A ) and 0 . 1% formic acid ( v/v ) and 10 mM ammonium formate in 90:10 ( v/v ) IPA/ACN ( mobile phase B ) at a flow rate of 0 . 4 ml/min ( all solvents purchased from SIGMA-Aldrich except water which was from Fisher , Germany ) . The sample injection volume was 5 μl and 10 μl for ESI+ and ESI- , respectively . The MS parameters were set as follows: capillary voltage , 3 kV ( ESI+ ) and 2 . 5 kV ( ESI- ) ; sample cone voltage , 30 V ( ESI+ ) and 25 V ( ESI- ) ; source temperature , 120°C; desolvation temperature , 400°C; desolvation gas flow , 800 L/h and cone gas flow , 25 L/h . For mass accuracy , a 0 . 2 ng/μl leucine encephalin ( mass/charge ratio ( m/z ) 556 . 2771 in ESI+ and m/z 554 . 2615 in ESI− ) solution at 20 μl/min was used as the lock mass . Data were collected in centroid mode with a scan range of 50–1200 m/z , with lock mass scans collected every 30 seconds and averaged over 3 scans to perform mass correction . Additionally , data-dependent acquisition ( DDA ) of the QC sample was performed for structural elucidation for each ionisation mode . Chromatograms and spectra were displayed using MassLynx ( version 4 . 1; Waters Corp . ) . The MassLynx package ‘Databridge’ was used to convert the data files into NetCDF format . The package XCMS ( R software ) and an in-house developed script was used for the pre-processing of the data ( peak-picking , alignment , grouping , zero filling , and median-fold change normalisation ) [35] . Integrals of metabolic features with a coefficient of variation ( CV ) greater than 30% in the QC samples ( n = 6 ) were removed in Microsoft Excel ( 2013 , Microsoft , USA ) prior to analysis . Metabolic data from plasma 1H NMR spectra and lipid profiling UPLC-MS data were initially analysed in SIMCA software ( V . 13 . 0 , Umetrics ) via Principal Component Analysis ( PCA ) [36] , a non-supervised descriptive model used to investigate inherent similarities/differences across the data set and identify outliers . Analyses resulted in the removal of one NMR sample with an acquisition artefact thus bringing the final total to n = 66: 45 HAT and 21 controls ( see S1 Table ) . PCA was additionally used to identify and remove contaminant/artefactual features , as described by Vorkas et al , 2015 [34] , and to evaluate experimental reproducibility by assessing QC sample clustering for both ESI+ and ESI- runs . This was followed by Partial Least Squares Discriminant Analysis ( PLS-DA ) , and Orthogonal PLS-DA ( O-PLS-DA ) [37] . Both are multivariate regression models used to assess the spectral differences that contribute towards classification of patients vs . controls , and to determine how well these differences can predict class separation . Contribution of within-class variation that is uncorrelated to the separation between classes is minimised in O-PLS-DA models , which were used for final extraction of discriminating metabolites . See supplementary protocols in supporting information ( S1 File ) for further details on data processing , analyses , and model validation . For NMR data , metabolites integrals were then compared between HAT patients and controls via the two-tailed Welch’s ( unequal variance ) T-test in Microsoft Excel , followed by Benjamini-Hochberg multiple test correction [38] . Those where p<0 . 05 was observed were labelled as statistically significant discriminatory markers . For UPLS-MS data , threshold criteria for initial selection of markers were variables with a correlation value p ( corr ) [1]>0 . 5 and covariance p[1]>0 . 05 , as visualized on the OPLS-DA S-plot , to extract the most discriminatory and robust features ( see supporting protocols ) . This was followed by conducting a two-tailed Welch’s T-test with Benjamini-Hochberg correction , whereby the final statistical discriminatory criteria was also p<0 . 05 after multiple testing correction . Lipid species assignment of discriminatory features was based on the accurate mass-to-charge ( m/z ) ratio , searched against online MS database METLIN ( https://metlin . scripps . edu ) and previous published results generated in-house [34 , 39] , as well as on the MS/MS information obtained in the DDA experiments of QC samples . Integrals of these discriminatory variables were re-imported into SIMCA as new loadings for O-PLS-DA models , to evaluate the sensitivity and specificity of the combination of these metabolites for biomarker potential . O-PLS-DA models was performed twice , separately for NMR and UPLC-MS data . Firstly , models were generated using integrals of all significantly discriminatory markers , and secondly using only integrals of the top 5 discriminating markers , defined as those which displayed the greatest % difference between patients and controls . Calculations are further explained in supplementary protocols ( see Supporting Information S1 File ) . To explore links between potentially complementary metabolic datasets , NMR metabolite integrals were correlated with corresponding integral of discriminatory lipid profiling features from matching patient samples ( n = 16 ) , via Pearson correlation in MATLAB , followed by Benjamini-Hochberg correction . Similarly Pearson -based correlation analyses ( with multiple test correction ) was performed between metabolic ( NMR and/or MS ) data sets . Relationships between patient demographic data and metabolic datasets were assessed using Spearman correlation analysis , Mann-Whitney tests , and mixed linear models as appropriate , in JMP ( v . 10 . 0 , SAS , USA ) .
Inherent differences between T . b . rhodesiense infected HAT patients and uninfected controls dominated overall dataset variability , demonstrated by the partial separation of these two populations along the first component in PCA ( Fig 1A ) , indicative that the HAT infection produced a strong metabolic response . No significant differences between metabolic profiles based on demographic factors ( gender , age , or diagnostic stage ) were found . Metabolic disparities between patients and controls were further examined using O-PLS-DA ( a supervised modelling method that maximises differences between classes whilst minimising within-class variation ) , which showed complete segregation between patients and controls , also along the first component ( Fig 1B ) . The model had a Q2Y ( model predictive capability parameter ) value of 0 . 708 , indicating that the model was robust . Further validation through permutation testing found that the likelihood of achieving the same discriminatory result by chance was zero ( p = 0 for both R2Y and Q2Y ) . It is noteworthy that although the first two components of the PCA model only cumulatively account for 18 . 1% of the total variance ( R2 ) in the data set , indicating variability in sample composition , the O-PLS-DA model indicates that the metabolic signature is robust . Examination of the corresponding O-PLS-DA loadings revealed that the lipid peaks accounted for the majority of the discrimination . In total , 34 endogenous metabolites were identified from NMR spectra though only 31 of these displayed distinct peaks that were used for integral calculation ( signals from isoleucine , leucine and lysine were superimposed by overlapping lipid peaks ) , and compared between patients and controls ( shown in S2 Table ) . Those metabolites that were significantly higher in disease are shown in Fig 2A , whilst metabolites significantly lower in disease are shown in Fig 2B . The majority of lipid moieties detected in plasma were significantly reduced in patients , including those containing unsaturated bonds ( CH = CH and C = CCH2C = C ) , lysyl group in plasma albumin , and cholesterol ( Fig 2B ) . In contrast , lipid glyceryl and ketene ( CH2CO ) groups were significantly higher in patients than in controls ( Fig 2A ) . Several plasma amino acids were also significantly discriminatory between HAT and controls , including histidine and alanine , which were found in lower concentrations in patients , and phenylalanine , which was augmented . Other significant changes included an increase of creatinine , N-acetyl glycoprotein , formate and myo-inositol in HAT patients whilst citrate was higher in controls . No associations between discriminatory metabolites and patient demographic data ( age , gender or diagnostic stage ) were found though the sample size in this study limited the power of this analysis ( this was particularly the case for staging as our cohort had relatively few early cases—see S1 Table ) . Additional O-PLS-DA models were built , firstly using integrals of all 16 discriminatory metabolites , followed by models using only the top 5 metabolites . The latter were defined as those which displayed the biggest measured percentage difference between HAT and controls , representing metabolites of most practical diagnostic potential . These consisted of phenylalanine , formate and lipid moieties CH2CO , CH = CH , and glyceryl . In both O-PLS-DA models , results provided identical specificity scores for classifying patients and controls samples , namely 95 . 24% ( with 95% confidence intervals [CI] of 86 . 1–100% ) . Model sensitivity was 88 . 89% ( 79 . 7–98 . 1% CI ) using all NMR discriminatory markers , and 84 . 44% ( 73 . 9–95 . 0% CI ) using just the top 5 metabolites ( see S3 Table for confusion matrices ) . Lipid plasma profiles were further examined using reversed-phase UPLC-MS . In total , 8771 metabolic features were detected in positive ionisation mode ( ESI+ ) , whilst 2543 features were observed in negative mode ( ESI- ) . Following the exclusion of peaks with CV > 30% across the QC’s , as well as the removal of contaminant peaks present in either QC’s or blank controls , 6952 features in ESI+ and 1714 features in ESI- were retained for final analyses . PCA models showed tight clustering of QC samples compared with the remaining test samples ( supplementary information S1 Fig ) , assuring the robustness of the MS experimental runs . As noted with NMR , lipid profiling results showed an inherent separation between the two classes already in the first two components of PCA , observed in both ionisation modes ( see S1 Fig ) . These differences were confirmed in the corresponding O-PLS-DA models ( S1 Fig ) . Permutation testing confirmed model robustness ( ESI+ , p = 0 . 004 for R2Y and p = 0 for Q2Y and for ESI- , p = 0 for both R2Y and Q2Y ) . Metabolic features responsible for class discrimination ( i . e . HAT patients vs . controls ) were visualised in S-plots in SIMCA , with the cut-off values of p ( corr ) [1]>0 . 5 and covariance p[1]>0 . 05 were used as selection criteria ( see Methods ) , shown in Fig 3A and 3B . Integrals of selected discriminatory features highlighted in the S-plots are shown in Fig 3C and 3D , demonstrating significant decreases in lysophosphatidylcholines 16:0 and 18:0 , as well as increases in phosphatidylcholine 32:0 and 34:1 . Overall , lipid profiling analysis identified 37 unique features that were statistically different between HAT and controls , 12 of which were found to be significantly different in both of these modes ( Table 1 ) . Features included six lysophosphatidylcholines ( LysoPCs 16:0 , 18:0 , 18:1 , 18:2 , 20:3 and 20:4 ) , four phosphatidylcholines ( PCs 32:0 , O-34:1 , 38:3 and 38:5 ) and two sphingomyelin species ( SMs d40:1 and d41:1 ) . Further information on UPLC-MS assignments of features can be found in S4 Table . The majority of lipid classes studied were glycerophospholipids ( LysoPCs and PCs ) , followed by sphingolipids ( mainly sphingomyelins ) and triglycerides ( see Table 1 and S4 Table ) . Free fatty acids were detected using our MS methodology but did not significantly differ between patients and controls . In total , six significantly altered lysoPCs measured in this study were found to be lower in patients than controls . Significantly lower intensities were also observed for eight out of nine sphingolipids , including seven sphingomyelins ( SM ) as well as nervonic ceramide ( Cer d42:2 ) . Only SM d34:1 , the smallest of the analysed SM species , was found to be higher in patients than controls . Additionally , patients had augmented levels of six triglycerides ( TG ) species and depleted levels of cholesterol ester ( CE ) 18:2 in patients , in line with NMR findings where we detected higher glyceryl moieties and lower cholesterol in HAT . In general , differences in lipid species concentrations between patients and controls were similar across the lipid subclasses , with the exception of the phosphatidylcholines . In contrast , our UPLC-MS results indicate that PCs that contained a high number of saturated bonds were significantly decreased in patients ( e . g . PCs 38:5 , 38:6 , 40:5 ) , whilst those with two or fewer unsaturated bonds were increased ( e . g . PCs 32:0 , 32:1 , and 34:1 , see Table 1 ) . These results mirror the NMR findings , where lipid moieties with unsaturated bonds ( CH = CH and C = CCH2C = C ) were significantly lower in patients than controls ( Fig 2 ) . Similar to the NMR data , additional O-PLS-DA models were generated using the integrals all 37 markers from both ionisation modes , followed by further models using only the top 5 features with the greatest % difference ( as shown in Table 1 ) . As most of the largest changes were observed in the positive mode , we selected the feature from this ionisation mode only . These included PC ( 34:3 ) , PC ( 32:0 ) , TG ( 51:2 ) , PC ( O-34:1 ) and SM ( d34:1 ) . Both O-PLS-DA models with either all markers or top 5 gave identifical sensitivity and specificity scores , namely 93 . 75% ( 81 . 9–100% CI ) and 85 . 71% respectively ( 67 . 4–100% CI; see S5 Table for confusion matrices ) . Potential links between UPLC-MS and NMR data was explored by correlating lipid metabolic features with NMR metabolite levels measured via NMR . Whilst multiple trends were observed , including links between lipid MS species and several NMR lipid moieties , 3-hydroxybutyrate ( a ketone body generated from fatty acid break-down ) , and energy associated metabolites , e . g . creatinine and lactate ) , none of these associations remained statistically significant after Benjamini-Hochberg multiple test correction was performed , due to lack of statistical power . No significant links were observed between MS data and age and/or gender data ( analyses could not be performed for diagnostic staging due to limited numbers of early stage within this subset of data—see S2 Table ) .
The HAT infection was characterised by strong metabolic differences in plasma profiles , based on both NMR and UPLC-MS , which shows the potential of this approach in the development of novel diagnostic marker sets . The discriminatory profile was largely driven by variation in lipids and amino acids . Phenylalanine was the single most discriminatory metabolite as detected by NMR , with the largest percentage difference between HAT and controls in concentration . Raised levels of phenylalanine have been frequently linked with infection and inflammation [40] . Phenylalaninemia has previously been reported in patients with enteric fever [41] , and with malaria [42] Evidence suggests that this phenomenon may be related to neurological effects seen in cerebral malaria [42] , since high levels of phenylalanine are known to be neurotoxic [43] . Other differences in plasma amino acid concentrations included significant decreases in histidine and alanine in HAT . Histidine concentrations have been shown to be negatively correlated with inflammatory disease markers in humans [44–46] and this would be consistent with clinical evidence of systemic inflammatory responses in HAT [3] . Creatinine concentration was also found to be significantly higher in HAT , potentially a sign of renal dysfunction which is one of the clinical features observed in sleeping sickness [47 , 48] . Additionally , concentrations of N-acetyl groups of plasma glycoproteins were also higher in patients . Most of these have been shown to belong to α1 acid glycoprotein [49 , 50] , another inflammatory marker known to be associated with the acute-phase response . This result is similar to what has been found with experimental murine infection of T . b . brucei where plasma acetylated glycoproteins ( dominated by O-acetyls ) were found to be augmented [11] . Some of the most pronounced changes observed in the NMR spectra were the clear disparities in the plasma lipid composition between the two groups . In addition to their roles as energy reserves and major constituents of membranes , lipids also represent highly biologically active metabolites , with involvement ( or function ) in signalling and a range of inflammatory processes , e . g . prostaglandins and leukotrienes synthesis [51] . Our results showed that the majority of the concentrations of discriminatory lipids were significantly reduced in patients . These primarily included lipids containing unsaturated bonds ( CH = CH and C = CCH2C = C ) , CH3 and CH2CH2CO moieties , and albumin lysyl groups , and high-density lipoprotein ( HDL ) cholesterol . Dyslipidaemia has been observed in a range of infections , measured primarily using lipid-specific biochemical serum assays , as opposed to comprehensive metabolic profiling , whereby significantly reduced HDL and total cholesterol have been reported in malaria [52] , enteric fever [53] and others [54 , 55] . Decreased lipoprotein CH3 moieties were also observed in experimental murine T . b . brucei infection [11] , confirming the need for deeper assessment into the variety of lipid species , to help dissect their different roles during infection . Further investigation of the lipid profile with UPLC-MS indicated that HAT patients displayed significantly lower levels of polyunsaturated phosphatidylcholines ( e . g . PC 40:6 , 40:5 , 38:5 and 38:3 ) , although PC molecules with 2 or fewer unsaturated bonds were in contrast increased . This profile presents a more complex picture than was obtained in experimental mouse infections with T . b . brucei , where an overall decrease in plasma PC concentrations was measured by NMR [11] . A range of lysophophatidylcholines ( e . g . LysoPC 16:0 , 18:0 , 18:2 ) intensities was also significantly lower in patients . Despite the fact that Trypanosoma brucei ssp have the capability to produce many of their lipids de novo , they are known to scavenge several lipid precursor molecules from the host and have been shown to require lipid uptake for optimal growth [56 , 57] . More specifically , these include fatty acids and lyso-phospholipids , which are assembled to make phospholipids , the most dominant lipid component of T . brucei ssp [58] . As fatty acyl chain composition in T . b . rhodesiense bloodstream forms have a high proportion of long-chain polyunsaturated fatty acids [59 , 60] , it could be hypothesised that the lower plasma concentrations of polyunsaturated lipids in HAT patients may be due to increased uptake by the parasites . Numerous sphingomyelin species were also found in lower concentrations in infection ( e . g . SM d40:1 , d41:1 and d42:1 ) , accompanied by a significant increase of ceramide . The sphingolipid turnover is regulated by the balance between enzymatic degradation and synthesis , with ceramide being a major break-down product . Oxidative stress , inflammatory cytokines and infection can trigger sphingomyelinase enzymes to increase ceramide generation . Higher levels of ceramide , in turn , have been linked to increased cell autophagy and apoptosis ( the latter particularly in endothelial cells leading to increased vascular permeability ) , additional pro-inflammatory cytokine and chemokine synthesis , and other metabolic disorders ( e . g . insulin resistance and obesity ) [61] . Thus , the changes in sphingolipids observed in HAT further highlights the widespread inflammatory effects on the host , associated with T . b . rhodesiense infection . Another characteristic of HAT patients was hypertriglyceridaemia . Not only is this result is in agreement with a range of rabbit and non-human primate models of T . brucei spp infections [21 , 22] , but is also consistent with marked increases in serum triglycerides in T . b . gambiense HAT [62] . However , the disease discriminatory potential of hypertriglyceridaemia ( often combined with increases in circulating free fatty acids ) will require further investigation as it has been shown to be a common feature across numerous infections in humans [52–54] . In summary , the presented work is the first to apply a comprehensive metabolic profiling approach to the investigation of Trypanosoma brucei infection in humans and highlights the potential of metabonomic technology for developing a diagnostic platform for HAT . The models generated based on both NMR and UPLC-MS were robust , thus the utility of this technology in an exploratory capacity is evident . Similarities but importantly significant differences in discriminatory NMR metabolites were identified to previously published metabolic profiles in experimental rodent T . brucei spp . infections . We identified a range of discriminatory plasma metabolites in clinical HAT that collectively have diagnostic disease marker potential ( up to 93 . 75% sensitivity and 95 . 24% specificity ) . While some of these markers have been associated with other infections or inflammatory processes , the effects of specific infection on a combined panel such as in this study has not previously been reported and may prove diagnostically effective in differentiating HAT from other endemics disease . However , it is obvious that neither NMR spectroscopy nor UPLC-MS are platforms that can be used at point-of-care in developing countries . Therefore the functionality of these platforms lies in characterisation of the metabolic consequences of a parasitic infection , with subsequent development of simpler clinical assays such as a biochemical dipstick based on the strongest differentiating biomarkers . Of particular interest from our results were differences in lipids , observed in both NMR and UPLC-MS , that dominated the variation in metabolic profiles between HAT and controls . Further investigation of inflammatory lipid mediators such prostaglandins and leukotrienes would also be of particular interest , to better understand pathology and provide key insights into links between lipid metabolism and immune responses in the host . Detailed lipid sub-species characterisation with the use of lipid reference standards and MS/MS are required to further isolate novel diagnostic candidates to ultimately aid in the detection and management of T . b . rhodiesiense HAT , moving closer towards the WHO goal of disease eradication .
|
Metabolic profiling of biofluids and tissues in disease and healthy individuals is a powerful approach to discover new markers for diagnosis . We have applied these techniques to the protozoan infection human African trypanosomiasis ( HAT ) , otherwise known as sleeping sickness . The form of HAT endemic in East Africa , caused by Trypanosoma brucei rhodesiense , requires technically demanding direct microscopic diagnosis , as there are no indirect rapid diagnostic tests available . We studied the metabolite profiles in plasma from HAT patients and controls . Clear biochemical differences were discovered between control individuals and patients , including changes in the overall lipid composition and concentration of certain amino acids . These may have been caused by the inflammatory immune response to infection and the uptake of particular molecules by the parasites , although further research will be required for confirmation . We demonstrate that plasma metabolic profiles are characteristic for T . b . rhodesiense infection . While some of these changes are consistent with those observed in an experimental mouse infection model of HAT , many are unique to this clinical study and indicate the necessity of validating experimental animal study data in clinical disease studies . Our results also reveal biochemical changes in patients that will help us understand the development of disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Discovery of Infection Associated Metabolic Markers in Human African Trypanosomiasis
|
Recognition of viral RNA structures by the intracytosolic RNA helicase RIG-I triggers induction of innate immunity . Efficient induction requires RIG-I ubiquitination by the E3 ligase TRIM25 , its interaction with the mitochondria-bound MAVS protein , recruitment of TRAF3 , IRF3- and NF-κB-kinases and transcription of Interferon ( IFN ) . In addition , IRF3 alone induces some of the Interferon-Stimulated Genes ( ISGs ) , referred to as early ISGs . Infection of hepatocytes with Hepatitis C virus ( HCV ) results in poor production of IFN despite recognition of the viral RNA by RIG-I but can lead to induction of early ISGs . HCV was shown to inhibit IFN production by cleaving MAVS through its NS3/4A protease and by controlling cellular translation through activation of PKR , an eIF2α-kinase containing dsRNA-binding domains ( DRBD ) . Here , we have identified a third mode of control of IFN induction by HCV . Using HCVcc and the Huh7 . 25 . CD81 cells , we found that HCV controls RIG-I ubiquitination through the di-ubiquitine-like protein ISG15 , one of the early ISGs . A transcriptome analysis performed on Huh7 . 25 . CD81 cells silenced or not for PKR and infected with JFH1 revealed that HCV infection leads to induction of 49 PKR-dependent genes , including ISG15 and several early ISGs . Silencing experiments revealed that this novel PKR-dependent pathway involves MAVS , TRAF3 and IRF3 but not RIG-I , and that it does not induce IFN . Use of PKR inhibitors showed that this pathway requires the DRBD but not the kinase activity of PKR . We then demonstrated that PKR interacts with HCV RNA and MAVS prior to RIG-I . In conclusion , HCV recruits PKR early in infection as a sensor to trigger induction of several IRF3-dependent genes . Among those , ISG15 acts to negatively control the RIG-I/MAVS pathway , at the level of RIG-I ubiquitination . These data give novel insights in the machinery involved in the early events of innate immune response .
IFN induction in response to several RNA viruses involves the intracytosolic pathogen recognition receptor ( PRR ) CARD-containing DexD/H RNA helicase RIG-I . Following its binding to viral RNA , RIG-I undergoes a change in its conformation through Lys63-type ubiquitination by the E3 ligase TRIM25 . This allows its N-terminal CARD domain to interact with the CARD domain of the mitochondria-bound adapter MAVS [1] , [2] . MAVS then interacts with TRAF3 to further recruit downstream IRF3 and NF-κB-activating kinases , that stimulate the IFNβ promoter in a cooperative manner . In addition , IRF3 stimulates directly the promoters of some interferon-induced genes ( early ISGs ) while NF-κB stimulates that of inflammatory cytokines [3] . The RNA of Hepatitis C virus ( HCV ) has an intrinsic ability to trigger IFNβ induction through RIG-I [4] , [5] , [6] . Yet HCV is a poor IFN inducer . One reason for this comes from the ability of its NS3 protease to cleave MAVS [7] . Another relates to the ability of HCV to trigger activation of the dsRNA-dependent eIF2α kinase PKR [8] , [9] which leads to inhibition of IFN expression through general control of translation while the viral genome can be translated from its eIF2α-insensitive IRES structure [8] . HCV infection can trigger important intrahepatic synthesis of several IFN-induced genes ( ISGs ) in patients [10] , [11] and in animal models of infection in chimpanzees [12] . Expression of ISGs can be explained at least in part by the ability of HCV to activate the IFN-producing pDCs in the liver through cell-to-cell contact with HCV-infected cells [13] . Intriguingly , despite the recognized antiviral activity of a number of these ISGs , their high expression paradoxically represents a negative predictive marker for the response of these patients to standard combination IFN/ribavirin therapy [14] , [15] , [16] . The ubiquitine-like protein ISG15 is among the ISGs which are the most highly induced by HCV [16] and was recently shown to act as a pro-HCV agent [17] . Interestingly , ISG15 was also shown to control RIG-I activity through ISGylation [18] . Here , we show that HCV controls IFN induction at the level of RIG-I ubiquitination through the ubiquitine-like protein ISG15 , one of the early ISGs . Use of small interfering RNA ( siRNA ) targeting to compare the effect of ISG15 to that of PKR on IFN induction and HCV replication led to the unexpected finding that HCV infection triggers induction of ISG15 and other ISGs by using PKR as an adapter through its N terminal dsRNA binding domain . This recruits a signaling pathway which involves MAVS , TRAF3 and IRF3 but not RIG-I . Altogether , our results present a novel mechanism by which HCV uses PKR and ISG15 to attenuate the innate immune response .
We recently reported that the HCV permissive Huh7 . 25 . CD81 cells [19] that we used to identify the pro-HCV action of PKR , did not induce IFN in response to HCV infection , unless after ectopic expression of TRIM25 [8] . We started this study by investigating at which level this defect could occur . A P358L substitution in the endogenous TRIM25 of these cells , revealed by sequence analysis , proved to have no incidence of the ability of TRIM25 to participate in the IFN induction process . Indeed , ectopic expression of a TRIM25 P358L construct was as efficient as a TRIM25wt construct to increase IFN induction in the Huh7 . 25 . CD81 cells , after infection with Sendai virus ( SeV ) ( Figure 1A ) . Like some other members of the TRIM family , TRIM25 is localized in both the cytosol and nucleus and is induced upon IFN treatment [20] . No specific difference between the cellular localization of TRIM25 was observed in the Huh7 . 25 . CD81 cells when compared to Huh7 cells or Huh7 . 5 cells , which rules out a role for a cellular mislocalization in its inability to participate in IFN induction ( Figure 1B ) . TRIM25 was also efficiently induced by IFN ( Figure 1B and Figure S1 ) . We assayed whether increasing TRIM25 upon IFN treatment could mimic the effect of its ectopic expression and restore IFN induction in response to HCV infection . However , this resulted only in a poor stimulation of an IFNβ promoter ( 3 to 5-fold ) , in contrast to its effect upon SeV infection ( 230-fold ) ( Figure 1C ) . Similarly , HCV infection at higher m . o . i , as an attempt to favour recognition of RIG-I by the viral RNA , only modestly increased IFN induction ( Figure 1D ) . TRIM25 plays an essential role in IFN induction through RIG-I ubiquitination [1] . We then analysed whether this step was affected by HCV infection in the Huh7 . 25 . CD81 cells . The results showed that , in contrast to SeV infection used as control , HCV infection could not trigger RIG-I ubiquitination , unless the cells are supplied with ectopic TRIM25 ( Figure 1E ) . Thus , HCV infection appears to mediate a control on IFN induction through regulation of RIG-I ubiquitination . Inhibition of the function of TRIM25 or RIG-I ubiquitination has been suggested to occur via the small ubiquitin-like protein ISG15 and the process of ISGylation [18] , [21] . We then analysed whether ISG15 was involved in the control of RIG-I ubiquitination upon HCV infection . For this , we chose a transient transfection approach using siRNAs targeting ISG15 in the Huh7 . 25 . CD81 cells . Indeed , this resulted in a strong ubiquitination of RIG-I at 9 hrs and 12 hrs post-HCV infection , which was equivalent to that observed in cells supplied with ectopic TRIM25 ( Figure 2A ) . A similar result was obtained after JFH1 infection in the Huh7 cells , used as another HCV-permissive cell line ( Figure S2 ) . Thus , ISG15 can control RIG-I ubiquitination in different cells infected by HCV . We next investigated whether ISGylation was involved in this process . Absence of detection of RIG-I ubiquitination after HCV infection of the Huh7 . 25 . CD81 cells precludes direct analysis of the effect of ISG15 on RIG-I . We used an IFNβ-luc reporter assay instead , as it proved to be sensitive enough to detect some IFN induction in response to JFH1 infection in those cells ( see Figure 1D ) . We found that IFN induction increased when cells were transfected with siRNAs targeting ISG15 while it decreased in cells overexpressing IGS15 ( Figure 2B ) . Expression of ISG15 in the presence of the E1 , E2 and E3 ligases involved in ISGylation ( respectively Ube1L , UbcH8 and HERC5 ) [22] further inhibits IFNβ induction ( Figure 2B ) . Similar results were observed upon infection with Sendai virus ( Figure S3 ) . The ISGylation process is strictly dependent on the presence of the E1 ligase Ube1L [23] . Indeed , enhanced IFN promoter activity has been observed in Ube1L−/− cells in response to NDV [18] . In accord with this , depletion of endogenous Ube1L from the Huh7 . 25 . CD81 cells ( Figure S4 ) , as such or after ectopic expression of ISG15 , UbcH8 and HERC5 , resulted in an increase in IFNβ induction after infection with HCV ( Figure 2B ) . We then analysed the effect of siISG15 on IFNβ induction after infecting the cells with HCV up to 72 hours , in order to pass through the 24 hr time-point where the signaling pathway leading to the transcription of this gene is expected to stop because of the NS3/4A-mediated cleavage of MAVS [8] . The results show that , whereas IFNβ transcription was indeed strongly inhibited after 24 hr in the control cells , it still occurred significantly in the cells expressing siRNA ISG15 ( Figure 2C ) . Previous data have shown a positive role for ISG15 on HCV production [24] , [25] . In accord with this , silencing of ISG15 resulted in clear inhibition of HCV RNA expression with however no significant consequence on the ability of the virions produced to re-infect fresh cells ( Figure 2D ) . Analysis of expression of MAVS and NS3 , as well as the expression of the core protein as another example of viral protein , then showed that the depletion of ISG15 both decreased and delayed the expression of the viral proteins as compared to the siRNA control cells and that this was correlated by a delay in the NS3/4A-mediated cleavage of MAVS ( Figure 2E ) . These results show that ISG15 controls the process of IFN induction during HCV infection by interfering with RIG-I ubiquitination through an ISGylation process and by boosting efficient accumulation of NS3 , among other viral proteins , thus favouring its negative control on IFN induction by cleavage of MAVS . ISG15 ( [24] , [25] and this study ) and PKR [8] , [9] emerge as two ISGs with pro-HCV activities , instead of playing an antiviral role . We then assayed the effect of a combined depletion of PKR and ISG15 on HCV replication and IFN expression in the Huh7 . 25 . CD81 cells . As shown in Figure 2 D and B , siRNAs targeting ISG15 were sufficient both to inhibit HCV replication ( Figure 3A ) and to increase IFNβ expression , either measured by RTqPCR ( Figure 3B ) or by using an IFNβ-luciferase reporter assay ( Figure 3C ) . Very limited additional effect was observed in the concomitant presence of siRNAs targeting PKR . ( Figure 3B ) . Interestingly , we noticed that expression of luciferase from the IFNβ promoter increased throughout the first 18 hours of HCV infection in the siISG15 cells ( Figure 3C ) . This was intriguing as it should have been inhibited after 12 hours of HCV infection through the eIF2α kinase activity of PKR and its control on translation [8] . We therefore analysed whether the state of PKR activation ( phosphorylation ) was dependent on the expression of ISG15 . For this , the Huh7 . 25 . CD81 cells were transfected either with siRNAs targeting ISG15 or with a plasmid expressing an HA-ISG15 construct and PKR phosphorylation was analysed as described previously [8] . The results showed that depletion of ISG15 inhibits PKR activation in the HCV-infected cells , while its overexpression stimulates it ( Figure 3D and Figure S5 ) . Therefore these data reveal that , in addition to negatively controlling RIG-I ubiquitination , ISG15 can also positively control PKR activity . The conjugation of both effects results in an efficient control of IFN induction during HCV infection . The Huh7 . 25 . CD81 cells express ISG15 at significant basal levels . This situation was not surprising as various cellular systems can also express some of the ISGs at basal level . Expression of ISG15 was approximately 2- and 5-fold higher in the Huh7 . 25 . CD81 cells than in the Huh7 . 5 or Huh7 cells ( data not shown ) . Intriguingly however , we noticed that ISG15 expression was increased in response to HCV infection ( see Figure 2E ) . To investigate this further , we simply re-used the RNAs prepared for the experiment shown in Figure 3B and performed a quantitative kinetics analysis . The results confirmed that HCV can trigger induction of ISG15 ( Figure 4A ) . Unexpectedly , analysis of the RNA extracted from the cells treated with siRNAs targeting PKR , revealed that ISG15 RNA expression was strongly repressed when PKR was silenced ( Figure 4A ) . This surprising result was confirmed by analysing induction of ISG56 , another early ISG [26] , both at the level of its endogenous RNA ( Figure 4B ) or by using an ISG56-luciferase vector ( Figure 4C ) . In the latter case , a strong increase of the reporter expression in the cells treated with siRNAs targeting ISG15 , was similar to the situation observed for IFNβ RNA ( Figure 2B and 2C ) . This can be related to activation of the RIG-I pathway , which can function when ISG15 is absent . These data suggest that HCV may use PKR to activate gene transcription . Importantly , this phenomenon was specific to HCV as infection with Sendai virus resulted in a similar induction of ISG15 and ISG56 , regardless of PKR ( Figure 4D and Figure S6 ) . We then examined whether overexpression of PKR could boost induction of ISG15 during HCV infection and how this would affect HCV replication and IFN induction , in relation to the pro-HCV action of ISG15 . Huh7 . 25 . CD81 cells were transfected with a plasmid expressing PKR alone or in presence of siRNAs targeting ISG15 , before being infected with HCV over 48 hours . Overexpression of PKR increased the ability of HCV to induce ISG15 and concomitantly , led to an increase in HCV RNA expression . The latter increase was abolished when ISG15 was silenced , thus showing that the PKR-dependent increase in HCV expression is mediated by ISG15 ( Figure 4E ) . However , while the cells silenced for ISG15 are able to induce IFN in response to HCV infection , as shown in Figure 3B , they are unable to do so when PKR is overexpressed . This suggests that PKR may also interfere with the process of IFN induction , independently of ISG15 , a possibility that remains to be explored . A role for PKR in gene induction in response to HCV infection has not been described before . Additional information was therefore obtained through a transcriptome analysis of 2165 genes in the Huh7 . 25 . CD81 cells treated with control siRNAs or siRNAs targeting PKR and infected with HCV for 12 hrs . Out of the most significant 422 genes that were identified , 99 were unmodified or barely modified and 33 were down-regulated , while 290 genes were found to be up-regulated by HCV infection ( data not shown ) . Among those , HCV infection triggered up-regulation of 49 genes which are directly dependent on PKR expression ( Table 1 ) . Forty percent of these genes ( 20 ) belong to the family of the ISGs , with ISG15 among the most induced genes ( Table 1 ) . In the reciprocal situation , only 17 genes depended on PKR for their down-regulation by HCV infection , with no link to a particular family of genes and limited variation both in number and intensity ( Table S1 ) . Thus , induction of ISGs upon HCV infection may occur through a novel signaling pathway that involves PKR . Infection with RNA viruses or transient transfection with dsRNA can directly and rapidly induce early ISGs , such as ISG15 , through IRF3 , after activation of the RIG-I/MAVS pathway and recruitment of TRAF3 , an essential adapter which recruits the downstream IRF3 kinases TBK1/IKKε . We have shown that the RIG-I pathway was not operative during HCV infection in the Huh7 . 25 . CD81 cells , precisely due to the presence of ISG15 . To determine how ISG15 induction through PKR relates to or differs from the RIG-I/MAVS pathway , the Huh7 . 25 . CD81 cells were treated with siRNAs aimed at targeting separately PKR , RIG-I , MAVS , TRAF3 and IRF3 ( Figure S7 ) and infected with HCV . The results clearly showed that induction of ISG15 in response to HCV infection depends on PKR , MAVS , TRAF3 and IRF3 but not on RIG-I ( Figure 5A ) . The participation of IRF3 was further confirmed by immunofluorescence studies which showed its nuclear translocation at 6 hours post-infection ( Figure S8 ) . ISG15 , as well as ISG56 , was also clearly induced in response to HCV infection in two other HCV permissive cell lines , such as Huh7 and Huh7 . 5 cells , and this induction was abrogated in presence of siRNAs targeting PKR ( Figure 5B and Figure S9 ) . Importantly , since Huh7 . 5 cells express a non-functional RIG-I/MAVS pathway due to a mutation in RIG-I , result with these cells supports the notion that the ability of HCV to trigger induction of ISGs through PKR is independent of RIG-I . To have more insights on this novel PKR signaling pathway , PKR was immunoprecipitated at early times points following infection of Huh7 . 25 . CD81 cells with HCV and the immunocomplexes were analysed for the presence of MAVS , TRAF3 and RIG-I . Both MAVS and TRAF3 , but not RIG-I , associate with PKR in a time dependent manner , beginning at 2 hrs post-infection ( Figure 5C ) . Strikingly , these associations were abrogated by the cell-permeable peptide PRI which is analogous to the first dsRNA binding domain ( DRBD ) of PKR [8] , while unaffected by C16 , a chemical compound which inhibits the catalytic activity of PKR ( Figure 5D ) . In line with this , PRI but not C16 , abrogated the ability of HCV to induce ISG15 ( Figure 5E ) . The same result was obtained for induction of ISG56 ( Figure S10 ) . We then used human primary hepatocytes ( HHP ) to determine whether HCV was also able to induce ISGs through PKR in a more physiological cellular model . A follow-up of the infection over a period of 96 hours showed that JFH1 was replicating correctly in those cells as well as leading to induction of ISG15 ( 10-fold ) and to some induction of IFNβ ( 2 . 5-fold ) . These cells were infected with JFH1 for 8 hours in the absence or presence of PRI , making convenient use of the cell-penetrating ability of this peptide . Longer period of treatment with PRI were not investigated for practical reasons ( see Materials and Methods ) . The results showed that PRI was significantly inhibiting the induction of ISG15 while it had no effect on that of IFNβ ( Figure 5F ) . Altogether , these data demonstrate that HCV triggers induction of early ISGs through MAVS and TRAF3 by using PKR as an adapter protein . The ability of HCV to control activation of the RIG-I/MAVS pathway after induction of ISG15 through a novel PKR/MAVS pathway suggests that PKR has the possibility to bind MAVS prior to RIG-I . To determine this , we established the kinetics of these interactions , after treating the Huh7 . 25 . CD81 cells with siRNAs targeting ISG15 prior to HCV infection . This was necessary in view of the negative control of ISG15 on RIG-I . MAVS was immunoprecipitated from the cell extracts at different times post-infection and the presence of PKR and RIG-I was examined in the immunocomplexes , as well as that of TRAF3 , used as marker of activation of the MAVS signaling pathway . As expected , only PKR was able to associate with MAVS and TRAF3 in the control cells ( Figure 6A ) whereas both PKR , RIG-I and TRAF3 were found in the immunocomplexes in the absence of ISG15 ( Figure 6B ) . The PKR/MAVS association took place at 4 hrs post-infection in the control cells but was observed 2 hrs earlier in the ISG15-depleted cells . Whether ISG15 plays a role in the regulation of the PKR/MAVS association remains to be determined . However , the presence of TRAF3 in association with MAVS at 2 hrs post-infection in the control cells ( Figure 6A ) correlates with its association with PKR ( Figure 5C ) which indicates that the MAVS pathway can be activated through PKR as soon as 2 hrs post infection . In ISG15 knock-down cells , the RIG-I/MAVS association occurred later at 6 hrs post-infection with an increase in TRAF3 association at 9–12 hrs post infection . Altogether , these data revealed that HCV infection triggers an earlier interaction of MAVS with PKR than with RIG-I . Finally , we asked whether PKR was able to associate with HCV RNA and how this association can be compared to that of RIG-I . PKR and RIG-I were immunoprecipitated at 2 , 4 and 6 hrs post-infection and the presence of HCV RNA was analysed in the complexes . The results showed that PKR associates with HCV RNA with best efficiency at 2 hrs post-infection . Importantly , this association was strongly inhibited in presence of PRI , thus confirming the importance of PKR DRBD in the process . In contrast , the association of HCV RNA with RIG-I was detected only at 6 hrs post-infection . Interestingly , the association between RIG-I and HCV RNA was not affected by PRI , which rules out the possibility that the initial formation of a complex between PKR and HCV RNA was a pre-requisite for the subsequent binding of RIG-I to HCV RNA . Immunoprecipitation of PKR at 1 , 2 , 4 and 6 hrs post-infection , in presence of an inhibitor of ribonucleases also did not lead to detection of RIG-I in the complexes ( Figure S11 ) . Association of HCV RNA with eIF2α , used as negative control , was not significant , thus showing the specificity of the assay ( Figure 6C ) . Whether a direct interaction of PKR with HCV RNA represents the initial event leading to the MAVS-dependent induction of early ISGs remains now to be characterized . Altogether , these data reveal an earlier mobilization of PKR than RIG-I in response to HCV infection which leads to activation of a MAVS-dependent signaling pathway .
Hepatitis C virus can attenuate IFN induction at multiple levels in infected hepatocytes , such as through the NS3/4A-mediated MAVS cleavage [7] , [27] and by using the eIF2α kinase PKR to control IFN and ISG expression at the translational level [8] , [9] . Here , we have identified another process by which HCV controls IFN induction at the level of RIG-I ubiquitination through ISG15 and an ISGylation process . Importantly , we have shown that ISG15 is rapidly induced , among other ISGs , in response to HCV infection , through a novel signaling pathway that involves PKR , MAVS , TRAF3 and IRF3 but not RIG-I . In this pathway , PKR is not used for its kinase function but rather as an adapter protein with its dsRNA binding domain ( DRBD ) playing an essential role in this mechanism ( Figure 7 ) . By transcriptome analysis , we showed that HCV induces a number of ISGs in the HCV-permissive Huh7 . 25 . CD81 cells and we confirmed the induction of two of these , ISG15 and ISG56 , in other HCV-permissive cells , such as Huh7 . 5 and Huh7 cells . In addition , induction of ISG15 by HCV in a PKR-dependent manner was confirmed in human primary hepatocytes . The ability of HCV to trigger high expression levels of ISG15 and ISG56 , as well as other ISGs , has previously been reported in models of HCV-infected chimpanzees [10] , [12] , [28] and in HCV-infected patients [14] , [15] , [16] . Induction of ISGs thus represents a general propriety of the response of the cells to HCV . In addition to this , natural variations in intra-hepatic levels of ISG15 in vivo may increase the susceptibility of some patients to HCV infection . The ability of HCV to control RIG-I activity through ISG15 is important to note in view of several reports which highlight the importance of a role for ISG15 in the maintenance of HCV in livers [15] , [16] or in the control of HCV replication in cell cultures [17] , [25] . Our data provide an explanation for the presence of ISGs at high expression levels in HCV-infected patients [14] , [15] , [16] and in models of HCV-infected chimpanzees [10] , [12] , [28] in the absence of , or with poor IFN expression . The 15 Kda ISG15 , or Interferon Stimulated Gene 15 [29] , also known as ubiquitin cross reactive protein ( UCRP ) [30] , can be conjugated ( ISGylation ) to more than 150 cellular protein targets [31] through the coordinated action of three E1 , E2 and E3-conjugating enzymes , in a process similar but not identical to ubiquitination . While both ubiquitin and ISG15 can use the same E2 enzyme UbcH8 , Ube1L functions as a specific E1 enzyme for ISG15 , in spite of its 45% identity with Ube1 , the E1 enzyme for ubiquitin [32] . The major E3 ligase for human ISG15 is HERC5 [33] . Interestingly , RIG-I was identified as a target for ISG15 , among other IFN-induced proteins or proteins involved in IFN action [31] . However , its activity appears to be negatively controlled by ISG15 and the ISGylation process , either as shown previously after cotransfection with the ISG15 and the ISG15-conjugating enzymes [18] or as shown here , in a model of infection with HCV . Indeed , ISG15 is now emerging as playing a proviral role in case of HCV infection . Several reports now highlight the importance of a role for ISG15 in the control of HCV replication in cell cultures [17] , [25] as well as in the maintenance of HCV in livers and pinpoint ISG15 as among the predictor genes of non-response to IFN therapy [14] , [15] , [16] . At present , we do not know at which level ISGylation regulates IFN induction in response to HCV infection . An HCV-mediated increase of ISG15 would favour preferential binding of ISG15 over that of ubiquitin to the E2 enzyme UbcH8 and hence enhance the spatio-temporal availability of UbcH8-ISG15 for HERC5 over that of UbcH8-ubiquitin for TRIM25 . It may also lead to inhibition of TRIM25 , through autoISGylation [21] , [34] , which would decrease its ability to ubiquitinate RIG-I . We showed that overexpression of HERC5 together with Ube1L , UbcH8 and ISG15 was increasing the ability of ISG15 to inhibit IFN induction by HCV ( Figure 2B ) . All three enzymes Ube1L , UbcH8 and HERC5 belong to the family of genes induced by IFN and it has been reported that ISGylation is optimum in a context of IFN treatment [18] , [35] . Therefore , it is tempting to speculate that elevated levels of ISG15 in some HCV-infected patients would bring the most favourable context for the virus when those patients are under IFN therapy . This would be in accord with the clinical data showing that HCV-induced high expression of ISG act as a negative predictive marker for response to IFN therapy . It is doubtful that viruses with high IFN-inducing efficiency , such as Sendai virus may control RIG-I through ISG15 and PKR . However , viruses that avoid inducing IFN may have use of the PKR pathway . A good example might be that of Hepatitis B Virus ( HBV ) [36] , [37] , [38] . PKR expression was previously reported to be elevated in HCC liver from chronically HBV infected patients [39] and a relationship between PKR and IFN induction during HBV infection would be important to evaluate . At present , we have established that HCV RNA interacts with PKR as soon as 2 hours post-infection . This interaction occurs prior the interaction of HCV RNA with RIG-I , which suggests that PKR may rapidly detect structures containing the incoming HCV RNA genome . Indeed , PKR has been reported to bind the dsRNA domains III and IV of HCV IRES [40] in addition to its ability to also bind 5′ triphosphorylated ss or dsRNA structures [41] . Whether PKR behaves as a pathogen recognition receptor for HCV RNA , like RIG-I , remains to be clarified . It is however clear that , in contrast to RIG-I , PKR acts here in favour of the pathogen rather than in favour of the host defense . We have established that the HCV RNA/PKR interaction depends on the first DRBD present at the N terminus of PKR and is independent on its kinase activity . The ability of PKR to serve as adapter in signaling pathways is not a total surprise since it has been previously shown to activate NF-κB through interaction of its C terminus with members of the TRAF family , such as TRAF5 and TRAF6 [42] . PKR contains also TRAF interacting motif in its N terminus [42] and an association between TRAF3 and PKR has been reported upon cotransfection in 293T cells [43] . Intriguingly , PKR was previously reported to participate in the induction of IFNβ , in association with MAVS , through activation of NF-κB or ATF-2 but not or partially IRF3; however these studies were not performed in the absence of RIG-I [44] , [45] , [46] . The mode of interaction between PKR , TRAF3 and MAVS , independently of RIG-I , and how it leads to a preferential induction of ISGs and not of IFNβ in response to HCV infection in contrast with the RIG-I/MAVS pathway remains to be determined . Based on our data , we propose now to divide the innate response to acute HCV infection into two phases: an early acute phase in which PKR is activated and a late acute phase that depends on RIG-I , the early phase controlling activation of the late phase . It is now essential to progress towards the generation of specific pharmaceutical inhibitors targeting PKR in order to abrogate the early acute phase to the benefit of the RIG-I-driven late phase . In a more general view , care should now be taken in the choice of compounds designed to be used as immune adjuvants , such as to be devoid of activation of the early acute PKR phase . This will ensure their efficiency as to activate properly the innate immune response through the late acute RIG-I phase .
The culture of Huh7 , Huh7 . 5 , Huh7 . 25 . CD81 cells , the preparation of Sendai virus stocks ( ≈2000 HAU/ml ) and of HCV JFH1 stocks ( ≈6 . 104 FFU/mL and ≈6 . 106 FFU/mL ) was as described [8] , [47] . Preparation and cultures of human primary hepatocytes was as described [48] . Of note , the ability of the Huh7 . 25 . CD81 cells to induce IFN in response to SeV without prior IFN treatment ( 40-fold ) was not observed in our previous study [8] . The ability of Sendai virus to induce IFN is related to the presence of copyback DI ( Defective Interfering ) genomes [49] . The higher IFN inducing ability of the novel Sendai virus stock may have come from an important accumulation of these copyback DI genomes , during its growth in chicken eggs . The C16 compound [50] and the cell-permeable PRI peptide [51] were provided by Jacques Hugon . These drugs were applied ( 200 nM for C16 and 30 mM for PRI ) one hour before the end of the 2 hr- incubation time with JFH1 and re-added to the medium after washing the cells with phosphate buffered saline ( PBS ) . Note that PRI loses its effect very rapidly , probably through degradation in the cells , and requires to be added every hour to the cells until the end of treatment . TRIM25 was cloned from the IFN-treated Huh7 . 25CD81 cells ( 500 U/ml IFN-α2a; Cellsciences ) after RT-PCR using the forward: 5′-ATGGCAGAGCTGTGCCCCCT-3′ and reverse 5′-CTACTTGGGGGAGCAGATGG-3′ primers . The pcDNA3 . 1 ( + ) vector expressing 5′HA tagged-TRIM25 ( provided by D . Garcin; University of Geneva , Switzerland ) was used to generate the TRIM25 P358L construct by site-directed mutagenesis . The IFNβ-firefly luciferase ( pGL2-IFNβ ) and pRL-TK Renilla-luciferase reporter plasmids were described previously [8] . The pGL3 luciferase reporter construct containing the −3 to −654 nucleotides of the ISG56 promoter was provided by N . Grandvaux [52] . The Myc-HIS-Ubiquitin construct was provided by R . Kopito ( Stanford University , CA ) . ISG15 was cloned from IFN-treated Huh7 cells using the forward: 5′- GGATCCCATGGGCTGGGACCTGACGGTG-3′ and reverse 5′-CTCGAGCTCCGCCCGCCAGGCTCTGT-3′ primers and inserted into the pcDNA3 . 1 ( + ) HA vector . The Ube1L , UbcH8 and HERC5 constructs were kindly provided by Jon M . Huibregtse [35] . The pcDNA1/AMP vector expressing PKR has been described previously [53] . The siRNAs directed against PKR , MAVS , RIG-I , TRAF3 and IRF3 which were used for the experiment described in figure 5A correspond to pools of siRNA ( Smartpool ) obtained from Dharmacon Research , Inc . ( Lafayette , CO ) , as well as siRNAs directed against Ube1L used in Figure 2B . Control ( scrambled ) siRNA and siRNA directed against PKR or ISG15 , used in all other experiments , were chemically synthesized by Dharmacon ( scrambled and PKR ) and by EUROFINS MWG Operon ( ISG15 ) ( Text S1 ) . The siRNAs ( final concentration 25 nM or 50 nM ) were transfected for 48 h using jetPRIME reagent according to the manufacturer's instructions ( PolyPlus transfection TM ) before transfection with other plasmids or before infection . Mab to ISG15 ( clone 2 . 1 ) was a kind gift of E . Borden [54] . Mab to PKR was produced from the murine 71/10 hybridoma ( Agro-bio; Fr ) with kind permission of A . G . Hovanessian [55] . Other antibodies were as follows: anti-mouse IgG ( Santa Cruz ) , anti-TRAF3 ( Santa Cruz ) , pThr451-PKR ( Alexis ) , MAVS ( Alexis ) , anti-actin ( Sigma ) , anti-pSer10-Histone H3 ( Millipore ) , anti-HCV NS3 ( Chemicon ) , anti-HCV core ( Thermo scientific ) , anti-RIG-I ( Alexis Biochemical Inc . ) , anti-TRIM25 ( 6105710; BD Bioscience ) , anti-IRF3 ( Santa Cruz ) , anti-HA ( 12CA5; Roche ) and anti-Myc ( Santa Cruz ) . Huh7 . 25 . CD81 cells ( 80 , 000 cells/well; 24-well plates ) were transfected with 40 ng of pRL-TK Renilla-luciferase reporter ( Promega ) and 150 ng of either pGL2-IFNβ-Firefly luciferase reporter or pISG56-luciferase reporter and processed for dual-luciferase reporter assay as reported previously [8] . Total cellular RNA was extracted using the TRIZOL reagent ( Invitrogen ) . HCV RNA was quantified by one-step RTqPCR . Reverse-transcription , amplification and real-time detection of PCR products were performed with 5 µl total RNA samples , using the SuperScript III Platinum one-step RTqPCR kit ( Invitrogen ) and an AbiPrism 7700 machine . For the sequence of the different primers , see Text S1 . The results were normalized to the amount of cellular endogenous GAPDH RNA using the GAPDH control kit from EuroGentec . Copies number of HCV RNA may vary due to internal calibration and depending on the preparation of the viral stocks . All m . o . i were calculated using the titers expressed in FFU/ml . The IFNβ , ISG15 , ISG56 , Ube1L and GAPDH amplicons were quantified by a two-step RTqPCR assay as described [8] . Cellular RNA was extracted and purified from the cells using RNAeasy mini kit ( QIAGEN K . K . , Tokyo , Japan ) . Comprehensive DNA microarray analysis was performed with 3D-Gene Human Oligo chip25k with 2-color fluorescence method by New Frontiers Research Laboratories , Toray Industries Inc , Kamakura , Japan as previously described [56] . In brief , each sample was hybridized with 3D-Gene chip . Hybridization signals were scanned using Scan Array Express ( PerkinElmer , Waltham , MA ) . The scanned image was analyzed using GenePix Pro ( MDS Analytical Technologies , Sunnyvale , CA ) . All the analyzed data were scaled by global normalization . Cells were washed once with PBS and scraped into lysis buffer 1 ( 50 mM TRIS-HCl [pH 7 . 5] , 140 mM NaCl , 5% glycerol , 1% CHAPS ) that contained phosphatase and protease inhibitors ( Complete , Roche Applied Science ) . The protein concentration was determined by the Bradford method . For immunoprecipitation , lysates were incubated at 4°C overnight with the primary antibodies as indicated and then in the presence of A/G-agarose beads ( Santa Cruz Biotechnology ) for 60 minutes . The beads were washed three times , and the precipitated proteins were extracted at 70°C using NuPAGE LDS sample buffer . Protein electrophoresis was performed on NuPAGE 4–12% Bis TRIS gels ( Invitrogen ) . Proteins were transferred onto nitrocellulose membranes ( Biorad ) , and probed with specific antibodies . Fluorescent immunoblot images were acquired and quantified by using an Odyssey scanner and the Odyssey 3 . 1 software ( Li-Cor Biosciences ) as described previously [8] . For detection of ISG15 , cells were lysed in RIPA buffer ( 50 mM TRIS-HCl [pH 8 . 0]; 200 mM NaCl; 1% NP-40; 0 . 5% Sodium Deoxycholate; 0 . 05% SDS; 2 mM EDTA ) and protein electrophoresis was performed on 4–20% polyacrylamide gels ( PIERCE ) . Pellets from cells washed in ice-cold phosphate-buffered saline ( PBS ) were lysed in ice-cold cytoplasmic buffer ( 10 mM TRIS [pH 8 . 0] , 5 mM EDTA , 0 . 5 mM EGTA , 0 . 25% Triton X-100 ) containing phosphatase and protease inhibitors . The suspension was centrifuged for 30 seconds at 14 , 000 g and the supernatant ( cytoplasmic fraction ) was transferred into microcentrifuge tubes . The nuclear pellet was resuspended in Urea buffer ( 8 M Urea , 10 mM TRIS [pH 7 , 4] , 1 mM EDTA , 1 mM dithiothreitol ) containing phosphatase and protease inhibitors , homogenized by vortex and boiled for 10 minutes . The protein concentration was determined by the Bradford method . Huh7 . 25 . CD81 cells were transfected for 48 hrs with 5 µg of Myc-His-Ubiquitin expression plasmid using jetPRIME reagent . The cells were then washed in ice-cold PBS containing 20 mM N-ethylmaleimide ( Sigma-Aldrich ) , harvested directly in Gua8 buffer ( 6 M guanidine-HCl , 300 mM NaCl , 50 mM Na2HPO4 , 50 mM NaH2PO4 [pH 8 . 0] ) , briefly sonicated , and centrifuged at 14 , 000 g for 15 min at 4°C . 1/10th of the lysate was subjected to precipitation with 10% trichloroacetic acid for protein analysis in whole cell extracts . The rest of the lysate was incubated for 2 hrs with 20 µl ( packed volume ) of Talon resin Ni-affinity beads ( Clontech ) on a rotating wheel . Bound proteins were washed four times in Gua8 buffer , three times in Urea 6 . 3 buffer ( 8 M Urea , 10 mM TRIS , 0 . 1 M Na2HPO4 , 20 mM Imidazole [pH 6 . 3] ) , and three times in cold PBS , after which they were eluted by boiling in NuPAGE LDS sample buffer . Electrophoresis was performed on 4–12% of acrylamide NuPAGE gels ( Invitrogen ) . Huh7 . 25 . CD81 cells were incubated for 10 min in their culture medium containing 1/10 volume ( Vol ) of a crosslinking solution ( 11% Formaldehyde , 0 . 1 M NaCl , 1 mM Na-EDTA-[pH 8] , 0 . 5 mM Na-EGTA-[pH 8] , 50 mM HEPES [pH 8] ) . The reaction was stopped by addition of a solution of 0 . 125 M glycine in PBS [pH 8] at room temperature ( RT ) . The cells were washed three times in ice-cold PBS containing 1000 U/ml of RNAse inhibitor ( Promega ) , scraped in PBS and dispatched into three sets containing ½ ( set 1 ) , ¼ ( set 2 ) and ¼ ( set 3 ) of the cell suspension . The three sets were centrifuged for 30 seconds at 14 , 000 g and 4°C and the cell pellets were lysed into lysis buffer 1 containing phosphatase/protease and RNAse inhibitors ( Promega ) for sets 1 and 2 or into TRIZOL reagent for set 3 . Cell lysates from sets 1 and 2 were then incubated at 4°C , first overnight with the appropriate primary antibodies and for 60 minutes in the presence of A/G-agarose beads ( Santa Cruz Biotechnology ) . After the incubation period , the beads were washed four times with buffer 1 . Set 1 ( HCV RNA bound to immunocomplexes ) and set 3 ( input HCV RNA ) were submitted to TRIZOL treatment and HCV RNA was quantified by one-step RTqPCR as described previously . The immunoprecipitated proteins from set 2 were extracted at 70°C using NuPAGE LDS sample buffer and analysed by immunoblot after electrophoresis on 4–12% of acrylamide NuPAGE gels ( Invitrogen ) .
|
Hepatitis C Virus ( HCV ) is a poor interferon ( IFN ) inducer , despite recognition of its RNA by the cytosolic RNA helicase RIG-I . This is due in part through cleavage of MAVS , a downstream adapter of RIG-I , by the HCV NS3/4A protease and through activation of the eIF2α-kinase PKR to control IFN translation . Here , we show that HCV also inhibits RIG-I activation through the ubiquitin-like protein ISG15 and that HCV triggers rapid induction of 49 genes , including ISG15 , through a novel signaling pathway that precedes RIG-I and involves PKR as an adapter to recruit MAVS . Hence , we propose to divide the acute response to HCV infection into one early ( PKR ) and one late ( RIG-I ) phase , with the former controlling the latter . Furthermore , these data emphazise the need to check compounds designed as immune adjuvants for activation of the early acute phase before using them to sustain innate immunity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"immunology",
"microbiology",
"liver",
"diseases",
"infectious",
"hepatitis",
"gastroenterology",
"and",
"hepatology",
"signaling",
"pathways",
"hepatitis",
"c",
"stress",
"signaling",
"cascade",
"viral",
"immune",
"evasion",
"biology",
"molecular",
"biology",
"signal",
"transduction",
"immunity",
"virology",
"innate",
"immunity",
"antivirals",
"molecular",
"cell",
"biology",
"signaling",
"cascades"
] |
2011
|
Hepatitis C Virus Reveals a Novel Early Control in Acute Immune Response
|
Aberrant DNA methylation is an important cancer hallmark , yet the dynamics of DNA methylation changes in human carcinogenesis remain largely unexplored . Moreover , the role of DNA methylation for prediction of clinical outcome is still uncertain and confined to specific cancers . Here we perform the most comprehensive study of DNA methylation changes throughout human carcinogenesis , analysing 27 , 578 CpGs in each of 1 , 475 samples , ranging from normal cells in advance of non-invasive neoplastic transformation to non-invasive and invasive cancers and metastatic tissue . We demonstrate that hypermethylation at stem cell PolyComb Group Target genes ( PCGTs ) occurs in cytologically normal cells three years in advance of the first morphological neoplastic changes , while hypomethylation occurs preferentially at CpGs which are heavily Methylated in Embryonic Stem Cells ( MESCs ) and increases significantly with cancer invasion in both the epithelial and stromal tumour compartments . In contrast to PCGT hypermethylation , MESC hypomethylation progresses significantly from primary to metastatic cancer and defines a poor prognostic signature in four different gynaecological cancers . Finally , we associate expression of TET enzymes , which are involved in active DNA demethylation , to MESC hypomethylation in cancer . These findings have major implications for cancer and embryonic stem cell biology and establish the importance of systemic DNA hypomethylation for predicting prognosis in a wide range of different cancers .
Aberrant DNA methylation is one of the most important cancer hallmarks [1] , yet its precise role in carcinogenesis and clinical prognosis remains ill-defined [2] . Indeed , the dynamical changes in DNA methylation that happen during carcinogenesis , in particular those prior to morphological changes , have not yet been explored in detail . Moreover , no study has so far reported a DNA methylation signature capable of predicting prognosis across multiple human cancers , unlike gene expression and DNA copy number where such prognostic signatures have been described [3] , [4] . Both hyper and hypomethylation are commonly observed in cancer [1] . In contrast to hypomethylation , which seems to target large inter-genic satellite repeat regions , hypermethylation appears to happen locally , preferentially targeting the promoters of genes . Several studies have reported that a statistically high fraction of these promoters map to stem cell PolyComb Group Target genes ( PCGTs ) [5] , [6] , many of which encode transcription factors needed for differentiation , and which are normally suppressed in embryonic stem cells through a reversible mechanism mediated by the Polycomb Repressive Complex ( PRC2 ) [7] . This preferential hypermethylation at PCGTs in cancer supports the view that the reversible gene repression of PCGTs in stem cells may be replaced by permanent silencing in cancer , potentially impairing the differentiation capacity of cells [1] , [5] , [6] . Although there is no causal functional data linking PCGT methylation to carcinogenesis yet , there is accumulating evidence that factors which lead to cancer , for instance age or oxidative damage , are causally involved in DNA methylation at PCGTs [8]–[11] . Another feature of the epigenetic landscape characterising human embryonic stem cells ( hESC ) was described by Lister et al [12] . Specifically , using single-base-resolution DNA methylation maps , they demonstrated that a substantial fraction of CpGs is heavily ( >80% ) Methylated in human Embryonic Stem Cells ( MESC ) ( see Materials and Methods for the precise definition of MESC CpGs and Table S1 for the complete list of MESC CpGs on the 27 k array ) . However , it is unknown at present what role MESCs may play throughout carcinogenesis . Thus , which epigenetic stem cell features are retained or changed in human cancer and even more importantly at which stage during human carcinogenesis these epigenetic changes occur , is still unclear . Motivated by these outstanding questions , we decided to ( i ) explore the dynamics of epigenetic changes at stem cell loci ( PCGTs and MESCs ) throughout all stages of human carcinogenesis and ( ii ) to investigate their potential role in predicting poor prognosis . To address our first aim , we used as a model the uterine cervix , since screening programs in place allow easy access to this organ , and cervical carcinogenesis is also one of the few scenarios in humans where DNA methylation changes in the actual cell of origin and occurring throughout disease progression can be analyzed . Specifically , we measured DNA methylation at over 27 , 000 CpGs in cervical cells and at three different stages: ( a ) three years before onset of dysplastic changes , ( b ) at the stage of non-invasive dysplasia , and ( c ) at the stage of invasive cervical cancer . To address our second aim we analysed DNA methylation data from 5 independent cohorts encompassing a total of 1 , 026 tumour samples in 4 different gynaecological cancers . In total , we analysed DNA methylation data from 10 independent studies , encompassing normal and cancer tissue from 5 different tissue types , including metastases ( Table 1 ) . Using these data we here report four major novel aspects of cancer epigenetics: ( i ) Hypermethylation at PCGT stem cell loci occurs up to three years before the first signs of morphological transformation , ( ii ) hypomethylation at MESC stem cell loci is a hallmark of cancer invasion , affecting both epithelial and stromal compartments , and increases further in metastases , ( iii ) hypomethylation instability at MESCs defines a stem cell DNA methylation signature that predicts poor prognosis in multiple human cancers independently of standard prognostic factors , and ( iv ) expression of TET enzymes [13]–[17] is strongly associated with MESC hypomethylation .
All methylation data in this study were generated with the Illumina Infinium Human Methylation27 beadchip array ( Materials and Methods ) , which assesses the DNA methylation status of 27 , 578 CpG sites located in the promoter regions of 14 , 495 genes as described previously [18] . Among these CpGs , 3 , 465 map to PCGTs , whilst 5 , 943 map to MESC CpGs ( Materials and Methods , Table S1 and Table S2 ) . We also made a distinction between CpGs located within Partially Methylated Domains ( PMDs ) ( a total of 4 , 750 CpGs on the array mapped to PMDs ) , and those that are not ( termed non-PMDs ) . PMDs demonstrate reduced methylation levels in more differentiated embryonic tissue compared to embryonic stem cells , and consist of focally hypermethylated elements ( corresponding overwhelmingly to CpG islands ) , concentrated within regions of long-range hypomethylation [12] . PMDs were recently described also in cancer [19] . For precise definitions see Text S1 . To investigate the dynamics of DNA methylation in human carcinogenesis we designed a study with samples from three different phases reflecting cervical carcinogenesis: ( 1 ) ‘Before Dysplasia ( BDy ) ’: normal cervical epithelial cells collected within the ARTISTIC trial [20] , [21] ( n = 152 ) of which 75 developed a cervical intraepithelial neoplasia grade 2 or 3 ( CIN2/3 ) after three years ( cases ) , whereas the other 77 remained normal ( controls ) . These samples were matched for age and HPV status . ( 2 ) ‘Dysplasia ( Dy ) ’: age-matched non-invasive dysplastic epithelial cells ( CIN2/3 ) ( n = 18 , all HPV+ ) and normal cervical epithelial cells ( n = 30 , 19 HPV− and 11 HPV+ ) collected within screening programs [22] , and ( 3 ) ‘Invasive Cancer ( CA ) ’: invasive cervical cancer tissue ( n = 48 ) and normal cervical tissue ( n = 15 ) collected within a clinical setting . Further details of the samples are described in Text S1 ( see also Table 1 ) . As expected , PCGTs were highly enriched among CpGs hypermethylated in invasive cervical cancer ( Figure 1A and 1C ) . In contrast , CpGs that become hypomethylated in invasive cervical cancer are to a large extent MESCs ( Figure 1B and 1D ) . Most importantly , PCGTs were hypermethylated three years prior to any cytological changes ( Figure 1C , OR = 2 . 44; 95%CI = 2 . 27–2 . 63; p<10−100 ) , especially for those PCGT CpGs located within PMDs ( OR = 4 . 81; 95%CI = 4 . 19–5 . 52; p<10−100 ) . We verified that PCGT enrichment was also independent of HPV status ( P<0 . 005 for HPV+ and HPV− ) . Notably , the frequency of hypermethylation remained fairly constant throughout the phases from non-invasive dysplasia to invasive cancer ( Figure 2A and Figures S1 , S2 , S3 , S4 ) . In contrast to PCGT methylation , MESC hypomethylation appears as a progressive process towards invasive cancer: whereas we observed a substantial enrichment of MESCs in the normal samples three years prior to the dysplastic changes ( OR = 5 . 69 and 9 . 55 for PMD and nonPMD respectively ) , non-invasive dysplastic samples had an increased MESC enrichment in hypomethylated CpGs ( OR = 7 . 62 and 12 . 30 for PMD and nonPMD , respectively ) and eventually MESC CpGs contributed most significantly to hypomethylated CpGs in invasive cancer ( OR = 18 . 84 and 26 . 85 for PMD and nonPMD respectively; Figure 1D , Figure 2A , and Figures S1 , S2 , S3 , S4 ) . In order to check that these enrichments are not just a consequence of the baseline methylation levels ( i . e . the levels in normal tissue ) , we estimated the enrichment relative to other CpGs with specific baseline methylation levels ( CpGs with mean β-values in normal cervical tissue samples of <0 . 2 and >0 . 4 ) . This confirmed that the observed PCGT and MESC enrichment was independent of the initial methylation levels in normal tissue , and that this was particularly true for PCGT/MESC CpGs within PMDs ( Figure S5 ) . Thus , MESC CpGs that showed reduced methylation levels ( <80% ) in normal tissue compared to their levels in hESCs ( >80% ) were still more likely to exhibit further hypomethylation in dysplasia and cancer than a control set of CpGs with similar methylation levels in normal tissue ( Figure S5 ) . To test if PCGT and MESC methylation changes are also present in cells which are not immediately involved in carcinogenesis we studied white blood cell DNA from women who carry BRCA1 mutations and who are therefore at an 80% lifetime risk of developing breast and/or ovarian cancer . Whereas MESC methylation was not altered , we observed that PCGTs were highly enriched among CpGs hypermethylated in blood cells from BRCA1 mutation carriers ( Figures S6 and S7 ) . Next , we asked if the progressive hypomethylation of MESCs towards invasive cancer is a generic feature of tumour biology . We analysed DNA methylation profiles of breast , endometrial , colorectal and lung cancer ( Text S1; Figure 2B and Figures S1 , S2 , S6 , S7 ) , and in all cancer types we observed a significant loss of methylation at MESC CpGs , concurrent with the expected hypermethylation of PCGT CpGs . As demonstrated in Figure 2A and 2B , PCGT methylation enrichment exists prior to and at the stage of non-invasive dysplasia when analyzing only epithelial cells without stroma and remains constant when studying invasive cancer tissue which contains some stromal components . In contrast , MESC enrichment doubles in the hypomethylated fraction when comparing invasive cancer to non-invasive dysplastic cells . This pronounced enrichment could be contributed by MESC hypomethylation in the cancer-associated stromal component . To test this , we analyzed those PCGTs and MESCs that are enriched in the hyper- and hypomethylated fractions in lung cancer and asked if these CpGs are also enriched in lung cancer associated fibroblasts compared to normal lung fibroblasts [23] . Interestingly , while there was no enrichment of PCGTs ( Figure 2C ) , there was a clear enrichment of lung cancer MESCs among PMD CpGs that are hypomethylated in lung cancer fibroblasts ( Figure 2D ) . This further supports the view that MESC hypomethylation is an important characteristic of cancer invasion , and that it may therefore be a molecular determinant of clinical outcome . Molecular signatures , and in particular gene expression signatures , involving stem cell genes have been associated with poor prognosis in several cancers [24] , [25] . Therefore , given the fundamental role of PCGT and MESC CpGs in the dynamics of DNA methylation in human cancer , as just described , it is natural to ask if DNA methylation changes at these stem cell loci can predict clinical outcome . In particular , we posited that epigenetic instability , as measured by DNA methylation changes from a normal reference , might indicate clinical outcome . To test this idea , we devised an Epigenetic Instability Index ( EpI ) to evaluate instability for each tumour sample as the fraction of significant DNA methylation changes relative to a corresponding normal reference profile ( Materials and Methods ) . The instability index was divided into 4 types according to the baseline normal reference methylation ( 0 = unmethylated , 1 = hemimethylated , 2 = methylated ) and the nature of DNA methylation changes ( 0→1/2 , 1→2 , 1→0 , 2→0/1 ) observed in cancer ( Materials and Methods , Figure 3A and 3B ) . In addition , we considered the EpI restricted to PCGT and MESC stem cell loci , and since very few PCGT CpGs were observed to be methylated ( 1 or 2 ) in normal tissue , this resulted in 3 stem cell EpI indices: PCGT ( 0→1/2 ) , MESC ( 1→0 ) , MESC ( 2→0/1 ) . Remarkably , we observed that the demethylation instability index ( DeMI ) at MESCs ( 2→0/1 ) was associated with poor prognosis in endometrial , breast , ovarian , and cervical cancers ( Figure 4 ) . In multivariate analysis , the DeMI was a predictor of poor prognosis in all cancers independently of other prognostic factors ( Table 2 and Table S3 ) , demonstrating the clinical potential of this DNA methylation stem cell signature . In contrast , the methylation instability index defined at PCGTs only correlated with clinical outcome in ovarian cancer ( Table S3 ) . Survival analysis at individual CpG level further demonstrated the consistent enrichment of MESC CpGs among prognostic CpGs hypomethylated in poor outcome samples in all 4 invasive cancers , whereas PCGT CpGs were not consistently enriched in either the hyper or hypomethylated prognostic component ( Table S4 ) . There was also substantial overlap between the MESC CpGs which have stable methylation levels in normal tissue and which become hypomethylated in cancer , and prognostic MESC CpGs that are hypomethylated in poor outcome tumour samples ( Table S5 ) . To further demonstrate that MESC hypomethylation is an important determinant of poor outcome in human cancer , we tested if these epigenetic changes progress further in metastatic cancer . Thus , we compared DNA methylation profiles of primary endometrial cancers to extra-uterine metastases of endometrial cancer . Importantly , the DeMI index was higher in metastatic cancer compared to primary tumours , but not so for the hypermethylation instability index at PCGTs ( Figure 5A ) . In fact , the DeMI index demonstrates clinical potential for discriminating primaries that may be destined to metastasize ( Figure 5B ) . From these data we can therefore conclude that while PCGT hypermethylation is an important event in early oncogenesis , which persists at later stages , MESC hypomethylation is a progressive process and a key characteristic of more malignant cancers ( Figure 3B ) . The ability of the DeMI index to predict clinical outcome in multiple cancers indicates that a core set of MESC CpGs may be involved . To investigate this we ranked the MESC CpGs according to the frequency of hypomethylation in each of the cancers considered . Many CpGs were observed to be hypomethylated in large fractions of tumours ( Figure 6 and Table S6 ) . While there were 6 MESC CpGs ( FCGR3B , FLJ27255 , FCN2 , KRT82 , CDH13 , KRTAP8-1 on chromosome 1 , 6 , 9 , 12 , 16 and 21 respectively ) commonly hypomethylated at a frequency of at least 10% in all four cancers ( P<10−4 ) , there were substantially larger overlaps between related cancers such as ovarian and endometrial cancer ( overlap of 98 CpGs , OR = 134 , 95%CI = ( 89–205 ) , P = 3 . 2×10−124 ) . Gene Set Enrichment Analysis ( GSEA ) [26] of the hypomethylated MESCs in each cancer also revealed a striking overlap of enriched terms , especially between endometrial and ovarian cancer where we observed widespread hypomethylation at 20q11 and 9q34 ( Table S7 ) . Up until recently it has been assumed that DNA demethylation in cancer is a passive event , occurring as a result of absent re-methylation during DNA replication , with a consequent dilution of this covalent DNA modification . This view has now been substantially challenged by the identification of TET ( ten eleven translocation ) dioxygenases , which can convert 5-methylcytosine into 5-hydroxymethylcytosine and 5-carboxylcytosine , which thus constitutes a pathway for active DNA demethylation [13]–[17] , [27] . In particular , it has been demonstrated that TET3-mediated DNA hydroxylation is involved in epigenetic reprogramming of the zygotic paternal DNA following natural fertilization and that this may also contribute to somatic cell nuclear reprogramming during animal cloning [16] . We therefore analysed mRNA expression of TET1 and two isoforms of TET2 , and TET3 ( see Text S1 for primer information ) , to test whether hypomethylation is associated with TET expression . We observed a strong correlation between high TET , in particular TET3 expression , and hypomethylation , specifically at MESC CpGs ( Figure 7 and Figure S8 ) . We checked that the anti-correlation of TET expression with MESC CpG methylation was independent of the level of methylation in normal tissue ( Figure S9 ) . Although this observation is purely correlative , it is consistent with the view that TET3 overexpression ( Figure S10 ) in cancer contributes to reprogramming of cancer cells via active DNA demethylation .
Epithelial cells of the uterine cervix offer a unique opportunity to study epigenetic alterations throughout carcinogenesis . Our first key result is the demonstration that normal cells of origin acquire methylation changes at least three years in advance of the first morphological changes . Specifically , our data demonstrate that PCGT hypermethylation and MESC hypomethylation are major contributors to early cervical carcinogenesis . This is independent of human papillomavirus ( HPV ) infection as our study was matched for HPV status , and since PCGT enrichment was observed in both HPV+ and HPV− samples . Importantly , the observed enrichments were also independent of the levels of methylation in normal tissue . That is , MESCs which showed full methylation ( i . e . β-value>0 . 8 ) or hemi-methylation ( i . e . 0 . 3<β-value<0 . 7 ) were preferentially hypomethylated in dysplasia and cancer in comparison to control sets of CpGs with same methylation levels in normal tissue . The role of PCGT methylation as a very early event is further supported by our finding that PCGTs were highly enriched among CpGs which were hypermethylated in blood cells from BRCA1 mutation carriers , suggesting that BRCA1 is an important regulator of the DNA methylome and that aberrant BRCA1 function could lead to increased predisposition to cancer through increased methylation at PCGT loci . The fact that BRCA1 mutation carriers showed increased PCGT methylation in their blood cells but are at no substantial increased risk to develop blood-borne cancers suggests that PCGT hypermethylation refers a substantial risk but that there are additional factors required ( e . g . endocrine , paracrine or viral triggers ) . Our second key result is that MESC hypomethylation occurs in both the epithelial and stromal components of cancer and that this is a progressive process , increasing significantly towards invasion and metastatic cancer . This in turn suggests that the level of MESC hypomethylation in primary tumours may be an important determinant of clinical outcome . Indeed , our third key result is the report of a stem cell ( MESC ) DNA hypomethylation signature that can predict clinical outcome in multiple human cancers , independently of known prognostic factors . To the best of our knowledge this constitutes the first report of a common prognostic signature in cancer that is based on DNA methylation , and is therefore an epigenetic analogue to the prognostic genomic instability signature presented in [3] . Besides the key distinction of PCGT and MESC CpGs , we also observed that the localisation of CpGs in relation to PMDs was another important facet of the pattern of DNA methylation changes . Specifically , PCGT hypermethylation was observed preferentially within PMDs , while the progressive MESC hypomethylation in cancer was equally strong in PMDs and non-PMDs . We point out that while the PMDs considered here were defined for colon cancer cells , that these broad regions of partial methylation overlap significantly between colon tissue and fibroblasts , suggesting that these regions may be largely similar also between different tissues . The similarities between normal developmental and cancer epigenetic programming are intriguing . While embryonic stem cells suppress differentiation-inducing genes reversibly via promoter occupancy of PRC2 , cancer cells suppress these same genes much more robustly via covalent DNA modification . Even more interestingly , trophoblast cells whose core function is to invade the maternal tissue and form the placenta , are relatively more hypomethylated compared with the inner cell mass , which will differentiate into the embryo [28] , supporting the view that hypomethylation may be associated with the capacity to invade neighbouring tissue such as the maternal endometrium . Similarly , the observed correlation between MESC hypomethylation and the malignant potential of cancers suggests that fully methylated MESCs may provide a protective mechanism against invasion . Thus , the fact that the great majority of MESCs exhibit similar high methylation levels in stem cells and normal tissues , means that high MESC methylation may be viewed as an intrinsic property of any normal cell , regardless of whether it is a stem cell or a mature differentiated one . In this model then , hypomethylation at MESCs would lead to a transformed cellular phenotype that is more prone to invasion . In this context however , it is worth pointing out that the observed MESC hypomethylation could also be reflecting changes in the stromal cell content of the tumours . Indeed , the observation that cancer fibroblasts show similar hypomethylation changes at MESC loci suggests that the more frequent MESC hypomethylation in invasive cancers could be partly due to increased numbers of cancer fibroblasts . It could also be argued that the other DNA methylation changes we have reported here are the result of changes in the stromal and immune cell compartments of the tumours . However , we verified using Principal Components Analysis ( PCA ) and GSEA analysis [26] on normal liquid based cytology ( LBC ) samples and separately on age-matched cervical dysplasias ( Table 1 , “Dy”-study ) that the components of variation associated with stromal and immune cell markers were very similar between normal and dysplasia , in stark contrast to PCGTs which showed a dramatic difference with comparatively no variation in normal tissue but representing the dominant component of variation in dysplasia ( manuscript in preparation ) . Thus , the DNA methylation changes at PCGT loci reported here are unlikely to be due to changes in the stromal cell composition of tumours . Finally , the crucial role of TET3 in DNA demethylation and early development , its overexpression in cancer , and the observed correlation with MESC hypomethylation , supports the view that aberrant developmental programs leading to reprogramming of the epigenome in adult cells may be critical for carcinogenesis . Interfering with these aberrant programs may therefore lead to novel ways to treat cancer . In summary , our findings suggest that epigenetic deregulation of two distinct sets of genes , both important for stem cell integrity , impact carcinogenesis in different ways: one process involves gain of methylation and is a hallmark of de-differentiation and early oncogenesis , while the other involves loss of methylation and is a key determinant of invasion and clinical outcome .
A recent study used bisulfite sequencing to map , at single-base-resolution , DNA methylation throughout the majority of the human genome in both embryonic stem cells and fibroblasts [12] . For each CpG site , the number of C and T reads covering each methyl cytosine on both forward and reverse strands were provided [12] . The multiple reads covering each methyl cytosine can be used as readout of the fraction of sequences within the sample that are methylated at that particular site ( i . e . C reads/C+T reads ) [29] , and hence , referred as the methylation level of the site . In this study , Methylated in human Embryonic Stem Cells ( MESC ) CpGs are the CpG sites that were covered by at least 5 reads on both forward and reverse strands ( i . e . the total number of C and T reads on both strands > = 5 ) and the overall mean methylation levels ( i . e . the average methylation level of both the forward and reverse strands ) is greater than 80% . MESC CpGs were then mapped to those present on the Illumina 27 k array ( Table S1 ) . Functional annotation ( gene assignment ) of the MESC CpGs present on the array was obtained from Illumina and Bioconductor annotation packages . PolyComb Group Target genes ( PCGTs ) were defined as CpGs which are occupied by SUZ12 and/or EeD and/or are trimethylated at Lysine 27 on histone H3 in human embryonic stem cells ( Table S2 , annotation file kindly provided by Benjamin P . Berman and Peter W . Laird ) [19] .
|
DNA methylation is an important chemical modification of DNA that can affect and regulate the activity of genes in human tissue . Abnormal DNA methylation and its subsequent effects on gene activity are a hallmark of cancer , yet when precisely these DNA methylation changes occur and how they contribute to the development of cancer remains largely unexplored . In this work we measure the methylation state of DNA at over 14 , 000 genes in over 1 , 475 samples , including normal and benign cells , invasive cancers , and metastatic cancer tissue . Using cervical cancer as a model , we show that gain of abnormal methylation at genes typically un-methylated in stem cells can be detected up to 3 years in advance of the appearance of pre-cancerous cells , while those genes typically methylated in stem cells lose this methylation progressively throughout cancer development . Furthermore , we discover that this process of methylation loss during cancer progression is a marker of poor disease outcome common to all four major women-specific cancers: breast , ovarian , endometrial , and cervical cancers . Finally we demonstrate the relationship between loss of methylation and cancer-specific over-production of a specific protein known to play an active role in removing methylation from DNA . Taken together these findings highlight the complex nature of DNA methylation dynamics in cancer development as well as their potential exploitation for clinical gain .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"epigenetics",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
The Dynamics and Prognostic Potential of DNA Methylation Changes at Stem Cell Gene Loci in Women's Cancer
|
Plant responses to low temperature are tightly associated with defense responses . We previously characterized the chilling-sensitive mutant chs3-1 resulting from the activation of the Toll and interleukin 1 receptor-nucleotide binding-leucine-rich repeat ( TIR-NB-LRR ) -type resistance ( R ) protein harboring a C-terminal LIM ( Lin-11 , Isl-1 and Mec-3 domains ) domain . Here we report the identification of a suppressor of chs3 , ibr5-7 ( indole-3-butyric acid response 5 ) , which largely suppresses chilling-activated defense responses . IBR5 encodes a putative dual-specificity protein phosphatase . The accumulation of CHS3 protein at chilling temperatures is inhibited by the IBR5 mutation . Moreover , chs3-conferred defense phenotypes were synergistically suppressed by mutations in HSP90 and IBR5 . Further analysis showed that IBR5 , with holdase activity , physically associates with CHS3 , HSP90 and SGT1b ( Suppressor of the G2 allele of skp1 ) to form a complex that protects CHS3 . In addition to the positive role of IBR5 in regulating CHS3 , IBR5 is also involved in defense responses mediated by R genes , including SNC1 ( Suppressor of npr1-1 , Constitutive 1 ) , RPS4 ( Resistance to P . syringae 4 ) and RPM1 ( Resistance to Pseudomonas syringae pv . maculicola 1 ) . Thus , the results of the present study reveal a role for IBR5 in the regulation of multiple R protein-mediated defense responses .
Plant growth and development are continuously affected by various environmental stresses , including low temperature and pathogen infection . Emerging evidence has shown that the defense responses of plants are regulated by temperatures [1] . Temperature modulates the defense responses induced by certain types of resistance ( R ) /R-like proteins , including Toll/Interleukin–1 receptor ( TIR ) - nucleotide-binding ( NB ) - leucine-rich repeat ( LRR ) proteins [N ( Resistance to tobacco mosaic virus ) in tobacco; RPP1 ( Recognition of Peronospora parasitica 1 ) -like , RPP4 , RPS4 ( Resistance to P . syringae 4 ) and SNC1 ( Suppressor of npr1-1 , Constitutive 1 ) in Arabidopsis] , CC-NB-LRR proteins [Rx in tomato; RPM1 ( Resistance to Pseudomonas syringae pv . maculicola 1 ) and RPS2 in Arabidopsis] , LRR-TM ( transmembrane-domain ) proteins ( Cf–4 and Cf–9 in tomato ) , TM-CC proteins [RPW8 ( Resistance to Powdery Mildew 8 ) in Arabidopsis] [2–7] , and the TIR-NB protein CHS1 ( chilling sensitive 1 ) in Arabidopsis [8] . A recent study showed that low temperatures ( 10°C to 23°C ) elevate R protein-mediated effector-triggered immunity ( ETI ) , and higher temperatures ( 23°C to 32°C ) lead to a shift in pattern-triggered immunity ( PTI ) signaling in plants [9] . These studies suggest that temperature largely affects the function of R proteins . Recent studies have revealed that a number of components regulate the activities of R proteins , which in turn finely tune defense signaling . Chaperone and co-chaperone proteins , such as the HSP90-SGT1b ( Suppressor of the G2 allele of skp1 ) -RAR1 ( Required for MLA12 resistance 1 ) complex , involve in multiple R protein-mediated defense pathways [10] . Earlier reports have indicated that these complexes are required for the correct folding and/or stability of R proteins [11–13] . However , several recent studies have suggested that both SGT1b and HSP90 play positive roles in the degradation of R proteins , including RPM1 , RPS2 and SNC1 , by the SCF complex [14–16] . The dual functions of R protein regulators ensure that the plant immunity system rapidly and properly responds to pathogen invasion . In addition to these chaperones , many other regulators are also involved in R protein regulation . A series of regulators of SNC1 were identified by screening suppressors and enhancers of snc1-1 , have been shown to regulate the chromatin , transcription and protein levels of SNC1 . These proteins include E1 and E4 ligases , U-box proteins , acetyltransferases , RNA binding proteins , nuclear pore complex components [13 , 17–22] . These results suggest that SNC1 and/or other R proteins are regulated by multiple biological processes including nucleo-cytoplasmic trafficking , transcriptional reprogramming , RNA processing and protein modification . Previous studies have shown that low temperature activates defense responses in plants harboring mutations in R/R-like genes , including CHS1 , CHS2/RPP4 and CHS3 [7 , 8 , 23] . Arabidopsis CHS3 encodes a TIR-NB-LRR-type R protein harboring a C-terminal LIM domain [23 , 24] . The chs3-1 mutant exhibits chilling-sensitive phenotypes , including small stature and increased disease resistance . The SGT1b and RAR1 proteins are required for R protein stability [25–27] . The chs3 chilling-sensitive phenotypes are suppressed in sgt1b and rar1 mutants [23] . However , the molecular regulatory mechanism of the temperature-dependent defense responses through CHS3 remains elusive . In the present study , we identified ibr5-7 as a suppressor of the chilling-sensitive phenotypes of chs3-1 . IBR5 ( Indole-3-Butyric Acid Response 5 ) encodes a dual-specificity MAPK phosphatase , which acts as a positive regulator of plant responses to auxin and ABA [28] . IBR5 physically interacts with MPK12 , and activated MPK12 is dephosphorylated and inactivated by IBR5 , thereby negatively regulating auxin signaling [29] . Here , we observed that ibr5 suppresses the chilling-sensitive phenotypes of chs3-1 independently of MPK12 . Biochemical data showed that IBR5 complexes with CHS3 and HSP90-SGT1b to to stabilize CHS3 . Moreover , IBR5 is involved in the R-gene mediated resistance specified by SNC1 , RPS4 and RPM1 . Thus , IBR5 plays an important role in regulating different R protein-mediated defense responses .
The chs3-1 plants are dwarfed and have small , curly leaves . The defense responses in chs3-1 are constitutively active at 16°C , but this phenotype is alleviated at higher temperatures ( 22°C ) [23] . To understand the molecular mechanism underlying the temperature-dependent cell death in the chs3-1 mutant , we performed a genetic screen to identify suppressors of chs3-1 ( suc ) . The chs3-1 seeds were mutagenized with ethyl methylsulfonate ( EMS ) , and the M2 population was screened for mutants with wild-type morphology at 16°C . Among the suppressors screened , most of the variations were second-site , loss-of-function mutations in CHS3 , thereby rescuing the chs3 chilling-sensitive phenotype . One suppressor harbored a mutation in RAR1 , and two suppressors harbored mutations in SGT1b; these genes are important regulators required for CHS3 function [23] . These results indicate that the genetic screen was effective . Here , we characterized suc5 as a new suppressor of chs3-1 . The chs3 suc5 mutant plants largely resembled wild-type plants when grown at 16°C , except these plants exhibited a slightly smaller stature compared with the wild type and had serrated true leaves ( Fig 1A ) . Previous studies have indicated that extensive cell death and strong defense responses occur in chs3-1 mutants grown at 16°C [23] . To determine whether the suc5 mutation affects these cell death-related phenotypes , chs3-1 plants were grown at 16°C , followed by staining with trypan blue and 3 , 3’-diaminobenzidine ( DAB ) . The suc5 mutation dramatically reduced the extensive cell death observed in chs3-1 mutants grown at 16°C ( Fig 1B ) . Furthermore , the accumulation of hydrogen peroxide ( H2O2 ) in chs3 suc5 plants grown at 16°C was dramatically reduced compared with chs3-1 ( Fig 1C ) . The chs3-1 mutant also accumulates high levels of salicylic acid ( SA ) [23] . To determine whether suc5 inhibits SA accumulation in chs3-1 at 16°C , the endogenous SA level in chs3 suc5 plants was measured . Both the free and total SA levels were dramatically reduced in chs3 suc5 plants grown at 16°C compared with the chs3 mutant ( Fig 1D ) . Because PR genes were highly expressed in chs3-1 , we further examined the expression of the PR genes in chs3 suc5 plants grown at 16°C . Quantitative real-time PCR ( qRT-PCR ) analysis showed that the expression of PR1 , PR2 and CHS3 was significantly reduced in chs3 suc5 plants grown at 16°C ( Fig 1E and 1F ) . Compared with wild-type plants , chs3-1 plants grown at 16°C exhibit enhanced resistance to a virulent pathogenic strain of Pseudomonas syringae pv tomato ( P . s . t . ) , DC3000 [23] . To investigate the role of SUC5 in the chs3-mediated basal defense response , the response of chs3 suc5 seedlings to P . s . t . DC3000 was analyzed . The suc5 mutation fully suppressed the chs3-conferred constitutive resistance to P . s . t . DC3000 , resulting in wild-type-like susceptibility ( Fig 1G ) . Taken together , these results demonstrated that , under chilling stress , the suc5 mutation largely suppresses all known autoimmune phenotypes of chs3 . To map the suc5 mutation , chs3 suc5 in Columbia ( Col ) was crossed with Landsberg erecta ( Ler ) to generate a mapping population . Among the F2 progeny , 50 plants homozygous or heterozygous at the chs3-1 locus and exhibiting a wild-type morphology at 16°C were used for rough mapping . The suc5 mutation was initially mapped to the top of chromosome II ( Fig 2A ) . Fine mapping using approximately 200 plants refined the mutation to a 500-kb region between markers F3C11 and F5G3 ( Fig 2A ) . Further sequencing analysis of this region in chs3 suc5 revealed a G-to-A substitution in the first exon of At2g04550 , resulting in an amino substitution from Arg to Lys ( Fig 2A ) . At2g04550 was previously identified as IBA RESPONSE5 ( IBR5 ) , which encodes a putative dual-specificity protein phosphatase [28] . To determine whether the mutation in IBR5 is responsible for the suppression the phenotypes of chs3 , a chs3 ibr5-3 double mutant was generated by crossing chs3-1 with ibr5-3 [29] . The ibr5-3 mutant largely restored the growth defects of chs3-1 mutant plants grown at 16°C ( Fig 2B ) . Furthermore , a wild-type genomic IBR5 fragment containing 5 . 0 kb of the 5’-promoter region and the 3’ untranslated region was transformed into the chs3 suc5 mutant . All eight T1 transgenic plants displayed chs3-conferred morphological and cell death phenotypes ( Fig 2B and 2C ) . Taken together , these results indicate that SUC5 is indeed IBR5 . Therefore , suc5 was renamed ibr5-7 . The ibr5-7 single mutant was isolated by crossing chs3 ibr5-7 with wild-type Col plants . Immunoblot analysis showed that the IBR5 protein level in ibr5-7 was lower than that in the wild-type Col plants , whereas no IBR5 protein was detected in ibr5-3 ( Fig 2D ) . Intriguingly , IBR5 protein accumulated in the chs3-1 mutant ( Fig 2D ) , implying that IBR5 might stabilize CHS3 protein . Consistent with a previous study on ibr5 loss-of-function mutants [28] , ibr5-7 displayed serrated leaves and was resistant to high concentrations of exogenous auxins , including IAA , IBA and 2 , 4-D ( S1 Fig ) . These results indicate that ibr5-7 is a novel null allele of IBR5 . To further investigate whether IBR5 physically interacts with CHS3 in CHS3-mediated signaling , a yeast two-hybrid assay was performed . As a TIR-NB-LRR-LIM-containing protein , the full-length CHS3 protein is approximately 185 kD and is difficult to express in yeast . Therefore , truncated constructs individually carrying the TIR , NB , LRR , unknown and LIM domains of CHS3 were generated and transformed into yeast ( Fig 3A ) . IBR5 directly interacted with the TIR domain of CHS3 in yeast ( Fig 3B ) . Previous studies have shown that CHS3 localizes to the nucleus [24] , consistent with the results of the present study ( S2B Fig ) . We also observed that IBR5 localized to both the cytosol and the nucleus ( S2A and S2B Fig ) . To determine whether CHS3 interacts with IBR5 in vivo , 35S:HA-Flag-IBR5 ( HF-IBR5 ) and the Myc-tagged TIR domain of CHS3 driven by the Super promoter ( TIR-Myc ) were transiently expressed in Arabidopsis mesophyll protoplasts , and subsequently , a co-immunoprecipitation ( co-IP ) assay was performed . IBR5 precipitated the TIR domain of CHS3 ( Fig 3C ) . Moreover , HF-IBR5 interacted with full-length CHS3-1-Myc ( the mutated form of CHS3 containing the same mutation as the chs3-1 mutant ) when expressed in N . benthamiana ( Fig 3D ) . These results indicated that CHS3 interacts with IBR5 through the TIR domain of CHS3 in vivo . IBR5 belongs to a family of dual specificity protein phosphatases ( DSPs ) , and the conserved cysteine in the conserved motif ( VxVHCx2GxSRSx5AYLM ) of DSPs is necessary for catalytic activity in many DSP proteins , including IBR5 and its homolog DsPTP1 [30] . To examine whether the phosphatase activity of IBR5 is required for interactions with CHS3 , we generated the mutated form of IBR5 ( IBR5C129S ) . The yeast two-hybrid assay showed that the mutation did not affect the interaction of IBR5 and CHS3 ( Fig 3B ) , suggesting that the catalytic activity is not necessary for the interaction between IBR5 and CHS3 . We next introduced Super:IBR5-Myc and Super:IBR5C129S-Myc into the chs3 ibr5 mutant background ( Fig 3E , 3F and 3G ) . As a control , IBR5-Myc fully recovered the chs3 ibr5 phenotype and PR1 expression . However , the morphological phenotypes and PR1 expression of chs3 ibr5 were partially rescued in the mutated IBR5C129S-Myc plants ( Fig 3F and 3G ) . These results suggest that the catalytic activity of IBR5 is required for full function in the CHS3-mediated defense response . Another gain-of-function chs3 mutant , chs3-2D , was dwarfed and exhibited a constitutively active defense phenotype at 22°C [24] . Moreover , chs3:chs3-2D-GFP transgenic plants were generated , exhibiting chs3-2D-like phenotypes [24] . To examine whether IBR5 affects the accumulation of CHS3 in chs3-2D-GFP plants , ibr5-3 chs3-2D-GFP plants were generated by crossing ibr5-3 with chs3-2D-GFP plants ( Fig 4A ) . The dwarf phenotype of chs3-2D-GFP plants was fully restored by ibr5-3 ( Fig 4B ) , and GFP signals were observed in the nuclei of chs3-2D-GFP transgenic plants ( Fig 4C ) . However , no obvious GFP signal was detectable in ibr5-3 chs3-2D-GFP plants ( Fig 4C ) . Consistently , the expression of PR genes in chs3-2D-GFP was also suppressed in ibr5-3 plants ( Fig 4D ) . The expression of CHS3 in ibr5-3 chs3-2D-GFP plants was also examined . As shown in Fig 4E , CHS3 expression in ibr5-3 chs3-2D-GFP plants was dramatically decreased compared with chs3-2D-GFP plants , consistent with the result obtained in chs3 ibr5-7 ( Fig 1F ) . Moreover , the expression of CHS3 was down-regulated in the ibr5-3 mutant ( Fig 4F ) . These results suggest that IBR5 positively regulates the expression of CHS3 , which might at least be partially responsible for the decreased CHS3-2D-GFP signals in ibr5-3 chs3-2D-GFP . To dissect the influence of IBR5 on CHS3 at the protein level , we examined the effect of IBR5 on the CHS3 protein level in Arabidopsis protoplasts . The protein level of CHS3-1-Myc in protoplasts co-expressing CHS3-1-Myc and HF-IBR5 was much higher than that in protoplasts expressing CHS3-1-Myc and HF ( Fig 4G ) . This result suggests that IBR5 also promotes the accumulation of CHS3 protein in plant . HSP90 plays an important role in the stability of R proteins [31] . A previous study showed that the hsp90 . 3–1 mutant was a suppressor of the chs2/rpp4-1d mutant [32] . Therefore , we examined whether hsp90 . 3–1 rescues the chilling-sensitive phenotype of chs3-1 . The chs3-1 hsp90 . 3–1 double mutant was generated and largely showed wild-type morphology but was smaller at 16°C ( Fig 5A ) , and cell death was dramatically suppressed ( Fig 5B ) . PR1 expression was also significantly inhibited in the double mutant ( Fig 5C ) . Further analysis showed that the F1 progeny of ibr5 chs3 and hsp90 . 3 chs3 could partially rescue the morphology , cell death and PR1 gene expression of chs3 ( Fig 5A , 5B and 5C ) . Moreover , the chs3 hsp90 . 3 ibr5 triple mutant more closely resembled wild-type plants than the chs3 hsp90 and chs3 ibr5 mutants in terms of growth , cell death and PR1 gene expression ( Fig 5A , 5B and 5C ) . These data indicate that HSP90 and IBR5 synergistically modulate the chs3-conferred growth and defense responses . HSP90 functions as a complex with SGT1b and RAR1 [11 , 33–35] and we previously showed that sgt1b and rar1 suppressed the phenotypes of chs3-1 [23] , similar to ibr5 ( Fig 1 ) . Next we determined whether IBR5 physically interacts with HSP90 and SGT1b in plants . To this end , a luciferase complementation imaging ( LCI ) assay was performed in Nicotiana benthamiana . IBR5 interacted with HSP90 and SGT1b in N . benthamiana leaves ( Fig 6A and 6B ) . Furthermore , co-IP assays showed that endogenous HSP90 successfully immunoprecipitated IBR5-Myc in transgenic plants expressing IBR5-Myc , but not in Myc transgenic plants ( Fig 6C ) . Similarly , SGT1b could be co-immunoprecipitated with IBR5 in transgenic plants expressing IBR5-Myc and SGT1b-FLAG , but not in transgenic plants expressing Myc and SGT1b-FLAG ( Fig 6D ) . We also examined the association between SGT1b and CHS3 . Co-IP assays showed that SGT1b associated with the TIR domain of CHS3 and full-length CHS3-1 in vivo ( S3 Fig ) . Furthermore , we examined the interaction of CHS3-1 with IBR5 and SGT1b in N . benthamiana leaves . As shown in Fig 6E , CHS3-1 could simultaneously pull down IBR5 and SGT1b in plants . These results indicate that IBR5 forms complex ( es ) with CHS3 , HSP90 and SGT1b in vivo . We subsequently investigated whether IBR5 protects proteins in vitro using a thermal aggregation assay with citrate synthase ( CS ) as a model substrate [36] . CS aggregation was examined after measuring the absorbance at 500 nm under thermally denaturing conditions ( 40°C or above ) . Heat-induced aggregation of CS was inhibited by HSP90 ( Fig 6F ) , consistent with the results of previous study [37] . IBR5 showed a weaker but more significant effect than HSP90 on the inhibition of CS aggregation . When IBR5 was incubated with CS at 43°C , the aggregation of CS was partially suppressed in a dose-dependent manner ( Fig 6F ) . This result indicates that IBR5 protects proteins against aggregation in vitro . The TIR domain of CHS3 shares amino acid similarity with TIR-NB-LRR R proteins SNC1 and RPP4 ( S4 Fig ) . Gain-of-function mutants of SNC1 ( snc1-1 and bal/snc1-2 ) activate the defense response in a temperature-dependent manner [6 , 38] . The loss of BON1 function in Arabidopsis leads to temperature-sensitive growth and autoimmune phenotypes resulting from the activation of SNC1 [6] . Phenotype analyses showed that the bon1-1 ibr5-3 ( bon1 ibr5 ) and bal ibr5-3 ( bal ibr5 ) double mutants were slightly larger than the bon1-1 and bal single mutants ( Fig 7A and 7B ) . Moreover , the cell death in bon1 ibr5 and bal ibr5 was reduced ( Fig 7C ) . Consistently , the expression of PR1 and PR2 genes in bon1 ibr5 and bal ibr5 was also lower than that in the bon1-1 and bal/snc1-2 mutants ( Fig 7D ) . We also examined the basal defense response of bon1 ibr5 and bal ibr5 to P . s . t . DC3000 . The pathogen resistance of bon1-1 and bal/snc1-2 mutants to P . s . t . DC3000 was slightly suppressed in the ibr5-3 mutant ( Fig 7E and 7F ) . These genetic data suggest that IBR5 is partially implicated in the SNC1-mediated defense response . To further dissect the function of IBR5 on SNC1 , we examined the expression of SNC1 in the ibr5-3 mutant . As shown in Fig 4F , the SNC1 transcript levels in the ibr5-3 mutant were much lower than those in Col , indicating that SNC1 expression is positively regulated by IBR5 . We next explored whether IBR5 physically interacts with SNC1 . A yeast two-hybrid assay showed that IBR5 interacted with the TIR domain of SNC1 ( Fig 8A ) . The interaction of IBR5 and full-length SNC1-1 ( the mutated form of SNC1 containing the same mutation as the snc1-1 mutant ) was confirmed by a co-IP assay in N . benthamiana leaves ( Fig 8B ) . Moreover , the protein level of SNC1-1-Myc increased when co-expressed with HF-IBR5 in Arabidopsis protoplasts ( Fig 8C ) . Taken together , these data suggest that IBR5 might also promote SNC1 protein accumulation . Our previous studies showed that the mutations in R protein CHS2/RPP4 or R-like protein CHS1 induce plant sensitivity and activate defense responses under chilling stress [7 , 8] . To investigate whether ibr5 also suppresses the chilling sensitivity of chs1-2 and chs2-1/rpp4-1d , we generated chs1-2 ibr5-3 ( chs1 ibr5 ) and chs2-1 ibr5-4 ( chs2 ibr5 ) double mutants . In terms of morphology and cell death , chs1 ibr5 and chs2 ibr5 mutants showed chs1-2 and chs2-1 single mutant phenotypes under chilling stress ( S5A and S5B Fig ) . However , PR1 expression in chs1 ibr5 was partially suppressed , whereas the expression of this protein in chs2 ibr5 was not obviously affected . A yeast two-hybrid assay showed that IBR5 did not interact with RPP4 ( Fig 8A ) . We also examined the pathogen resistance of ibr5 mutants to RPP4-specific Hyaloperonospora arabidopsidis ( H . a . ) Emwa1 . As controls , Col was completely resistant to H . a . Emwa1 , whereas rpp4-r26 , which contains a mutation in RPP4 resulting in the introduction of a stop codon in front of the LRR domain [32] , was susceptible to H . a . Emwa1 ( S6 Fig ) . The ibr5-3 and ibr5-7 mutants showed complete resistance to H . a . Emwa1 ( S6 Fig ) , suggesting that IBR5 might not be involved in RPP4-mediated oomycete resistance . To examine whether basal defense was affected in the ibr5 single mutant , we infected wild-type Col and ibr5 mutants with virulent P . s . t . DC3000 . The bacterial growth in ibr5-3 and ibr5-7 was comparable to that in wild-type plants , whereas as the control , pad4-1 , supported 100 times more bacterial growth than the wild-type plants ( Fig 9A ) , suggesting that IBR5 does not play an important role in basal defense . As the role of CHS3 is compromised in the ibr5 mutant , we further investigated whether IBR5 is required for other R-gene-mediated disease resistance . The ibr5 mutants were inoculated with P . s . t . DC3000 carrying either avrRpm1 , avrRps4 , or avrRpt2 . The ibr5 mutants showed enhanced susceptibility to P . s . t . DC3000 ( avrRpm1 ) and P . s . t . DC3000 ( avrRps4 ) ( Fig 9B and 9C ) . In contrast , no remarkable difference in the growth of P . s . t . DC3000 ( avrRpt2 ) was detected between the ibr5 mutants and wild-type Col ( Fig 8D ) . As a control , pad4-1 showed enhanced disease susceptibility to all three avirulent bacterial strains ( Fig 9B , 9C and 9D ) . These results suggest that IBR5 might also contribute to disease resistance mediated by RPM1 and RPS4 . We next examined the expression of RPM1 and RPS4 in the ibr5-3 mutant . The transcription levels of RPM1 and RPS4 were slightly down-regulated in the ibr5-3 mutant compared with wild type plants ( Fig 4F ) . Furthermore , we examined whether IBR5 interacts with TIR-NB-LRR protein RPS4 or CC-NB-LRR protein RPM1 . The results of a yeast two-hybrid assay showed that neither wild-type IBR5 nor mutated IBR5C129S interacted with the TIR domain of RPS4 or CC domain of RPM1 ( S7A Fig ) . However , interestingly , we observed the direct interaction of IBR5 and IBR5C129S with RPS4-interacting protein RRS1 [39] in yeast ( S7B Fig ) . The results of additional co-IP assays showed that IBR5 could pull down RPS4 ( S7C Fig ) , suggesting that IBR5 might form a complex with RPS4 and RRS1 . In contrast , no interaction between IBR5 and RPM1 was observed in plants ( S7D Fig ) . Furthermore , no obvious change in the RPM1 protein level was detected when IBR5 and RPM1 were co-expressed ( S7E Fig ) .
CHS3 is involved in temperature-dependent defense responses [23] . In the present study , we identified ibr5-7 as a suppressor of chs3-1 . IBR5 interacts with CHS3 and HSP90/SGT1b chaperones to stabilize the CHS3 protein , thereby modulating temperature-dependent defense responses . In addition , IBR5 is invovled in defense responses mediated by several other R genes , including SNC1 , RPS4 and RPM1 . IBR5 is a MAP kinase phosphatase ( MKP ) that dephosphorylates activated MAPKs [40 , 41] . Emerging evidence has shown that MKPs play important roles in modulating plant defense responses [41] . For example , the mkp1 null mutation in the Col accession exhibits constitutive stress response phenotypes , causing a dwarf stature dependent on SNC1 [42] . However , it remains unclear whether the substrates of MKP1 , MPK3 and MPK6 [42 , 43] are involved in the regulation of SNC1 . Additional studies have revealed that MKP1 functions as a negative regulator of MPK6-mediated pathogen-associated molecular pattern ( PAMP ) responses and disease resistance [44] . The mkp2 null mutant displays delayed symptoms of disease induced by Ralstonia solanacearum , and shows increased susceptibility to Botrytis cinerea [45] . MKP2 might regulate MPK3/MPK6 activity during different pathogen defense responses . Moreover , MKP2 reverses hypersensitive-like responses triggered by MPK6 upon plant treatment with fungal elicitors [45 , 46] . It is likely that these MKPs have negative functions in defense signaling pathways through their MPK substrates . The data obtained in the present study showed that the loss of IBR5 function suppresses chs3-conferred , temperature-dependent defense responses and slightly weakens the autoimmunity of bal/snc1-2 , a gain-of-function mutant of a TIR-NB-LRR protein SNC1 . Moreover , resistance mediated by R proteins , such as RPS4 and RPM1 , is compromised in the ibr5 mutants . These results suggest that IBR5 plays a positive role in the ETI pathway mediated by multiple R proteins . Therefore , different MKPs play either positive or negative roles in plant immunity . As IBR5 is a MAP kinase phosphatase , we also examined whether the dephosphorylation activity of IBR5 is required for the regulation of CHS3 . The inactive form of IBR5 ( IBR5C129S ) partially rescues the phenotype of chs3 ibr5 , suggesting that the dephosphorylation activity of IBR5 is necessary for the complete function of this protein in the CHS3-mediated defense response . MPK12 is a substrate of IBR5 [29] , and the reduced expression of MPK12 partially complements auxin , but not ABA insensitivity , in the ibr5 mutant , suggesting that there might be other substrate ( s ) of IBR5 involved in IBR5-mediated ABA responses [29] . In the present study , the loss-of-function mutant mpk12-3 displayed an auxin-sensitive phenotype ( S8A , S8B and S8C Fig ) , similar to that of mpk12 RNAi lines [29] . However , mpk12-3 cannot rescue the phenotype of chs3 ibr5 ( S8D Fig ) , indicating that the CHS3-mediated defense response does not require MPK12 . Moreover , the mutant of auxin receptor tir1 enhances the auxin insensitive phenotype of ibr5 , implying that IBR5 is involved in the auxin response independently of the TIR1-mediated auxin signaling pathway [30] . Therefore , the role of IBR5 in R-mediated resistance might not occur through the function of IBR5 in the auxin response . ibr5 mutant is shown to be insensitive to ABA , but the underlying mechanism is completely unknown [28] . ABA signaling plays a role in defense responses in a complicated manner . Recent studies have reported that the overexpression of ABA receptors maintains stomatal closure during P . s . t . DC3000 infections , leading to enhanced resistance after dipping , but enhanced susceptibility to the pathogen after infiltration [47] . In contrast , several ABA signaling mutants , such as abi1 and abi4 , do not exhibit prominent phenotypes under the snc1-1 background or during pathogen infection [48] . The ABA-insensitive mutant cpr22 , containing a deletion on two cyclic nucleotide-gated ion channel genes , shows constitutive PR gene expression , enhanced pathogen resistance and SA accumulation [49] . WRKY40 , WRKY18 , and WRKY60 play negative roles in ABA signaling [50] . The single and triple mutants show diverse phenotypes to different pathogens , as these mutants are more resistant to P . syringae but more susceptible to B . cinerea compared with the wild type [50] . Whether the role of IBR5 in the ABA response is involved in CHS3-mediated defense pathway remains unknown . In plants , the TIR domain is indispensable for defense signal transduction [51] . For example , the transient expression of the TIR domain of RPS4 in N . benthamiana leaves triggers cell death in a manner dependent on both SGT1 and HSP90 [13 , 51] . The TIR domain of L6 , a TIR-NB-LRR R protein in flax , forms a homodimer required for immune signaling [52] . A recent study showed that TIR domain heterodimerization is necessary to form a functional RRS1/RPS4 effector recognition complex [9] , indicating the importance of the TIR domain of R proteins in signal recognition and transduction . Moreover , the TIR domain of R proteins interacts with other proteins [39 , 53–56] . In the present study , we observed that the TIR domain of CHS3 interacts with IBR5 in vitro and in vivo . The mutation of IBR5C129S does not affect this interaction , suggesting that the dephosphatase activity of IBR5 is not a prerequisite for the interaction between IBR5 and CHS3 . We also examined the interaction between IBR5 and four other TIR-NB-LRR R proteins: SNC1 , RPP4 , RPS4 and its interacting R protein RRS1 [39] , and CC-NB-LRR , RPM1 . Under these conditions , IBR5 interacts with SNC1 and RRS1/RPS4 in plant , but not with RPP4 and RPM1 . Consistently , ibr5 partially suppresses the cell death and PR gene expression in bal/snc1-2 but does not suppress the expression of these proteins in chs2/rpp4-1d . These data suggest that IBR5 might specifically interact with certain TIR-NB-LRR proteins to regulate defense signaling . However , the mechanism underlying the involvement of IBR5 in defense responses mediated by the CC-type R protein remain unclear . Increasing evidence has shown that HSP90 , SGT1 and RAR1 form a protein complex with chaperone activity required for the proper folding and stabilization of R proteins [11 , 23 , 33 , 34 , 57–60] . In this complex , HSP90 and RAR1 increase R protein levels [57] . The phosphatase PP5 interacts with HSP90 and the tomato R protein I–2 , and together with HSP90 , PP5 acts as a co-chaperone [61] . In the present study , we found an interaction between the phosphatase IBR5 and the HSP90/SGT1B/RAR1 complex in vivo , suggesting that IBR5 complexes with these chaperone proteins and stabilizes CHS3 . Increasing evidence further supports the notion: ( 1 ) The in vitro holdase activity of IBR5 indicates that this enzyme markedly inhibits temperature-dependent CS degradation . ( 2 ) The F1 progeny of chs3 ibr5 and chs3 hsp90 partially inhibits the chs3 phenotype . Moreover , the chs3 ib5 hsp90 triple mutant is larger than both chs3 hsp90 and chs3 ibr5 and is reminiscent of the wild type . We speculate that different complexes comprising IBR5 and HSP90 might play roles in CHS3-mediated signaling . This involvement might reflect the dosage effect of IBR5 and HSP90 . A similar mechanism was observed for the COI1 ( CORONATINE INSENSITIVE 1 ) and HSP90 proteins in regulating RPM1-mediated disease resistance [62] . ( 3 ) chs3-dependent phenotypes are suppressed by sgt1b , rar1 [23] and hsp90 . 3–1 , indicating that HSP90 , RAR1 and SGT1b are required for CHS3 activation . ( 4 ) IBR5 interacts with the HSP90 , SGT1b and CHS3 proteins in vivo . Collectively , these data show that IBR5 associates with HSP90/RAR1/SGT1b to stabilize the CHS3 protein . Because SGT1b interacts with a core component of the SKP1-CULLIN1-F-box ( SCF ) E3 ligase complex , S-phase kinase-associated protein 1 ( SKP1 ) , SGT1b has been considered as a subunit of the SCF complex [63 , 64] . Recent studies have shown that SNC1 is degraded by the SCFCPR1 complex [65 , 66] . In a protein-protein interactome study , IBR5 was also shown to interact with SKP1 [67] . These results suggest that IBR5 , in concert with HSP90/SGT1B proteins , might affect the function of SCF E3 ubiquitin ligase complexes , thereby regulating the ubiquitination and degradation of some R proteins . Further studies will elucidate the molecular mechanisms underlying the roles of IBR5 in R-mediated defense responses .
Arabidopsis thaliana Col–0 , chs3-1 [23] , ibr5-3 [29] , pad4-1 [68] , hsp90 . 3–1 , rpp4-r26 [32] , and mpk12-3 ( SAIL_543_F07 ) were used in this study . Plants were grown in soil or on Murashige and Skoog ( MS ) medium containing 2% ( w/v ) Suc and 0 . 8% ( w/v ) agar at 16°C or 22°C with a 16-h light/8-h dark cycle under white light . Salicylic acid measurement and were described previously [23] . The suppressors of chs3-1 were screened and mapped as described previously [23] . Approximately 200 plants with chs3-1 background with wild-type morphology at 16°C were used for mapping to a 500-kb region between markers F3C11 and F5G3 . Full length genomic DNA of candidate genes was amplified by PCR and sequenced to find the mutant site . To confirm the mutated gene , genomic complementation and T-DNA insertion mutant were used . Trypan blue and DAB staining was performed as previously described [69 , 70] . The pathogen resistance assay on Pseudomonas syringae pv tomato ( P . s . t . ) strain DC3000 was performed as described previously [32] . Briefly , 3-week-old plants were dipped by the bacterial suspension containing 10 mM MgCl2 and 0 . 025% Silwet L–77 . The concentrations of pathogen for virulent strain ( P . s . t . DC3000 ) and avirulent strains ( P . s . t . DC3000 with avrRmp1 , avrRpt2 and avrRps4 ) are OD600 of 0 . 05 and 0 . 2 , respectively . The dipped leaves were collected at 2 hour and 4 day post-inoculation . Three replicate samples were collected to measure the resistance to pathogen . Total RNA isolated from 2-week-old plants grown at 22°C or 3-week-old plants grown at 16°C using TRIzol reagent ( Invitrogen ) . The quantitative real-time PCR was performed using a SYBR Green PCR Master Mix kit ( Takara ) . The relative expression levels were calculated as described ( Huang et al . , 2010 ) . Gene-specific primers were listed in S1 Table . To obtain the IBR5::IBR5 construct , the genome fragment of IBR5 ( from ~2kb upstream of ATG to stop codon ) was amplified and cloned into pCAMBIA1300 vector . To generate Super:IBR5 and Super:SGT1b constructs , cDNAs of IBR5 and SGT1b were amplified and cloned into the pSuper1300-Myc/GFP/Flag vectors containing a Super promoter . To generate Super:CHS3-1-Myc , Super:SNC1-1-Myc , Super:RPM1-Myc and Super:RPS4-Myc , genomic DNA of chs3-1 , snc1-1 , RPM1 and RPS4 were amplified and cloned into the pSuper1300-Myc vectors containing a Super promoter . The primers were listed in S1 Table . Agrobacterium tumefaciens stain of GV3101 carrying different constructs was transformed into wild-type or mutant plants via standard floral dipping method [71] . To create bait and prey plasmids , fragments were amplified by different primers and cloned into pGBKT7 and pGADT7 . All primer sequences are listed in S1 Table . Bait and prey plasmids were co-transformed into yeast strain AH109 . Yeast strains containing the bait and prey plasmids were cultured in SD-Trp-Leu liquid medium to OD600 = 0 . 4–0 . 6 . Interaction was tested on SD-Trp-Leu-His-Ade medium . cDNAs of HSP90 and SGT1b were amplified and cloned into nLuc vector . IBR5 cDNA was amplified and cloned into cLuc vector . A . tumefaciens strain GV3101 containing different constructs was infiltrated into N . benthamiana leaves according to the protocol described previously [72] . Firefly Luciferase Complementation Imaging ( LCI ) assay was performed as described [73] . To clone the IBR5-His construct , IBR5 cDNA was amplified and cloned into pQE80L vector . HSP90 fragment was amplified and cloned into pMalC2 vector . The thermal aggregation assay was performed as described [74] with some modifications . Citrate synthase from porcine heart ( Sigma-Aldrich ) was used as substrate . Light scattering was measured by a thermostatically controlled fluorescence spectrophotometer ( F–7000 ) . Proteins were diluted by 40 mM HEPES/KOH ( pH 7 . 5 ) and the reaction temperature was kept at 43°C . The plasmids were purified and transformed into Arabidopsis mesophyll protoplasts [75] or N . benthamiana leaves [72] . Proteins were extracted with extracting buffer containing 150 mM NaCl , 10 mM Tris-HCl pH 7 . 6 , 2 mM EDTA , 0 . 5% NP–40 and 1 x protease inhibitor cocktail ( Roche ) . The protein extracts were then incubated with anti-Myc beads ( Sigma-Aldrich ) at 4°C for 2 hour . After washing five times with extraction buffer , the samples were used for immunoblotting with anti-HA ( Sigma-Aldrich ) or anti-Flag ( Sigma-Aldrich ) antibodies .
|
Resistance ( R ) genes play central roles in recognizing pathogens and triggering plant defense responses . CHS3 encodes a TIR-NB-LRR-type R protein harboring a C-terminal LIM domain . A point mutation in CHS3 activates the defense response under chilling stress . Here we identified and characterized ibr5-7 , a mutant that suppresses the chilling-induced defense responses of chs3-1 . We observed that the enhanced defense responses and cell death in the chs3-1 mutant are synergistically dependent on IBR5 and HSP90 . IBR5 physically interacts with CHS3 , forming a complex with SGT1b/ HSP90 . Moreover , IBR5 is also involved in the R-gene resistance mediated by SNC1 , RPS4 and RPM1 . Thus , IBR5 plays key roles in regulating defense responses mediated by multiple R proteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
IBR5 Modulates Temperature-Dependent, R Protein CHS3-Mediated Defense Responses in Arabidopsis
|
Severe outcomes have been described for both Plasmodium falciparum and P . vivax infections . The identification of sensitive and reliable markers of disease severity is fundamental to improving patient care . An intense pro-inflammatory response with oxidative stress and production of reactive oxygen species is present in malaria . Inflammatory cytokines such as tumor necrosis factor-alpha ( TNF-alpha ) and antioxidant agents such as superoxide dismutase-1 ( SOD-1 ) are likely candidate biomarkers for disease severity . Here we tested whether plasma levels of SOD-1 could serve as a biomarker of severe vivax malaria . Plasma samples were obtained from residents of the Brazilian Amazon with a high risk for P . vivax transmission . Malaria diagnosis was made by both microscopy and nested PCR . A total of 219 individuals were enrolled: non-infected volunteers ( n = 90 ) and individuals with vivax malaria: asymptomatic ( n = 60 ) , mild ( n = 50 ) and severe infection ( n = 19 ) . SOD-1 was directly associated with parasitaemia , plasma creatinine and alanine amino-transaminase levels , while TNF-alpha correlated only with the later enzyme . The predictive power of SOD-1 and TNF-alpha levels was compared . SOD-1 protein levels were more effective at predicting vivax malaria severity than TNF-alpha . For discrimination of mild infection , elevated SOD-1 levels showed greater sensitivity than TNF-alpha ( 76% vs . 30% respectively; p<0 . 0001 ) , with higher specificity ( 100% vs . 97%; p<0 . 0001 ) . In predicting severe vivax malaria , SOD-1 levels exhibited higher sensitivity than TNF-alpha ( 80% vs . 56% , respectively; p<0 . 0001; likelihood ratio: 7 . 45 vs . 3 . 14; p<0 . 0001 ) . Neither SOD-1 nor TNF-alpha could discriminate P . vivax infections from those caused by P . falciparum . SOD-1 is a powerful predictor of disease severity in individuals with different clinical presentations of vivax malaria .
Severe malaria presents a relevant public health problem worldwide , affecting the socio-economic development of many communities . The identification of predictors of disease severity is critical to improve patient care . Most of the actual knowledge regarding the immunopathological determinants of malaria severity refers to infection caused by Plasmodium falciparum , but growing evidence also associates vivax malaria with severe complications [1] , [2] . Together with rising documentation of drug resistance worldwide , the complications of Plasmodium vivax infection represents a global health threat . Therefore , identifying markers of disease severity is essential to improve clinical management . Plasma TNF-alpha levels have been described as a biomarker for the estimation of disease severity for P . falciparum [3] and is associated with clinical severity in P . vivax [4] infections , but there is scarce data evaluating or validating more sensitive and reliable predictors of severe disease . During malaria infection , reactive oxygen species ( mainly superoxide anions ) are produced at high levels , inducing parasite killing and tissue damage [5] . To circumvent this biological injury , the anti-oxidant enzyme Cu/Zn superoxide dismutase ( SOD-1 ) converts these unstable free radicals into hydrogen peroxide ( H2O2 ) , which can be removed by the catalase and glutathione systems [6] . Studies in both mice [7] and humans [8] have correlated the SOD-1 activity with tissue damage . Therefore , investigating markers related to oxidative stress could provide useful tools to manage malaria . The present work shows that the plasma level of SOD-1 is a surrogate marker of severe vivax malaria in a population from the Brazilian Amazon , in which P . vivax infection is highly endemic . The performance of SOD-1 as a predictor of disease severity even surpasses that of TNF-alpha .
The objective of this study was to test whether the plasma level of SOD-1 , an antioxidant enzyme , could predict vivax malaria severity with equivalent of better efficacy compared to the currently used marker TNF-alpha . Plasma samples were obtained from individuals living in Buritis , a recently urbanized municipality in Rondônia , Brazilian Amazon , with a high risk for vivax malaria transmission [9] , during June 2006 and August 2007 . Active and passive malaria case detections were performed . These included home visits and study of individuals who sought care at the diagnostic center of Brazilian National Foundation of Health ( FUNASA ) . In addition , patients admitted to the Buritis municipal Hospital with clinical signs of mild or severe malaria [10] were also asked to participate in the study . All individuals from fifteen to seventy years , of both sexes , who had been living in the endemic area for more than six months , were invited to be included in the study . The malaria diagnosis was performed using two methods ( double-blinded ) . First , patients were screened by thick smear examination using field microscopy and the parasitaemia ( parasites/uL ) was quantified in positive cases . Further , nested PCR was performed in all whole blood samples to confirm the diagnosis . Exclusion criteria were viral hepatitis ( A , B , C , and D ) , chronic alcoholism , human immunodeficiency virus type 1 infection , yellow fever , leptospirosis , cancer and chronic degenerative diseases , sickle cell trait and the use of hepatotoxic or immunosuppressant drugs . Two individuals presenting P . malariae infection were identified and excluded from the study . In addition , 16 age-matched people infected with P . falciparum ( uncomplicated forms ) were invited to participate . In the last phase of the study , plasma samples from these individuals with P . falciparum malaria were used in order to assess if the markers compared were useful to discriminate P . vivax from P . falciparum infections . After obtaining the parasitological diagnosis , all vivax malaria positive cases were followed for 30 days . Individuals infected with P . falciparum were not included in the follow up . Infected individuals who remained without any presumptive malaria symptoms were considered asymptomatic; patients presenting clinical or laboratory signs of complicated malaria [10] were considered severe cases , while those who were symptomatic without any complication were mild cases . In hospitalized participants presenting with severe disease , two plasma samples were obtained: one at the hospital admission and other seven days after malaria treatment initiation . Thus , of 415 individuals initially approached , 58 were excluded for meeting exclusion criteria , 86 withdrawn consent and 36 neglected the follow up . The sample was then composed of non-infected volunteers ( n = 90 ) and individuals with different clinical presentations of vivax malaria: asymptomatic ( n = 60 ) , mild ( n = 50 ) and severe infection ( n = 19 ) . The detailed clinical descriptions of the participants together with the outcomes have been already addressed by our group [11] . A summary of the baseline characteristics of the participants is illustrated in Table 1 . All the malaria cases were treated by the FUNASA health care professionals according to the FUNASA standardized protocols . The flow chart of the validation study is shown in Figure S1 . Written informed consent was obtained from all participants , and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki . The project was approved by the institutional review board of the Faculdade de Medicina , Faculdade São Lucas , Rondônia , Brazil , where the study was performed . The molecular diagnosis of malaria was performed using nested PCR , as described previously [12] . Briefly , 300 µL of whole blood collected on EDTA was prepared for DNA extraction through the phenol-chloroform method followed by precipitation with sodium acetate and ethanol . The first PCR rDNA amplification was performed with Plasmodium genus-specific primers named PLU5 and PLU6 . Positive samples yielded a 1 , 200-bp fragment , which served as template for the nested reaction . The nested PCR amplification was performed with species-specific primers for 30 cycles at annealing temperatures of 58°C for P . falciparum ( Fal1 and Fal2 primers ) , and 65°C for P . vivax ( Viv1 and Viv2 primers ) or P . malariae ( Mal1 and Mal2 primers ) . The fragments obtained for P . vivax were of 120 bp , whereas for P . falciparum and P . malariae were 205 bp and 144 bp , respectively . The oligonucleotide sequences of each primer used are listed in Table 2 . The products were visualized in 2% agarose gel stained with ethidium bromide . One uninfected blood sample was included for every twelve samples processed to control for cross-contamination . Fifteen percent of positive PCR samples were re-tested to confirm the amplification of plasmodial DNA . All tests were performed and confirmed at our main laboratory at the Centro de Pesquisas Gonçalo Moniz , Brazil . Plasma levels of TNF-alpha were measured using the Cytometric Bead Array - CBA® ( BD Biosciences Pharmingen , USA ) according to the manufacturer's protocol , with all samples run in a single assay . The flow cytometric assay was performed and analyzed by a single operator , and standard curves were derived from cytokine standards . The minimum limit of detection was 3 . 7 pg/mL . Plasma measurements of creatinine , alanine amino-transaminase ( ALT ) and haemoglobin were made at the clinical laboratory of Faculdade São Lucas and at the Laboratório LPC ( Salvador , Bahia . Brazil ) . SOD-1 plasma concentrations were measured using the Cu/Zn Superoxide Dismutase ELISA Kit according to the manufacturer's protocol ( Calbiochem , EMD chemicals , Darmstadt , Germany ) . Briefly , human serum was diluted 1∶200 in PBS and distributed in a sensitized 96-wells plate . The samples were incubated for one hour at room temperature with HRP-conjugated anti-Cu/Zn SOD antibody . A colorimetric substrate was added for ten minutes , being the system protected from intense light . The reaction was stop and the plate read at 450nm . The SOD activity assay was performed using the Superoxide Dismutase Colorimetric Assay Kit according to the manufacturer's protocol ( Cayman chemical , Ann Arbor , MI , USA ) . Briefly , radical detector was added to a sensitized 96-wells plate . Pre-diluted ( 1∶50 ) samples were distributed in wells . The reaction was started using Xanthine Oxidase , and the plate was read after twenty minutes at 450nm . One unit of SOD is defined as the amount of enzyme needed to exhibit 50% dismutation of the superoxide radical . The Kruskal-Wallis test with Dunn's multiple comparisons or linear trend analysis was used to compare SOD-1 and TNF-alpha levels according to different clinical presentations of vivax malaria infection . The Mann-Whitney test was used to verify differences between asymptomatic and symptomatic , between mild and severe vivax malaria or between P . vivax and P . falciparum infections . Correlations between SOD-1 or TNF-alpha levels and severity factors were performed using the Spearman test . Receiver-operator characteristic ( ROC ) curves with C-statistics were used to establish the threshold value of SOD-1 and TNF-alpha able to discriminate between mild and severe infection . A p value<0 . 05 was considered statistically significant .
Increased vivax malaria severity was associated with higher plasma levels of SOD-1 ( P<0 . 0001; Figure 1A and 1B ) , with similar trend being noted with regard to SOD activity ( P<0 . 01 for linear trend; data not shown ) . Considering individuals with mild and severe infections together ( n = 69 ) , increased SOD-1 protein levels were correlated with higher parasitaemia ( r = 0 . 77 , p = 0 . 03; Figure 2A ) , while this correlation did not reach significance for TNF-alpha ( r = 0 . 68 , p = 0 . 07; Figure 2B ) . In addition , splenomegaly and hypotension were more prevalent in patients with high SOD-1 and TNF-alpha levels compared to those with low levels of both factors ( 43 . 2% vs . 5 . 1% respectively; Fisher's test p = 0 . 02 ) . Correlation between SOD-1 protein levels and plasma creatinine measurements was r = 0 . 72 ( p = 0 . 03; Figure 2C ) , while the correlation between TNF-alpha and creatinine did not achieved statistical significance in the cohort under investigation in this study ( r = 0 . 68 , p = 0 . 06; Figure 2D ) . Furthermore , a similar pattern was observed regarding the correlation of ALT with SOD-1 protein levels ( r = 0 . 81 , p = 0 . 03; Figure 2E ) or TNF-alpha ( r = 0 . 75 , p = 0 . 03; Figure 2F ) . SOD-1 protein levels were also directly associated with systemic TNF-alpha ( r = 0 . 57 , p<0 . 0001; Figure 2G ) . All individuals with severe disease presented with anemia at the time of hospitalization ( haemoglobin mean: 6 . 2±1 . 4 ) , while in those with mild infection , only 14/50 were anemic ( haemoglobin mean: 12 . 5±2 . 0 ) . In agreement with previous findings on P . falciparum [3] and P . vivax [4] infections , individuals with severe malaria displayed higher plasma levels of TNF-alpha than those with asymptomatic parasitaemia or mild disease ( Figure 1B ) . Within the individuals presenting with severe disease who successfully recovered after in-hospital care ( n = 13 ) , the systemic levels of both SOD-1 and TNF-alpha decreased at least two fold during the seventh day of anti-malarial treatment ( p = 0 . 0005 and p = 0 . 001 , respectively; Figure 3 ) . We also assessed the possibility of estimating threshold levels of TNF-alpha and SOD-1 to discriminate between asymptomatic and symptomatic infection . As expected , individuals with symptomatic infection ( mild or severe ) presented higher levels of both TNF-alpha and SOD-1 than those who were symptomless ( Figure 1A ) . SOD-1 , however , was a better marker than TNF-alpha ( Figure 4A ) . Moreover , TNF-alpha and SOD-1 levels were elevated in individuals with severe disease compared to mild disease ( Figure 1B ) . SOD-1 was also more powerful than TNF-alpha in predicting severe disease ( Figure 4B ) . In an attempt to address if the plasma levels of both SOD-1 and TNF-alpha were useful to discriminate P . vivax from P . falciparum malaria , we compared plasma samples from individuals presenting with mild symptomatic vivax malaria and age matched individuals with symptomatic P . falciparum infection . Neither SOD-1 nor TNF-alpha could differentiate between the infections ( Figure 5 ) .
This study illustrates the possibility of using SOD-1 levels as a severity biomarker in human P . vivax malaria and highlights the likelihood of exploring the future use of the plasma SOD-1 levels as an effective marker of malaria severity . To validate our results , studies investigating samples from different endemic areas are crucial , as local health conditions such as co-infections may limit the effective use of a biomarker . A possible advantage of measuring SOD-1 levels as part of the clinical management in endemic areas cannot be assumed from our results . The use of the SOD-1 as a reliable marker also depends on future field interventions in which the pre-test prediction and cost-effectiveness should be considered . In addition , the specific role of SOD-1 in the immunopathogenesis of severe vivax malaria was not explored in this study and is still being addressed by our group .
|
Despite being considered a relatively benign disease , Plasmodium vivax infection has been associated with fatal outcomes due to treatment failure or inadequate health care . The identification of sensitive and reliable markers of disease severity is important to improve the quality of patient care . Although not imperative , a good marker should have a close causative relationship with the disease pathogenesis . During acute malaria , an intense inflammatory response and a well-documented oxidative burst are noted . Among the free radicals released , superoxide anions account for the great majority . The present study aimed to evaluate the reliability of using an antioxidant enzyme , responsible for the clearance of superoxide anions , as a marker of vivax malaria severity . Thus , we investigated individuals from an Amazonian region highly endemic for vivax malaria with the goal of predicting infection severity by measuring superoxide dismutase-1 ( SOD-1 ) plasma levels . In addition , we compared the predictive power SOD-1 to that of the tumor necrosis factor ( TNF ) -alpha . SOD-1 was a more powerful predictor of disease severity than TNF-alpha in individuals with different clinical presentations of vivax malaria . This finding opens up new approaches in the initial screening of severe vivax malaria cases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"evidence-based",
"healthcare/methods",
"for",
"diagnostic",
"and",
"therapeutic",
"studies",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases"
] |
2010
|
Plasma Superoxide Dismutase-1 as a Surrogate Marker of Vivax Malaria Severity
|
Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza . Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models . We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error . Overall , we find that humidity forcing improves forecast performance ( at 1–4 lead weeks , 3 . 8% more peak week and 4 . 4% more peak intensity forecasts are accurate than with no forcing ) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing ( 4 . 4% and 2 . 6% respectively ) . These findings hold for predictions of outbreak peak intensity , peak timing , and incidence over 2- and 4-week horizons . The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast .
A growing body of evidence indicates that the survival and transmissibility of influenza are affected by ambient humidity conditions . Laboratory experiments have shown that aerosolized influenza survival rates increase in low ambient relative humidity ( RH ) , i . e . less than 40% [1–10] . Additional experiments examining the transmission of human influenza among guinea pigs have also shown that transmission increases at low RH levels [11] . Further evaluation of these experiments has revealed a strong relationship in which low absolute humidity ( AH ) conditions favor the survival and transmission of influenza [12] . Low AH conditions manifest outdoors during winter in temperate regions . Furthermore , because temperature indoors is managed but humidity is generally not , both AH and RH tend to be low indoors during winter . Indeed , even though RH is often maximal outdoors during winter , indoor RH often reaches the very low levels ( 10–40% ) favorable for influenza survival and transmission , and indoor AH closely mirrors outdoor levels [13–16] . People in the developed world , such as the US , spend approximately 90% of their time indoors [17]; as a result , influenza transmission is suspected to occur indoors in the developed world . In addition , because both indoor RH and indoor AH are highly co-variable with outdoor AH , either variable can be estimated using outdoor AH . Consequently , regardless of whether one considers RH or AH to be the true modulator of influenza survival and transmissibility ( and the mechanisms for this modulation remain undetermined ) , outdoor AH can be used to estimate the effect of humidity on influenza transmission . A number of epidemiological studies have found associations between outdoor AH and estimates of influenza incidence or influenza-related mortality . Statistical analysis has shown that the onset of influenza outbreaks is associated with anomalously low AH conditions [18] . Low AH levels in temperate regions have also been associated with increased influenza-associated mortality levels [19] , increased influenza transmission intensity [20] , and increased influenza incidence [21] . Modeling studies have shown that the seasonality of influenza in temperate regions can be reproduced when influenza transmission potential is modulated by observed AH conditions [18] . Furthermore , AH , along with other dynamical processes , can be used to explain the timing of both seasonal and pandemic influenza outbreaks [22–25] . Indeed , even the development of pandemic influenza outbreaks out of season ( i . e . during summer ) can be understood in the context of ambient seasonal humidity conditions , contact patterns , and population susceptibility . Specifically , ambient humidity sets an upper bound on the transmission potential ( a maximal basic reproductive number ) , contact patterns also influence transmission ( e . g . through preferential mixing among certain sub-populations ) , and population susceptibility reduces transmissibility as it drops . In effect , AH constrains the extent to which an influenza virus strain is capable of sustained transmission during summer and sustained transmission is possible only if population susceptibility to that strain remains sufficiently high . For circulating seasonal influenza strains , higher AH and lower susceptibility conspire to limit sustained transmission of circulating influenza strains during summer; however , the introduction of a new pandemic strain , for which population susceptibility is much higher , can enable sustained transmission during summer [22 , 25] . Recently , we developed a number of model-inference systems for the ensemble forecast of seasonal influenza [26–29] . These systems use a compartmental model , such as a susceptible-infected-recovered-susceptible ( SIRS ) model , that is first optimized prior to forecast using observational estimates of US state and municipal influenza incidence . When first developed , we used an AH-forced SIRS model for these predictions , as this model had been used to describe the seasonality of influenza at state geographic scales in the US [18] . However , when applied to influenza forecast over larger areas , e . g . US CDC Health and Human Services multi-state regions , or subtropical regions , we have heretofore adopted a SIRS form without AH forcing [27 , 30] . It remains an open question whether the prediction of influenza in temperate regions is improved by the inclusion of AH forcing . In this paper , we perform retrospective forecasting over 10 seasons for 95 cities using 4 different forms of AH forcing: 1 ) no AH forcing; 2 ) optimization and forecast with local climatological AH forcing ( i . e . historical average AH conditions on a given day ) ; 3 ) optimization and forecast with local observed AH forcing ( i . e . AH as observed on a given day ) ; and 4 ) optimization with observed AH forcing and forecast with climatological AH forcing . This effort applies these 4 AH forcing approaches to an established model-inference prediction system that has been used for 5 years to forecast influenza operationally in real-time [27 , 31] . We explore whether clear differences in forecast accuracy emerge among these 4 approaches and quantify whether inclusion of AH forcing improves forecast accuracy . We hypothesize , given evidence suggesting ambient AH modifies the survival and transmissibility of influenza , that AH forcing will improve dynamic model influenza forecast .
Forecast of influenza is here generated using compartmental models describing the propagation of influenza through a population , observational estimates of influenza incidence , and data assimilation methods for model optimization [26–29] . Four different compartmental models were used to generate the forecasts . All four forms are perfectly-mixed , absolute humidity-driven compartmental constructs with the following designations: 1 ) susceptible-infectious-recovered ( SIR ) ; 2 ) SIRS; 3 ) susceptible-exposed-infectious-recovered ( SEIR ) ; and 4 ) susceptible-exposed-infectious-recovered-susceptible ( SEIRS ) . The differences among the model forms align with whether waning immunity , which allows recovered individuals to return to the susceptible class , or an explicit period of latent infection ( the exposed period ) is represented . As the SEIRS model is the most detailed , we present it here . All other forms are derived by reduction of these equations , which are as follows: dSdt=N−S−E−IL−β ( t ) ISN−α ( 1 ) dEdt=β ( t ) ISN−EZ+α ( 2 ) dIdt=EZ−ID ( 3 ) where S is the number of susceptible people in the population , t is time in years , N is the population size , E is the number of exposed people , I is the number of infectious people , N-S-E-I is the number of recovered individuals , β ( t ) is the contact rate at time t , L is the average duration of immunity , Z is the mean latent period , D is the mean infectious period , and α is the rate of travel-related import of influenza virus into the model domain . The contact rate , β ( t ) , is given by β ( t ) = R0 ( t ) /D , where R0 ( t ) , the basic reproductive number , is the number of secondary infections the average infectious person would produce in a fully susceptible population at time t . Specific humidity , a measure of absolute humidity ( AH ) , modulates transmission rates within this model by altering R0 ( t ) through an exponential relationship derived from laboratory experiments [12]: R0 ( t ) =R0min+ ( R0max−R0min ) e−aq ( t ) ( 4 ) where R0min is the minimum daily basic reproductive number , R0max is the maximum daily basic reproductive number , a = 180 ( unitless ) , and q ( t ) is the time-varying specific humidity ( in kg/kg ) . The value of a is estimated from the laboratory regression of influenza virus survival upon AH [18] . Simulations were performed with fixed travel-related seeding of I of 0 . 1 infections per day ( 1 infection every 10 days ) . For each of the 4 compartmental model forms ( SIR , SIRS , SEIR , SEIRS ) , four different approaches were used to test how the incorporation of AH conditions in the model framework affects the accuracy of influenza forecast . Each approach applied AH conditions in a different fashion . In the first approach , ‘observed AH’ , observed local daily AH conditions are applied using Eq 4 . This use of AH is not realistic for real-time forecast , as future AH conditions are not known; however , as applied here , it can be used to forecast influenza outbreak characteristics retrospectively and to determine if such information would , in theory , improve forecast accuracy . For the second approach , ‘climatological AH’ , we use local daily climatological AH conditions in Eq 4 . Here , the climatology is based on 24 years ( 1979–2002 ) and represents the historical average conditions on a given day for the location at which the model is applied . Due to averaging over many years , climatological AH is much smoother than observed AH conditions and has been used operationally to generate real-time influenza forecasts [27 , 31] . The third approach , ‘combination AH’ , is a hybrid of the first two approaches . Observed AH is used during model optimization prior to forecast , but the forecast of future outcomes is generated using climatological AH . This strategy can be used for real-time forecasting and was implemented previously [26] . The final approach , ‘no AH’ , replaces Eq 4 with R0 ( t ) = R0 . In this fashion R0 is treated as an adjustable parameter to be optimized during the data assimilation process ( see below ) . As with the other parameters of the model , it remains fixed during forecast when the optimized model is integrated into the future to generate an ensemble of predictions . Specific humidity ( q; used in Eq 4 ) data were compiled from the National Land Data Assimilation System ( NLDAS ) project-2 dataset . These data are derived through spatial interpolation , temporal disaggregation and vertical adjustment from station measurements and National Center for Environmental Prediction North American Regional Reanalysis [32] . The gridded NLDAS meteorological data are available in hourly time steps on a 0 . 125° regular grid from 1979 through the present [33] . Specific humidity data from the grid cell containing the centroid of each of the 95 cities included in this study were assembled for 1979–2015 . These hourly data were then averaged to daily resolution . A 1979–2002 ( 24 year ) daily climatology was then constructed for each city . As described in Shaman et al . [27] , weekly estimates of influenza incidence were generated by multiplying 2003–2015 historical Google Flu Trend ( GFT ) estimates of municipal influenza-like illness ( ILI ) [34] , as these data were released in real time ( S1 Data ) , by coincident census division ( regional ) weekly laboratory-confirmed influenza positive proportions as compiled through the National Respiratory and Enteric Virus Surveillance System ( NREVSS ) and U . S . -based World Health Organization ( WHO ) Collaborating Laboratories [35] . This combined metric , termed ILI+ , provides a more specific measure of influenza incidence than ILI alone , which non-specifically captures signal from other circulating respiratory viruses , such as adenovirus and rhinovirus [27 , 36] . While the spatially coarser regional NRVESS/WHO data does not fully discriminate variability in influenza positivity at the municipal scale , multiplication of municipal ILI with these regional proportions does remove some of the signal associated with other respiratory viruses and provides a more precise estimate of influenza activity than ILI alone . Three ensemble filter methods—the ensemble Kalman filter [37] , the ensemble adjustment Kalman filter [38] and the rank histogram filter [39]—and a particle filter ( PF ) with resampling and regularization [40] were used in conjunction with ILI+ to optimize and initialize the compartmental models prior to forecast . Both the model state variables ( S , E , I ) and parameters ( L , Z , D , R0max , R0min and R0 ) were subject to optimization . Ensemble filter simulations were run with 300 ensemble members and PF simulations were run with 10 , 000 particles . Each ensemble filter algorithm is applied sequentially through time to update ensemble model simulations of observed state variables ( i . e . influenza incidence ) to better align with observations ( i . e . ILI+ ) . These updates are calculated , per the specifics of each filter algorithm ( described below ) , by halting the ensemble integration when a new observation comes available . The posterior is then integrated through time using the model equations to the next observation and the process is repeated . Through this iterative updating process the ensemble of simulations provides an increasingly accurate estimate of the observed state variable ( i . e . influenza incidence ) , and estimates of the unobserved variables and parameters ( e . g . susceptibility and mean infectious period ) are obtained through additional adjustments that take advantage of their co-variability with the observed state variable . In general , Kalman filters assume normality of the observational error , the prior distribution , and the posterior distribution . Differences among the ensemble filter algorithms manifest from the means by which the update is specified . The ensemble Kalman filter ( EnKF ) is a stochastic , perturbed observation Kalman filter in which the update of each ensemble member is computed using the current observation plus Gaussian random noise [41] . That is , the posterior for each ensemble member is simply the weighted sum of the prior for that ensemble member and the observation plus random noise with variance equal to the observational error variance . The weights themselves are calculated as ratios of the ensemble prior variance and the observational error variance . The ensemble adjustment Kalman filter ( EAKF ) employs a deterministic algorithm to compute the ensemble posterior mean and variance [38] . At each update , the EAKF algorithm aligns the first two ensemble posterior moments with those predicted by Bayes’ theorem . Unlike the EnKF and EAKF , the rank histogram filter ( RHF ) does not impose a Gaussian structure on the prior , observations or posterior [39]; rather , this filter employs an algorithm that creates an approximate probability distribution by ordering ( i . e . ranking ) the ensemble prior . In this fashion , the RHF admits non-Gaussian distributions , thus relaxing the normality assumption inherent to most Kalman filters . For the ensemble filters , multiplicative inflation [38] was applied following the assimilation of each weekly observation of ILI+ . The inflation was used to counter the ensemble filter tendency toward ‘filter divergence' , which occurs when the prior ensemble spread becomes spuriously low . In the absence of inflation , the system may give too little weight to the observations and thus diverge from the true trajectory . Unlike the above ensemble filters , PFs are an alternate class of assimilation method that do not require assumptions about linearity or normality . The PF approach used here adopts sequential importance sampling with resampling and regularization [40 , 42] . Resampling generates a new suite of particles with equal weight during the model integration whenever the effective sample size is low . Regularization jiggles the state and parameter values of each resampled particle to eliminate redundancies and further sample parameter space around each previously highly weighted particle . As a consequence of resampling and regularization , a much richer range of parameter and state space is spanned than with a basic PF , which relies only on the initial parameter choices . For all 16 model-filter combinations ( i . e . 4 models × 4 filters ) a scaling factor was employed to convert ILI+ to influenza incidence , the quantity represented in the compartmental models , per Shaman et al . [27] . Both ILI and ILI+ are biased in that they only capture persons seeking medical attention and are measured per 100 , 000 patient visits . The scaling factor partially compensates for this bias by accounting for the probability that a person with influenza seeks medical attention and the probability that a person seeks medical attention for any reason [27] . Even after this scaling , bias in the data likely remains; however , as long as this bias remains stationary , it should not corrupt forecasting . That is , if a given model is well optimized using biased observations , it should make biased predictions , as the accuracy of those out-of-sample predictions is being assessed using the same biased dataset . Additional details on the application of the ensemble filters and PF to infectious disease models are provided in Shaman and Karspeck [26] and Yang et al . [28] . S1–S3 Figs present example forecasts from the more than 1 . 2 million predictions generated . For each model-filter combination , we generated weekly retrospective forecasts of influenza outbreak characteristics during weeks 6–25 of the influenza season ( beginning early October ) over 10 seasons ( 2003–2004 through 2014–2015 , excluding the pandemic seasons 2008–2009 and 2009–2010 ) . For the ensemble filters , the ensemble mean trajectory was used for forecast accuracy assessment; for the PF , the particle weighted average trajectory was used . A simple average of these trajectory forecasts as generated by all 16 model-filter combinations was used for accuracy analysis unless otherwise specified . We limit our analysis to forecasts made 0–8 weeks before the predicted peak week . Let O ( t ) be the ILI+ observed at time t and Fw ( t ) the ILI+ forecast made for time t using ILI+ available through week w , i . e . w < t . The predicted peak intensity at w is defined as the maximum of the average forecast trajectory , and the peak week is the week when that maximum occurs . A predicted peak week is defined as accurate if it is within ±1 week of the observed peak week , and the predicted peak intensity is deemed accurate if it is within ± 25% of the observed peak intensity . In addition , absolute error was calculated for each prediction of peak week and peak intensity and used to rank the weekly performances of the 4 AH forecast approaches . Root mean squared error ( RMSE ) was calculated over the entire trajectory at time horizons of 2 and 4 weeks: RMSEwh=∑t=w+1w+h ( Fw ( t ) −O ( t ) ) 2h ( 5 ) where h ∈ {2 , 4} weeks . We also performed a Friedman test followed by a Nemenyi test to assess whether forecast error differed significantly among the 4 AH forcing approaches . The Friedman test is a non-parametric test that ranks the error of each group—here , each AH forcing approach—for each forecast location-week . The Nemenyi test assesses for statistically significant differences between each pair of ranked groupings .
For predictions of peak intensity , forecasts using one of the 3 AH forcing approaches were superior at all lead times ( Fig 1 ) . Peak week forecasts showed similar results , with the exception of the forecasts with 6- and 7-week leads . Note that the number of no AH forecasts generated with 6- and 7-week leads was small , so this result may be due to sampling error . The climatological AH forecasts were most accurate for leads of 0 through 3 weeks . These findings were insensitive to the choice of error margin used to define accuracy ( S4 and S5 Figs ) . Forecast error rank for peak intensity reveals the climatological AH forecasts most often had the lowest rank ( smallest error ) at most leads and the no AH forecasts most often had the largest error at all leads ( Fig 2 ) . For predictions of peak week , the no AH forecasts again most often had the largest error at all leads; however , differences among the 3 humidity-forced forecasts were less clearly discernible . These findings were confirmed by Friedman rank , which showed that overall the no AH forecasts ranked the worst among the four AH approaches ( Table 1 ) . Results from the pairwise comparison revealed highly significant differences among most pairings ( p < 0 . 001 ) with the exception of the climatological AH-combination AH pair ( Table 2 ) . Similar findings hold for RMSE over 2- and 4-week prediction horizons ( Fig 3 ) . Here , the no AH forecasts rank either best or worst depending on lead time . Specifically , the no AH forecasts rank worst at longer leads but often rank best at shorter leads ( 2 to 4 weeks ) . In contrast , the combination and observed AH forecasts most often cluster at ranks 2 and 3 . The climatological AH forecasts appear to have the greatest diversity of ranking; however , overall it has the lowest mean ranking ( Table 1 ) and provides a significant benefit over the 3 other approaches ( Table 2 ) . When we stratify the forecasts by model form , we find similar results for peak intensity ( Fig 4 ) . Specifically , the no AH forecasts are less accurate than the 3 AH-forced forecasts for each of the 4 compartmental model forms . Similarly , the forecasts of peak week stratified by model form are similar to the overall findings ( Fig 1 ) . For peak intensity , stratification by filter reveals a less clear distinction between the no AH forecasts and the 3 AH-forced forecasts ( Fig 5 ) . The EAKF and EnKF no AH peak intensity forecasts do comparatively well for 0- to 2-week leads , whereas the RHF no AH forecasts do well for 4- to 8-week leads . The PF no AH forecasts of peak intensity generally perform less well at all leads but 0 weeks . For peak week , the results by filter are less distinct . Here the EAKF no AH forecasts perform better at longer lead times ( 5–8 weeks ) , and the EnKF at 0 to 1 week leads .
Overall , our findings indicate that peak intensity forecast accuracy improves with some type of AH forcing regardless of lead time . With the exception of long-lead time forecasts , where the low number of samples for the no AH approach make it difficult to interpret the results , forecast of peak week timing is also generally improved with AH forcing . Analyses of forecast error reveal that predictions generated with no AH forcing are most likely to perform worse than counterpart AH-forced prediction . While this tendency is consistent for forecast peak intensity , for peak timing , RMSE2 and RMSE4 , the no AH approach at times produced superior forecasts ( Figs 2 and 3 ) , although the overall performance of the no AH form was worse ( Table 1 ) . Among the 3 AH forcing approaches the differences were less clear . The climatological AH approach most often performed best ( Table 1 ) ; however , for forecast of peak intensity and peak timing , the accuracy of the climatological and combination AH approaches was not statistically different ( Table 2 ) . It is interesting that forecasts with climatological AH forcing were more accurate than those generated using observed AH conditions . This finding indicates that the short-term fluctuations of observed AH due to synoptic variability—the 1–4 day variability associated with storms and frontal systems—may actually degrade prediction . Whether this effect is due to the transience of the synoptic AH signal , which corrupts filter optimization , ILI+ observational noise , or the simplicity of the models , which may not appropriately represent the effects of these fluctuations on virus transmissibility , is not clear . To test whether model simplicity underlies our findings , we performed a synthetic test in which we used local daily , observed AH data for 2003–2015 and the SIRS model to generate time series of influenza incidence for 61 cities ( see S1 Text ) . Daily cases were aggregated by calendar week and observations were drawn from a negative binomial distribution . These synthetic observations were then used , in turn , in conjunction with the EAKF , EnKF and RHF filters to optimize the SIRS model , with each of the 4 AH approaches , and generate forecasts of the synthetic truth for each of the 61 cities and 10 seasons . Forecast results for this synthetic target were similar to the findings for the ILI+ target ( S1 and S2 Tables ) . In particular , forecasts made using the climatological AH approach most consistently ranked first or tied for first even though observed AH had been used to generate the synthetic observations . This finding indicates that model simplicity is not the primary reason for our findings . For shorter lead predictions ( 1 to 4 weeks ) the improvement of the climatological AH approach over other approaches appears to be greater for the 2 models that include a latent period ( i . e . the SEIR and SEIRS models , see Fig 4 ) . Laboratory evidence and physical considerations point to an immediate effect of ambient humidity on virus survival and transmissibility [4] . However , given that there exists an intrinsic delay between infection and seeking medical attention , a lag between humidity anomalies and observed changes in rates of ILI and influenza positivity rates should be evident , and has similarly been observed for excess pneumonia and influenza mortality [18] . For ILI+ , observations are summed over a calendar week , so some individuals are no doubt infected and seek clinical care in the same week; however , for other individuals , the effects of humidity at the end of one week , would be expected to modulate ILI+ levels during the following week [43] . It thus may be that the models with a latent period ( i . e . SEIR and SEIRS ) are able to capture some of this lag—but perhaps for the wrong reason in that the period of latency accounts for some of the time between infection and seeking medical attention . While there is no perfect estimate of influenza infection rates , the use of 2003–2015 historical GFT municipal ILI estimates in this study may have introduced some biases . Firstly , Google repeatedly altered their algorithm for estimating ILI [44–45]; we here used GFT ILI estimates as they were initially made available in real time , rather than subsequently revised estimates , in order to produce retrospective forecasts as they would have been generated in real time . Secondly , GFT ILI national and regional estimates have documented inaccuracies relative to target CDC ILI measurements [46–47] . Thirdly , GFT ILI estimates were developed and validated using national and regional CDC ILI targets . Detailing of the precise algorithm used by Google or the process of extrapolation to sub-regional spatial scales has not been published . Our use of municipal ILI estimates , which are spatially correlated and derived from the same GFT algorithm , suggests that the 95 cities used in this study may not provide 95 independent tests of the importance of humidity . Consequently , it is possible that the significance of the rank differences reported in Table 2 are inflated . In general , our findings support the continued use of climatological AH forcing when generating influenza forecasts . This approach has been our primary means for generating operational real-time forecasts . Many other groups are also presently developing and generating influenza forecasts using a combination of dynamical and/or statistical methodologies [48–52] , both of which have their strengths . Given the findings here , which indicate that inclusion of humidity forcing improves forecast accuracy , this forcing should be included and tested in conjunction with other forecasting systems . Further , as new , improved model forms and filtering methods are brought online , the effects of humidity forcing on forecast accuracy will need to be continually monitored .
|
Laboratory and epidemiological evidence indicate that atmospheric absolute humidity conditions modulate the survival , transmission , incidence and seasonality of influenza . Absolute humidity ( AH ) conditions are often incorporated as a forcing factor in mathematical models used to describe and forecast influenza incidence . Here we examine whether the inclusion of absolute humidity forcing improves influenza forecast accuracy . We perform retrospective influenza forecasting over 10 seasons for 95 cities using 4 different forms of AH forcing: 1 ) no AH forcing; 2 ) optimization and forecast with local climatological AH forcing; 3 ) optimization and forecast with local observed AH forcing; and 4 ) optimization with observed AH forcing and forecast with climatological AH forcing . We find that humidity forcing improves forecast performance and that forecasts generated using climatological humidity forcing generally outperform forecasts that utilize observed humidity forcing .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"influenza",
"atmospheric",
"science",
"pathogens",
"applied",
"mathematics",
"microbiology",
"orthomyxoviruses",
"simulation",
"and",
"modeling",
"viruses",
"algorithms",
"optimization",
"seasons",
"mathematics",
"rna",
"viruses",
"forecasting",
"statistics",
"(mathematics)",
"humidity",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"medical",
"microbiology",
"kalman",
"filter",
"mathematical",
"and",
"statistical",
"techniques",
"microbial",
"pathogens",
"climatology",
"meteorology",
"influenza",
"viruses",
"earth",
"sciences",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"physical",
"sciences",
"statistical",
"methods",
"organisms"
] |
2017
|
The use of ambient humidity conditions to improve influenza forecast
|
Sudden cardiac death ( SCD ) continues to be one of the leading causes of mortality worldwide , with an annual incidence estimated at 250 , 000–300 , 000 in the United States and with the vast majority occurring in the setting of coronary disease . We performed a genome-wide association meta-analysis in 1 , 283 SCD cases and >20 , 000 control individuals of European ancestry from 5 studies , with follow-up genotyping in up to 3 , 119 SCD cases and 11 , 146 controls from 11 European ancestry studies , and identify the BAZ2B locus as associated with SCD ( P = 1 . 8×10−10 ) . The risk allele , while ancestral , has a frequency of ∼1 . 4% , suggesting strong negative selection and increases risk for SCD by 1 . 92–fold per allele ( 95% CI 1 . 57–2 . 34 ) . We also tested the role of 49 SNPs previously implicated in modulating electrocardiographic traits ( QRS , QT , and RR intervals ) . Consistent with epidemiological studies showing increased risk of SCD with prolonged QRS/QT intervals , the interval-prolonging alleles are in aggregate associated with increased risk for SCD ( P = 0 . 006 ) .
Despite recent progress in treatment and prevention of coronary heart disease , sudden cardiac death ( SCD ) remains a major public health problem , with an annual incidence of SCD that ranges from 50 to 100 per 100 , 000 in the general population [1] , [2] . While there has been a great deal of focus on SCD in the setting of Mendelian forms of arrhythmia ( e . g . long and short QT syndromes ) , the vast majority of SCD events occur in the general population , with up to 50% of individuals manifesting SCD as a first sign of disease [3] . An estimated ∼80% of all SCDs are associated with coronary disease , ∼10–15% in the setting of cardiomyopathy and ∼5% occur in persons with myocarditis , coronary anomalies or ion channelopathies ( e . g . long QT/Brugada/short QT syndromes ) [4] . Despite this clinical heterogeneity , a familial component to SCD risk has been demonstrated even after adjusting for traditional cardiovascular disease risk factors [5] , [6] , [7] , [8] , suggesting that genetic factors are likely to play an important role . The critical importance of the genetic contribution for effective prediction and prevention of SCD has been emphasized in a recent consensus document from the US National Heart Lung and Blood Institute [9] . In this study , we perform a meta-analysis of 5 genome-wide association studies ( GWAS ) with follow-up genotyping in up to 11 additional studies of European ancestry to identify genetic variants that modify susceptibility to community-based SCD . In addition to an unbiased scan of the genome , we also focus on specific SNPs that have been previously associated with electrophysiological traits that , when extreme , are associated with increased risk for SCD ( QRS , QT , and RR intervals ) .
To identify genetic determinants of SCD , genome-wide genotyping and imputation of ∼2 . 5 million SNPs was performed in 1 , 283 SCD cases and ∼20 , 000 controls drawn from 5 samples of European ancestry: Atherosclerosis Risk in Communities ( ARIC ) , Framingham Heart Study ( FHS ) , FinGesture , Oregon Sudden Unexpected Death Study ( Oregon-SUDS ) , Rotterdam Study ( RS ) ( Table S1 ) . All individual studies showed minimal test statistic inflation after post-imputation quality control ( genomic control λ<1 . 03 ) ( see Materials and Methods ) . Meta-analysis was performed using inverse variance weighting , with minimal test statistic inflation observed ( λ = 1 . 004 ) and no early departure from the null expectation ( Figure S1A ) , suggesting that overall there was good genotyping quality and minimal population substructure . Two loci exceeded the genome-wide significance threshold of P<5×10−8 ( Figure S1B , Table 1 ) . There were 13 independent loci containing at least 1 SNP with P<10−5 ( Table S2 ) . We attempted to follow-up all of these loci in additional independent case-control samples from FinGesture and Oregon-SUDS , as well as in seven additional cohorts: Cardiovascular Health Study ( CHS ) , CVPath Institute Sudden Cardiac Death registry ( CVPI-SCDr ) , the Harvard Cohorts ( 5 combined cohorts , see Materials and Methods ) . In total , this follow-up genotyping included 1 , 730 cases and 10 , 530 controls of European ancestry ( Table S3 ) . We were able to design assays and obtain high quality genotype data for 11 SNPs corresponding to 10 of these independent loci ( Table 1 ) . Nominally significant replication results consistent with the GWAS were observed for two highly correlated SNPs ( r2 = 1 in HapMap CEU samples ) in the BAZ2B ( bromodomain adjacent zinc finger domain 2B ) locus ( rs174230 , P = 0 . 03; rs4665058 , P = 0 . 01 ) . On the other hand there was no significant evidence of replication for the two loci that were of genome-wide significance in the initial scan ( rs2650907 and rs12601622 located on chromosomes 16 and 17 , respectively ) , suggesting that they represent either false positive signals in the initial scan or insufficient power of the replication cohort . In this context , it is important to note that the imputation quality of rs12601622 was poor across all studies ( imputation quality ( r2 ) <0 . 4 ) . When combining follow-up results with the discovery GWAS results , only rs4665058 approached genome-wide significance ( P = 5 . 8×10−8 ) . This marker was therefore further genotyped in the ARREST study , which includes 719 SCD cases , and in 4 , 190 population-based controls , and in the recently published AGNES case-control study [10] , which is a study of 670 individuals with first MI and ventricular fibrillation ( VF ) compared to 654 with MI alone . In the combined follow-up cohorts , including the ARREST and AGNES studies , this SNP was significant after Bonferroni correction for the number of loci tested ( Pnominal = 8 . 0×10−5; Pcorrected = 8 . 0×10−4 ) . In a combined analysis of GWAS and all follow-up genotyping results , rs4665058 well exceeded genome-wide significance ( P = 1 . 8×10−10 ) ( Figure 1 ) . The risk allele ( A allele ) of rs4665058 has a study size weighted frequency of 1 . 4% , and increases risk for SCD by 1 . 92-fold per allele ( 95% CI 1 . 57 to 2 . 34 ) in the combined analysis ( Figure S2 ) , and by 1 . 65-fold per allele ( 95% CI 1 . 29 to 2 . 12 ) in the follow-up samples alone ( Table 1 ) . No significant heterogeneity was observed ( Q statistic = 15 . 6 , P = 0 . 11; I2 = 35 . 9 , 95% CI 0 to 68 . 5% ) . No significant interaction was observed for either sex or age ( data not shown ) . It is interesting to note that the risk allele is the ancestral allele ( based on non-human primate sequence ) , and its low frequency in European ancestry populations suggests strong negative selection , as fewer than 0 . 8% of ancestral alleles have reached a frequency of 1 . 4% or lower ( Figure S3 ) . In addition to performing an unbiased GWAS in the 5 discovery cohorts , we also used the results from the initial GWAS meta-analysis to examine the role of SNPs previously reported to be associated with QRS ( ventricular depolarization ) , QT ( ventricular depolarization/repolarization ) , and/or RR ( inverse heart rate ) intervals [11] , [12] , [13] , [14] . These electrocardiographic measured traits are associated with cardiovascular mortality and SCD in both Mendelian settings ( e . g . LQTS ) , and in the general population [15] , [16] , [17] . Overall , there were 49 independent loci associated with the ECG traits reported ( 25 for QRS [14] , 16 QT [12] , [13] , 9 for RR [11] , with one locus overlapping between QRS and QT intervals ) ( Tables S4 , S5 , S6 ) . While in general , we hypothesize that QRS/QT/heart rate prolonging alleles increase risk for SCD , recent pleiotropy analyses of ECG traits have shown inconsistent directions of effects for some alleles ( e . g . a QT-prolonging allele is associated with decreased QRS interval ) [14] , and thus , a priori we have chosen a two-sided test to assess significance of association for ECG SNPs with SCD . Nominal significance was observed for 3 loci , including PLN ( QT/QRS , P = 0 . 013 ) , NOS1AP ( QT , P = 0 . 010 ) , and KCNQ1 ( QT , P = 0 . 014 ) . A fourth locus , TKT/CACNA1D/PRKCD ( QRS , P = 0 . 0007 ) , was significant after multiple-test correction for all 49 tested SNPs ( corrected P = 0 . 034 ) ( Table 2 ) . Interestingly , the direction of effect for TKT/CACNA1D/PRKCD and KCNQ1 is opposite of that expected: the QRS/QT interval prolonging allele is associated with decreased risk for SCD . For the remaining 2 loci , the direction of effect is consistent with a model in which increasing QRS/QT interval increases risk for SCD . Indeed , looking across all 49 loci ( Tables S4 , S5 , S6 ) , we find that in aggregate QRS/QT/heart rate ( inverse RR ) -prolonging alleles are more often associated with increased risk for SCD ( 31/49; P = 0 . 03 ) . This result is entirely driven by QRS ( 17/25 , P = 0 . 04 ) ( Table S4 ) and QT intervals ( 12/16 , P = 0 . 02 ) ( Table S5 ) , with no overrepresentation observed for RR interval ( 3/9 , P = NS ) ( Table S6 ) . The results for QRS and QT intervals ( combined P = 0 . 006 ) are consistent with epidemiological studies , which show increased QRS/QT interval in the general population is associated with increased risk of SCD . While the general trend is significant , the observation that 2/4 of the individual SNPs nominally associated with SCD actually show the opposite direction of effect , including TKT/CACNA1D/PRKCD , suggests the need to further elucidate how each of these variants modifies both the underlying QRS/QT interval trait as well as the potential role in SCD . For example , it is possible that the risk for SCD is not directly ( or entirely ) mediated though the effect on QRS/QT interval , as has been suggested for NOS1AP [18] .
Using meta-analysis of GWAS with follow-up genotyping in independent samples , we demonstrate strong evidence for SCD susceptibility at locus 2q24 . 2 in individuals of European ancestry . Somewhat surprisingly , we identified a relatively rare allele ( MAF = 0 . 014 ) with a strong effect ( OR = 1 . 92 ) , which is in contrast to most GWAS for complex traits , which have typically identified alleles with MAF >0 . 1 and ORs<1 . 5 . Given concerns about imputation accuracy for rarer alleles , we note that rs4665058 is almost perfectly in LD with rs174230 ( r2 = 1 . 0 in CEU ) , which is directly genotyped in all the GWAS samples , and shows almost identical association results ( Table 1 ) . Further , we observe the same effect size for FinGesture GWAS and follow-up genotyping samples ( Figure S2 ) , again suggesting that imputation does not affect the results . Indeed , it is quite plausible that rare variants play a large role in risk for SCD given the fatal nature of SCD ( ∼10% of SCD victims survive ) potentially selecting against more common risk alleles . Despite the strong evidence for association with SCD across the entire study , we note that no effect for rs4665058 is observed in the AGNES and CHS studies . Power was 63% and 52% , respectively , under an additive genetic model with OR = 1 . 92 , requiring a nominal P = 0 . 05 . We observe no significant heterogeneity in the meta-analysis including all studies ( P = 0 . 11 ) , suggesting that the lack of association in these two cohorts is likely a function of random chance , magnified by the low frequency of the risk allele . We would also point out that AGNES has a more narrow phenotype than the other studies , and included only those who suffered their first MI and survived the initial event to receive in-hospital care , which could contribute to the lack of association . For CHS , SCD adjudication was performed along with the ARIC samples , and thus represents an identical phenotype . The only difference was the age of the individuals in these two cohorts ( ARIC 45–64 years , CHS >65 ) , however in interaction analyses we did not observe age to be a significant modifier of the genetic effect . As with other GWAS , the current study design is not without limitations . First , association approaches depend upon linkage disequilibrium to identify associated SNPs , thus the underlying functional variant at any of the loci is potentially unrecognized . Indeed , using Pilot 1 data from the 1000 Genomes project ( November 2010 release ) [19] , we do not identify any missense mutations highly correlated with the most strongly associated SNP , rs4665058 ( r2>0 . 8 ) , suggesting that the functional variant is likely to be regulatory . To test whether rs4665058 was an eQTL , we searched the GTEx eQTL database ( http://www . ncbi . nlm . nih . gov/gtex/test/GTEX2/gtex . cgi ) , which queries lymphoblastoid , liver and various brain regions . We did not observe any eQTLs for rs4665058 or any SNPs from the 1000 Genomes data highly correlated with rs4665058 ( r2>0 . 8 ) . We recognize that this negative finding is not necessarily informative , given that expression from heart tissue has not been queried . Second , we only implicate loci , as opposed to genes , and additional work is required to definitively identify which gene at a locus is responsible . Indeed , while the strongest association signal maps to an intron in BAZ2B , due to linkage disequilibrium , the signal also extends to the WDSUB1 and TANC1 genes . All three genes are expressed in human heart ( http://www . genecards . org ) and the mouse heart during various key stages during cardiogenesis and the formation of the autonomic nervous system from neural crest ( Embryonic day , E7 . 5–10 . 5 ) ( http://biogps . gnf . org ) . BAZ2B and WDSUB1 are essentially uncharacterized , however , TANC1 has been shown to regulate dendritic spines and excitatory synapses in both cultured neurons and a mouse knock-out [20] . TANC1 is most highly expressed in heart in humans , however , no cardiac phenotype in the TANC1 knock-out mouse was noted . Third , while we present evidence that in aggregate QRS/QT interval-prolonging alleles are associated with SCD , only TKT/CACNA1D/PRKCD exceeds a Bonferroni corrected P-value threshold , suggesting inadequate power for these analyses . The findings related to individual QRS/QT interval-prolonging alleles should therefore be considered exploratory and require replication in additional populations . Finally , we note that the meta-analysis consisted of both population-based and case-control studies , with some of the case-control studies using CAD controls as opposed to population-based controls ( Tables S1 and S3 ) . The inclusion of different control groups allows us to separate out whether the risk conferred by the BAZ2B locus acts through increased risk for CAD , which is present in ∼80% of SCD victims . The consistent results in studies with CAD controls ( FinGesture GWAS , Oregon-SUDS GWAS , Oregon-SUDS replication ) ( Figure S2 ) provide evidence that the risk associated with rs4665058 may be specific to SCD , rather than a generic risk factor for CAD . Indeed , given the case mix across the cohorts ( primary ventricular fibrillation , ischemic CAD , non-ischemic CAD , non-CAD ) , the study is best powered to identify variants that increase risk of SCD through a mechanism common across the various subtypes of SCD . In summary , we have identified the locus including the bromodomain-containing gene [21] , BAZ2B , as a new SCD susceptibility locus . The bromodomain is an exclusive protein domain known to recognize acetyl-lysine residues on proteins and might play an important role in chromatin remodeling and gene transcription regulation [22] . The risk allele , while low frequency in Caucasian populations ( MAF = 0 . 014 ) has a relatively large effect , increasing risk for SCD by >1 . 9-fold per allele ( 95% CI 1 . 57 to 2 . 34 ) . While rs4665058 may not be clinically relevant in the general population in whom the increment in absolute risk attributable to the variant is modest , exploring its role in high-risk populations ( e . g . heart failure , SQTS/LQTS ) may help to identify those who could benefit from intervention . Beyond the BAZ2B locus , our study also highlights the role of QRS/QT interval associated variants in the risk of SCD , and suggests that larger GWAS of these and other intermediate risk factors may yield additional SCD loci .
Five studies consisting of individuals of European ancestry from Europe and the United States contributed to the GWAS discovery phase of this study: Atherosclerosis Risk in Communities ( ARIC ) , Framingham Heart Study ( FHS ) , FinGesture , Oregon-Sudden Unexpected Death Study ( Oregon-SUDS ) , and Rotterdam Study ( RS ) . For follow-up , additional genotyping was performed in independent samples from FinGesture and Oregon-SUDS , as well as in 8 additional populations of European ancestry: AmsteRdam REsuscitation STudies ( ARREST ) , Cardiovascular Health Study ( CHS ) , CVPath Institute Sudden Cardiac Death registry ( CVPI-SCDr ) , and Harvard Cohorts ( consisting of 5 combined populations , see below ) . We also performed a look-up of our top result in the AGNES study . All studies received approval from the appropriate institutional review committees , and the subjects in each cohort provided written informed consent . For all studies , covariates , measured at baseline for prospective cohorts , included age and gender , with the following exceptions: FinGesture GWAS included the top 10 principal components calculated from pre-imputation genotype data through a multi-dimensional scaling ( MDS ) method , as implemented in PLINK [34]; CVPI-SCDr did not include covariates , as the controls were population-based controls from CHS . Genotyping was performed using either Affymetrix or Illumina arrays , depending on the cohort ( Table S7 ) . Each study performed filtering of both individuals and SNPs to ensure robustness for genetic analysis . SNP genotypes were assessed for quality , and SNPs failing quality control were removed before imputation according to specific criteria ( Table S7 ) . Each study utilized the remaining SNP genotypes to impute genotypes for approximately 2 . 5 million autosomal SNPs based on linkage disequilibrium patterns observed in the HapMap CEU samples ( Utah residents of Northern and Western European descent ) . Imputed genotypes were calculated as dosages , with fractional values between 0 and 2 reflecting the estimated number of copies of a given allele for a given SNP for each individual . The use of dosages allows for the incorporation of the uncertainty in the imputations into subsequent analysis . All studies used a hidden Markov model as implemented in the MACH software [35] . All results are reported on the forward strand . Post-imputation , quantile-quantile ( QQ ) plots were generated for each study , stratified by both allele frequency and imputation quality to identify classes of SNPs that show strong early departure from the null . Based on these analyses , for the ARIC and Rotterdam studies , SNPs with minor allele frequency ( MAF ) <0 . 01 were excluded . For FHS , SNPs with MAF <0 . 02 and/or imputation quality score <0 . 40 were excluded . Genotyping was performed using iPlex single base primer extension with MALDI-TOF mass spectrometry according to manufacturer protocols ( Sequenom Inc . , San Diego , CA ) . SNPs were excluded from each study if call rate was ≤90% or Hardy-Weinberg equilibrium P<0 . 001 . SNPs were excluded from final meta-analysis if fewer than 4 out of 5 studies reported back results for that SNP . PCR and extension primer sequences are available upon request . In ARREST , rs4665058 was genotyped using TaqMan ( Applied Biosystems ) , and primer and probe sequences are available upon request . In AGNES , genotypes at rs4665058 were determined by direct genotyping ( N = 360 ) using Taqman assay or imputation from HapMap reference panel ( N = 969 ) using the Markov-chain Monte Carlo method implemented in MACH1 . 0 ( Rsq = 0 . 97 ) . Details on genotype imputation have been described elsewhere [10] . For prospective community-based samples , associations between SCD and SNPs were tested using Cox proportional hazards regression models under the assumption of an additive model of genotypic effect . For case-control samples , a logistic regression framework was employed . For the Harvard cohorts , risk set analysis was used to match cases and controls holding time at risk stable; conditional logistic regression in risk set sampling avoids differences in time at risk that otherwise result . These models were adjusted for age and sex . In family-based cohorts ( FHS ) , linear mixed modeling was implemented to additionally control for relatedness [36] . A genomic control correction factor ( λ ) , calculated from all imputed SNPs , was applied on a per-study basis to account for cryptic population sub-structure and other potential biases [37] . Regression results were meta-analyzed using inverse variance weighted fixed-effects models as implemented in the METAL software package ( http://www . sph . umich . edu/csg/abecasis/metal/ ) . Results were considered statistically significant at a P-value of 5×10−8 , a figure that reflects the estimated testing burden of one million independent SNPs in samples of European ancestry [38] . For age by SNP and sex by SNP interactions , we performed regression analysis as described above separately within each study , including both main effects and an interaction term in the model . Meta-analysis of discovery and replication results was performed using inverse-variance weighting as implemented in the R package ‘meta’ ( R version 2 . 81 , http://www . r-project . org/ ) . To account for the age range differences across the cohorts , we used a random-effects model for the age by SNP interaction .
|
Family studies have clearly demonstrated a role for genes in modifying risk for sudden cardiac death ( SCD ) , however genetic studies have been limited by available samples . Here we have assembled over 4 , 400 SCD cases with >30 , 000 controls , all of European ancestry , and utilize a two-stage study design . In the first stage , we conducted an unbiased genome-wide scan in 1 , 283 SCD cases and >20 , 000 controls , and then performed follow-up genotyping in the remainder of the samples . We demonstrate strong association to a region of the genome not previously implicated in SCD , the BAZ2B locus , which contains 3 genes not previously known to play a role in cardiac biology . In addition , we used the genome-wide scan data to test a focused hypothesis that genetic variants that modulate ECG traits associated with SCD ( QT , QRS , and RR intervals ) also modify risk for SCD , and we demonstrate that QT- and QRS-prolonging alleles are , as a group , associated with increased risk of SCD . Taken together , these findings begin to elucidate the genetic contribution to SCD susceptibility and provide important targets for functional studies to investigate the etiology and pathogenesis of SCD .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"acute",
"cardiovascular",
"problems",
"genome-wide",
"association",
"studies",
"medicine",
"coronary",
"artery",
"disease",
"arrhythmias",
"genetics",
"biology",
"human",
"genetics",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics",
"cardiovascular"
] |
2011
|
Identification of a Sudden Cardiac Death Susceptibility Locus at 2q24.2 through Genome-Wide Association in European Ancestry Individuals
|
Box C/D snoRNAs are known to guide site-specific ribose methylation of ribosomal RNA . Here , we demonstrate a novel and unexpected role for box C/D snoRNAs in guiding 18S rRNA acetylation in yeast . Our results demonstrate , for the first time , that the acetylation of two cytosine residues in 18S rRNA catalyzed by Kre33 is guided by two orphan box C/D snoRNAs–snR4 and snR45 –not known to be involved in methylation in yeast . We identified Kre33 binding sites on these snoRNAs as well as on the 18S rRNA , and demonstrate that both snR4 and snR45 establish extended bipartite complementarity around the cytosines targeted for acetylation , similar to pseudouridylation pocket formation by the H/ACA snoRNPs . We show that base pairing between these snoRNAs and 18S rRNA requires the putative helicase activity of Kre33 , which is also needed to aid early pre-rRNA processing . Compared to yeast , the number of orphan box C/D snoRNAs in higher eukaryotes is much larger and we hypothesize that several of these may be involved in base-modifications .
Non-coding RNA ( ncRNA ) represents the most abundant form of gene expression in eukaryotic cells [1] . Small nucleolar ( sno ) RNAs are a group of well-characterized ncRNA molecules of variable length of 60 to 1000 nts . Based on evolutionarily conserved sequence elements , these snoRNAs can be divided into three major classes: the box C/D , box H/ACA and the MRP ( Mitochondrial RNA Processing ) snoRNAs . These snoRNAs form a scaffold for the assembly of a distinct core of highly conserved proteins to form well-defined C/D and H/ACA snoRiboNucleoProteins ( snoRNPs ) , and the RNAse MRP . Box C/D snoRNPs catalyze site-directed 2′-O-ribose methylation , whereas H/ACA snoRNPs catalyze site-directed pseudouridylations of specific rRNA nucleotides [2] . RNAse MRP and some box C/D snoRNAs , like U3 , U14 and U8 in higher eukaryotes are involved in pre-rRNA processing . The RNA component of box C/D and box H/ACA snoRNPs functions as an adaptor to guide the catalytic activity of the modification enzyme associated with the RNP to its target site [1] . Canonical box C/D snoRNAs contain conserved and distinctive sequence elements: the C/C′ ( 5′–RUGAUGA–3′ ) and D/D′ ( 5′–CUGA–3′ ) motifs , and one or two guide sequences of 10–21 nucleotides positioned upstream of the D/D′ regions that can base-pair to the RNA target [2] . These guide sequences direct ribose methylation to the nucleotide base-paired to the 5th nucleotide up-stream of the D or D′ sequence ( box D+5 rule ) [3] . Target complementarity of these guide sequences can be extended by means of another conserved region located elsewhere in the snoRNA [4] . The 2′-O-methylation reaction is catalyzed by the S-adenosyl methionine ( SAM ) dependent methyltransferase Nop1 ( Fibrillarin in higher eukaryotes ) [5] . Computational and biochemical analyses have led to the characterization and identification of most box C/D snoRNA targets [6 , 7] . However , in eukaryotes ( including yeast ) , several snoRNAs appear to lack a guide sequence or have complementarity to rRNA that is not characterized . These snoRNAs are classified as ‘orphan’ snoRNAs . Recent studies have shown that box C/D snoRNAs not only target rRNA and snRNA but also mRNAs and that they may have functions not related to site-directed methylation of ribose sugars [8–10] . In particular , fragments derived from snoRNAs ( sdRNAs–snoRNAs derived ) have been identified that exhibit miRNA-like characteristics and regulate alternative mRNA splicing in several species including mammals [9 , 11] . With an increasing number of studies emphasizing the association of box C/D snoRNAs with diseases such as cancer , Prader–Willi Syndrome ( PWS ) and obesity , it is becoming more likely that these snoRNAs are involved in other cellular processes than we are currently aware of [12–15] . Saccharomyces cerevisiae contains 46 box C/D snoRNAs . Apart from three snoRNAs , snR4 , snR45 and snR190 , the target and/or function of these box C/D snoRNAs have been characterized [16] . Although snR190 contains a guide sequence that could potentially methylate G2395 in the 25S rRNA , no methylation has been reported at this residue [16 , 17] . As far as snR4 and snR45 are concerned no complementary sequence for any of the rRNAs has been reported . Nevertheless , both snR4 and snR45 have been demonstrated to bind to canonical box C/D snoRNA proteins including Nop1 [4] . Recently , we and others identified and characterized two highly conserved acetylated cytosines in helix 34 and helix 45 of yeast , plant and human 18S rRNA along with their corresponding acetyltransferase—Kre33 ( yeast ) /NAT10 ( human ) [18–20] . Kre33 assembles with the pre-rRNA when transcription of the 3′ minor domain ( encompassing helices 44–45 ) has been completed and was proposed to bind as a homodimer [21–23] . In addition , Kre33/NAT10 aided by the adaptor protein Tan1/THUMPD1 acetylates serine and leucine tRNAs [16] . Here , we reveal that the box C/D snoRNAs snR4 and snR45 specifically guide Kre33 to two cytosines that are acetylated in yeast 18S rRNA . CRAC analyses revealed Kre33 binding sites on these snoRNAs as well as on the 18S rRNA ( the 5′ domain , helices 34 and 45 ) . We show that both snoRNAs establish extended bipartite complementarity around the targeted cytosines . This base-pairing depends on the putative helicase activity of Kre33 –which we also find to be essential for pre-rRNA processing–and results in looping out of targeted nucleotide . This is the first demonstration of an unexpected new function of box C/D snoRNPs in directing base modifications . Our data suggest that rRNA acetylation is mechanistically similar to pseudouridylation by H/ACA snoRNPs , where the residue to be modified is isolated and “bulged out” by flanking helices for ready access of the modification enzyme .
18S rRNA of eukaryotes contains two acetylated cytidine residues , one in helix 34 that is vital for translation fidelity , and another one in helix 45 that constitutes the decoding site of the ribosome ( Fig 1A ) [18 , 19] . Acetylation of both cytosines is catalyzed by the highly conserved acetyltransferase Kre33/NAT10 [18–20] . To gain insights into the mechanism of Kre33-directed acetylation , we mapped possible Kre33 rRNA binding sites on the 18S rRNA using CRAC ( UV cross-linking and analysis of cDNAs ) [24] . Kre33 mainly cross-linked to tRNAs and rRNAs ( Fig 1B ) . Confirming previous work [18] , Kre33 cross-linked to leucine and serine tRNAs that are acetylated by Kre33 at position C12 ( S1A Fig ) . Mapping of the cross-linking sites ( indicated by high frequency of mutations at specific sites in reads ) suggested that in the majority of these tRNAs Kre33 binds proximal to the ac4C-12 residue ( S1A Fig ) . Within the 18S rRNA , Kre33 binds predominantly to the 5′ domain of the 18S rRNA , specifically around helices 7 , 8 , 9 and 10 , making direct contacts with U-residues surrounding positions U132 , U192 , U259 , and U277 of 18S rRNA ( Figs 1C , 1D and S1C ) . Apart from the 5′ domain , Kre33 also cross-linked to helices 34 and 45 proximal to the acetylation sites ( Figs 1C and S1B ) . Unexpectedly , we observed significant cross-linking to the orphan box C/D snoRNAs snR4 and snR45 ( Figs 1B , 1E , 1F and S1C ) . The CRAC data showed that Kre33 primarily cross-linked to both the 5′ and 3′ regions of snR4 and to the 3′ end of snR45 ( Fig 1E and 1F ) . Both snR4 and snR45 also co-immunoprecipitated with Kre33-TAP above background levels , confirming the CRAC data ( Fig 2A and 2B ) . Co-precipitation of the box C/D snoRNA U3 ( Fig 2A and 2B ) is consistent with Kre33 associating with the SSU processome [25] . Fractionation of yeast extracts on density gradients revealed that both snR4 and snR45 co-sedimented with Kre33 in higher molecular weight fractions ( Fig 2C ) , suggesting that Kre33 associates with these snoRNAs in pre-ribosomes . The bulk of Kre33 was detected in lower molecular weight fractions , which presumably represent the population of Kre33 involved in tRNA acetylation ( Fig 2C ) . Recently , a vertebrate specific U13 box C/D box snoRNA was reported to be involved in 18S rRNA acetylation [18] . However , the precise role of this box C/D snoRNA in 18S rRNA acetylation remained unknown . U13 has been described as a vertebrate or plant specific box C/D snoRNA [26 , 27] . Conventional bioinformatics software did not allow us to identify any yeast box C/D snoRNA with significant homology to vertebrate U13 [18] . Phylogenetic analysis and systematic comparison of conserved nucleotide sequences of snR4 and snR45 , discovered in our CRAC analysis to bind specifically to Kre33 , with U13 revealed that snR45 displays a significant sequence similarity to U13 ( Figs 3A and S3 ) . Like U13 , snR45 showed extended bipartite complementarity to regions around the acetylated cytidine in helix 45 of 18S rRNA indicating that snR45 may act as an antisense guide . These results suggest that snR45 is the likely yeast functional orthologue of the vertebrate and plant U13 snoRNA . To determine whether yeast snR45 , similar to U13 , influences 18S rRNA acetylation , we analyzed the nucleotide composition of 18S isolated from a snr45 deletion strain using quantitative RP-HPLC . Loss of snR45 caused a 50% reduction in 18S rRNA acetylation ( Fig 3B ) , suggesting a direct role in the modification of one of the two acetylated cytidine residues that in 18S rRNA of S . cerevisiae are located at position 1280 in helix 34 and at position 1773 in helix 45 ( Fig 1A ) . To identify the cytosine that remained unmodified in the absence of SNR45 , we isolated 18S rRNA fragments corresponding to helix 34 and helix 45 , respectively , using a mung bean nuclease protection assay . RP-HPLC analyses of these fragments revealed that deletion of SNR45 leads to complete loss of only ac4C1773 in helix 45 , whereas ac4C1280 in helix 34 remained unaffected ( Fig 3C ) . These results show that yeast snR45 is indeed the functional orthologue of the vertebrate U13 snoRNA and demonstrate that acetylation of the highly conserved cytosine C1773 in helix 45 of the 18S rRNA is dependent on this box C/D snoRNA . In view of the specific involvement of snR45 in formation of ac4C1773 , we speculated that the second snoRNA that was enriched in our CRAC data , snR4 , could guide the ac4C1280 acetylation in helix 34 . Like for snR45 , conserved guide-like sequences in snR4 were found by phylogenetic comparison . These sequences exhibit extended complementarity to the region around ac4C1280 ( Figs 3D and S3 ) , which suggested that snR4 assists in acetylation of this residue . Indeed , deletion of SNR4 led to 50% reduction in the amount of acetylated residues ( Fig 3E ) and , as found by mung bean protection assay , a complete loss of only ac4C1280 without affecting acetylation of ac4C1773 ( S4A Fig ) . Upon deletion of both SNR4 and SNR45 18S rRNA cytosine acetylation was completely abrogated ( Fig 3E ) . Because Kre33 also catalyzes acetylation of serine and leucine tRNAs assisted by Tan1 , we next determined whether this also depends on the presence of snR4 and snR45 . Deletion of both snoRNA genes did not influence tRNA acetylation ( S4B Fig ) , suggesting that snR4 and snR45 are specifically involved in the acetylation of 18S rRNA . We conclude that the acetyltransferase Kre33 is guided to its two substrate cytosines at positions 1280 and 1773 on the 18S rRNA by the box C/D snoRNAs snR4 and snR45 , respectively . Box C/D snoRNAs are known to carry the 2′-O-methyltransferase Nop1/Fibrillarin that specifically acts on the sugar moiety of nucleotides . To our knowledge , this is the first demonstration that box C/D snoRNAs can contain more than one modification enzyme , in these cases both a ribose 2′-O-methyltransferase ( Nop1 ) and nucleobase acetyltransferase ( Kre33 ) . To better understand how snR4 and snR45 act in 18S rRNA acetylation , we generated secondary structure models based on in vivo DMS RNA structure probing data ( Fig 4A and 4C ) as well as phylogenetic analysis of snR4 ( Figs 4B and S2A ) and snR45 sequences ( Figs 4D and S3A ) . DMS predominantly modifies ring nitrogen of exposed A , G and C residues . Secondary structures or poor solvent accessibility protect from DMS methylation [28] . As shown in Fig 4 , almost all DMS-modifications were found on nucleotides that have been modelled to be single-stranded or at the termini of proposed helical domains , which we take as supportive evidence for the structures we propose . Both snoRNAs can adopt structures with a unique albeit comparable architecture . In each snoRNA , the 5′ end consists of a highly conserved sequence with 18S rRNA-complementarity that precedes the C box , while the second guide-like sequence resides in a loop abutting a phylogenetically well-supported helix downstream of the C′ motif . Phylogenetic evidence points to a pseudo-knot formed by this loop and nucleotides downstream of the C box , which would bring both guide regions in close proximity . The putative D′-region is non-canonical in most snoRNAs and not detectable as such in the human or plant counterparts . The bulk of either snoRNA is organized in a variable helical region that bridges the D′ and C′ motifs . With these secondary structures , we can begin to model how the bipartite base-pair interactions between 18S rRNA and the guide-sequences of snR4 and snR45 can expose the cytidine residue that has to interact with acetyltransferase domain of Kre33 ( Fig 4B and 4D ) . Kre33 protects predominantly the snoRNA regions involved in these base-pair interactions and mapping of its cross-linking sites on the 2D structure of snR4 and snR45 revealed that Kre33 makes a direct contact proximal to the guide sequence in the loop ( Fig 4B and 4D ) . Notably , the Kre33 binding sites on 18S rRNA are spatially adjacent to those of snR4 and snR45 , consistent with the idea that these orphan snoRNAs guide Kre33 to its target sites . We tested whether the guide sequences , referred from here onward as GS1 ( at the 5′ ) and GS2 ( in the loop ) , are essential for acetylation . GS1 of snR4 extends from nucleotide number 1 to 5 and according to our model establishes base pairing with nucleotides 1286 to 1290 of 18S rRNA ( Fig 4B ) . GS2 consists of nts 142 to 149 and interacts with 18S nts 1264 to 1271 ( Fig 4B ) . Similarly , GS1 of snR45 covers nts 1 to 15 that base-pair to nts 1781 to 1798 of 18S rRNA ( Fig 4D ) ; GS2 includes nts 120 to 127 of snR45 and binds to nts 1760 to 1767 of 18S rRNA . Along with other conserved regions ( shown in Figs 5A , 5B , S6A and S6B ) , we mutated these sequences in both snR4 and snR45 ( Figs 5A , 5B , S6C and S6D ) and expressed mutant sno-RNAs from plasmids in a strain with a double deletion for SNR4 and SNR45 . Mutant snoRNAs that were stably expressed were tested for functionality ( acetylation ) in vivo ( Figs 5C , 5D and S6G ) . Disruption of the base-pairing between GS1 of snR4 and snR45 with 18S rRNA resulted in complete loss of acetylation at residue C1280 and C1773 , respectively ( Fig 5E and 5F ) . Similarly , disrupting GS2 of both snoRNAs resulted in 88% and 82% reduction in acetylation at residue C1280 and C1773 , respectively ( Fig 5E and 5F ) . These observations demonstrate that the guide sequences GS1 and GS2 are essential for efficient acetylation . Apart from guide sequences , different conserved regions of both snR4 and snR45 ( highlighted in Figs 5A , 5B and S6A–S6D ) were altered . The effect of these mutations on acetylation at position 1280 or 1773 ( Fig 5E and 5F ) indicates that the helical segments that maintain the architecture of the snoRNAs are important ( snR4 ) or essential ( snR45 ) for their function in guiding acetylation , and this does not simply reflect a requirement for snoRNA stability , but presumably their impact on higher order structure of the snoRNAs ( Figs 5C , S6E , 5D and S6F ) . We also attempted to introduce compensatory 18S rRNA mutations in helix 34 and 45 to determine if the observed acetylation defects in snR4 and snR45 could be rescued . Every single mutation we generated in helix 34 or 45 , however , was lethal . We conclude that the predicted snR4 and snR45 guide sequences are essential for acetylation of 18S rRNA at C1280 and C1773 . Corroborating the involvement of these snoRNA in guiding acetylation , we found , among hybrid RNAs crosslinked to Kre33 , a chimera of snR4 and its proposed binding site on 18S rRNA ( see below ) . In addition to its acetyltransferase domain , Kre33 contains an N-terminal DEAD-box like helicase module ( Fig 6A ) . Changing the conserved lysine residue in the Walker A motif ( P loop ) of the Kre33 helicase domain to an alanine ( K289A ) ( Fig 6B , 6C and 6D ) leads to a ~90% reduction in 18S rRNA acetylation ( Fig 6D ) [16] . Notably , the corresponding lysine residue in the bacterial homolog of Kre33 , TmcA ( Fig 6C ) , has been shown to be indispensable for its ATPase-dependent helicase function [29] . How the helicase domain of Kre33 influences the acetylation reaction remains unclear . Helicases , including DEAD-box helicases , not only catalyze unwinding of RNA duplexes , but can also facilitate strand annealing [24–26] . Because the kre33-K298A mutant did not support normal acetylation , it seems unlikely that Kre33 is required for dissociation of snR4 and snR45 . We therefore hypothesized that the helicase domain of Kre33 facilitates the binding of snR4 and snR45 to their respective targets . To test this , we analyzed the distribution of snR4 and snR45 in cell lysates fractionated by sucrose density gradient centrifugation . Cell extracts were prepared from strains expressing the wild-type Kre33 or the kre33-K289A helicase mutant ( Fig 7A ) . Interestingly , while the K298A mutation did not strongly impact the distribution of Kre33 in the gradient , it significantly increased snR4 and snR45 levels in the lower molecular weight fractions ( Fig 7A ) . We also observed a similar profile for snR4 and snR45 upon Kre33 depletion ( S7 Fig ) . In contrast the helicase mutation did not noticeably affect Kre33 co-sedimentation with higher-order complexes ( Fig 7A ) . These data suggest that the predicted Kre33 helicase activity is not important for association with 90S pre-ribosomes , but it is important for efficient recruitment of these snoRNAs to 90S pre-ribosomes . A significant reduction in DMS reactivity of snR4 and snR45 in K289A mutant further suggests a decrease in Kre33 interaction with snR4 and snR45 . This interaction with Kre33 is likely vital for both snoRNPs to attain a conformation that is necessary for their interaction with pre-ribosomes . Our rRNA processing analysis of the helicase mutant revealed that loss of putative helicase activity impairs early A0 , A1 and A2 cleavages , leading to an accumulation of aberrant 23S and 22S rRNA species , a pattern that was previously observed upon hypomorphic expression of Kre33 ( Fig 8B ) [16] . We also observed a significant growth defect in the helicase mutant ( K289A ) compared to isogenic wild type and the acetylation deficient mutant ( H545A ) of Kre33 ( Fig 8C ) . This prompted us to test the sedimentation profile of other snoRNAs–snR40 , snR51 , snR55 , and snR49 , snR57 , and snR41 –in kre33-K289A , including those that bind in the vicinity of Kre33 binding sites on the 18S rRNA ( snR40 , snR55 , snR51 , and snR49 ) . Sedimentation profiles of snR41 , snR57 , snR51 and snR55 in the helicase mutant appeared very similar to those of snR4 and snR45 ( Fig 7A ) , suggesting that putative helicase activity of Kre33 is required for the association of both snR51 and snR55 with 90S pre-ribosomes . On the other hand , the sedimentation profiles of snR40 , and snR49 revealed that these snoRNAs accumulate in fractions with aberrant 23S pre-rRNA containing pre-ribosomes ( Fig 7A ) . This suggests that the putative helicase activity of Kre33 is necessary for efficient release of these snoRNAs from the pre-ribosome . The sedimentation pattern of snR10 and snR30 , snoRNAs that are not associated with Kre33 containing 90S [22] remained unchanged in the helicase mutant ( Fig 7A ) . Intriguingly , the annealing sites for snR40 and snR55 , which modify Gm1271 and Um1269 in helix 34 of 18S rRNA , overlap with the GS2 base-pairing site of snR4 and Kre33 ( Fig 8D ) . We found 2′-O-methylation at these sites to be significantly reduced in the kre33-K289A mutant ( Fig 8E and 8F ) . Considering the different sedimentation patterns of snR40 vs snR4 and snR55 in the Kre33 helicase mutant , this would imply that snR40 may remain bound on pre-rRNA , which in turn could block the association of snR4 or snR55 . Therefore , it is possible that Kre33 is required for facilitating the release of snR40 and the subsequent association of snR4 and/or snR55 . It is equally possible that in view of the strong processing defect in absence of Kre33 that other , essential assembly/processing factors rely on putative Kre33 helicase activity for their function or release . Therefore , the dependence on putative helicase activity of Kre33 for acetylation and other modifications could be indirect , namely as a consequence of upstream 90S conformational changes that are facilitated by Kre33 . We analyzed our CRAC-data-sets with the Hyb-pipeline [30] to find chimeric reads of different RNAs that were cross-linked to the same Kre33 molecule and therefore could have been ligated together during library preparation [31] . In direct support of a role of snR4 during acetylation by Kre33 , we retrieved one hybrid between GS2 of snR4 and its predicted target region in helix 34 , overlapping the snR40 and snR55 methylation sites ( S1 Table , Fig 8D ) . Providing further evidence for a role of Kre33 in controlling snoRNA occupancy on pre-rRNA the data-set contained two hybrids of snR40 , one with its target site in helix 34 , the other around Gm562 , a recently discovered snR40 target [32]; six hybrids between snR77 and the nearby methylation site at Um578; one hybrid of snR52 and one of snR79 with their annealing sites over Am420 and Cm1007 , respectively; and two hybrids of U14 with a region around position 100 , while , notably , seven hybrids of snR55 with its target region in helix 34 were found ( S1 Table ) . At all these sites but the one for snR79 we detected Kre33 binding ( S1 Table ) . Apart from many rRNA-rRNA hybrids , we retrieved hybrids of the 25S rRNA region around 900: one with snR4 , overlapping the target-site of snR40 and near that of snR60 for which two hybrids were retrieved . Furthermore , two hybrids for snR45 with a 3′ region of 25S were found . Whether these observations point to a direct role of Kre33 in the regulation of snoRNA-25S interactions and the timing of pre-rRNA processing remains to be determined .
Functional analyses of non-coding RNAs including snoRNAs have revealed that the majority of these ncRNAs act as a scaffold for the assembly of a catalytic complex and are responsible for the substrate specificity [1] . Although the large majority of the box C/D snoRNPs catalyze site-specific ribose methylation , a few are known to be involved in other processes , such as rRNA processing and regulation of alternative mRNA splicing [1 , 33] . Herein , we expand the functional repertoire of the box C/D snoRNAs to site-specific base acetylation . Recently , we identified two highly conserved acetylated cytidines , one in helix 34 and another one in helix 45 , in the 18S rRNA of yeast , plant and human , catalyzed by a highly conserved acetyltransferase , Kre33/NAT10 . In the present study , we showed specific involvement of two orphan sno-RNAs , snR4 and snR45 , in guiding Kre33-dependent acetylation of budding yeast 18S rRNA: snR4 for ac4C1280 and snR45 for ac4C1773 . Our analysis of the guide sequences for both snR4 and snR45 revealed that in contrast to the canonical box C/D snoRNAs , snR4 and snR45 function akin to H/ACA snoRNA where the guide sequences establish base-pairing with the regions on either side of the target nucleotide and result in looping out of this target nucleotide . Both snR4 and snR45 adopt a comparable fold and establish base-pairing in a similar fashion with the 18S rRNA on either side of the acetylated residue which leads to the fixation of a 9 to 11 nucleotides long bulge that contains the targeted cytosine ( Fig 4B and 4D ) . This looping out facilitates the accessibility of the targeted base to Kre33 and appears to be a salient feature of snoRNAs involved in modifying a nitrogenous base: isomerization in the case of H/ACA snoRNPs and acetylation in the case of snR4 and snR45 . Nevertheless , unlike for ribose methylation and pseudouridylation , it is very difficult to establish the sequence rules for snR4 and snR45 . This is primarily because so far , we have only encountered two such specialised snoRNPs . Apart from guide sequences that target snR4 and snR45 for Kre33 mediated acetylation , these snoRNAs contain canonical C/D and C′/D′ boxes with a complete set of core C/D box proteins; Nop1 , Nop56 , Nop58 and , being the primary binder , Snu13 ( S5 Fig ) [4] . We used the reads from previous CRAC studies of the core proteins Nop1 , Nop56 and Nop58 [4 , 24] to map their precise binding sites on the 2D structure models of these snoRNPs ( S5 Fig ) . Interestingly , these 2D models of snR4 and snR45 with the cross-linking sites of Kre33 along with core proteins , suggest that Kre33 and Nop1 presumably coexist on these snoRNPs–both enzymes interact at distinct sites . It is unlikely , however , that these snoRNAs mediate any conserved or specific 2′-O-methylation . Experimentally , it has been established that for efficient modification a minimal length of 7–8 base-pairs between a snoRNA-guide and its target is required and a maximum gap of 2 unpaired nucleotides between a functional guide and the associated D/D′ box is tolerated [3 , 4] . Only upstream of the D box in snR45 a conserved sequence is present that could direct modification of a short target sequence ( 5′-AAnUUUuU; nucleotide to be modified underlined ) , but in view of its length and irregularity , this seems a very poor guide for 2′-O-methylation . Our Kre33 CRAC analysis revealed three 18S rRNA binding sites: the 5′ domain , helix 34 and helix 45 . Our data suggest that Kre33 predominantly interacts with the 5′ domain of 18S rRNA and our results support the hypothesis that its interaction with h34 and h45 is aided by snR4 and snR45 , respectively . The Kre33 binding sites on 18S rRNA are spatially adjacent to that of snR4 and snR45 and a chimeric RNA that covers both GS2 of snR4 and its base-pairing site on h34 provides direct evidence . Interestingly , Kre33 has been previously characterized as a component of the SSU processome and shown to physically interact with several components of the U3 snoRNP containing 90S particle including Rrp9 , Enp1 and Nop14 [25 , 34] . In recent cryo-EM structures of the 90S particle from Chaetomium thermophilum and S . cerevisiae , Kre33 has been modeled into the head domain formed by the 5′ domain of 18S rRNA and suggested to bind there as a homodimer [23 , 34] . Our co-immunoprecipitation of U3 with Kre33 corroborated its association with the early 90S particle and our CRAC analysis precisely mapped Kre33 binding sites in the 5′ domain of 18S rRNA at nucleotide resolution , providing direct biochemical evidence . Recent biochemical analyses of the early assembly of the SSU processome have shown that Kre33 joins the SSU processome relatively late once the 3′ major ( h34 ) and minor ( h45 ) domain of 18S rRNA have been transcribed [22] . Interestingly , within the 5′ domain Kre33 occupies the same region where early 90S assembly factors such as Efg1 , Bfr2 and Lcp5 and snoRNAs snR44 , snR49 and snR51 assemble [22 , 35] . The 90S particle undergoes several structural and compositional reorganizations during its transition from 90S to pre-40S , especially around the time Kre33 joins [35 , 36] . These transitions are aided by different helicases that facilitate the release of assembly factors including snoRNAs [37–39] . The stable base-pairing interactions between snoRNPs and the rRNA must be removed to advance ribosome synthesis . Any delay in the release of these snoRNPs results in substantial rRNA processing defects , as observed for many mutants of helicases involved in ribosome biogenesis [2] . Furthermore , since several snoRNA guided modifications cluster in functionally conserved regions of the ribosome , the modification machinery involved must be released to enable other enzymatic complexes to modify rRNA in the same region [36] . It is important to note that apart from its acetyltransferase domain , Kre33 has an N-terminal helicase domain , raising the intriguing possibility that Kre33 assists in the release of factors like Efg1 , Bfr2 and Lcp5 or snoRNAs from the 5′ domain along with other helicases like Dbp4 and Has1 during the transition from 90S to 40S [36] . Our observation that mutations in the helicase domain of Kre33 cause early pre-rRNA processing defects , combined with aberrances in 18S-modification and association of several snoRNAs with the pre-ribosomes , strongly supports such a function for Kre33 . It is equally possible that the snoRNA sedimentation defects observed in the K289A mutant are likely indirect , due to alternative folding of rRNA that precludes the accessibility/binding of these snoRNAs . On the other hand , as far as the effect on snR4 and snR45 is concerned , our RNA probing analyses revealed a significant change in DMS reactivity , prompting us to conclude that putative helicase activity of Kre33 is needed for annealing . Nevertheless , the impact in K289A mutant on snR4 and snR45 might also be a consequence of the pre-rRNA processing defect . This is further complicated by the possibility that putative helicase activity of Kre33 is directly involved in the removal of snR40 that remains attached to pre-ribosomal particles in the helicase mutant . There seem to be various subpopulations of 90S [23 , 34 , 35] and the observed changes in snoRNAs sedimentation might also relate to the 90S sub-population that relies on Kre33 activity . The snoRNA-rRNA hybrids found in our CRAC-libraries yielded support for Kre33 action on snR4 , snR40 and snR55 and indicated that Kre33 could regulate the interactions of 18S rRNA sequences with other snoRNAs , such as snR77 , snR52 , and possibly U14 . CLASH on Kre33 should enable us to generate a more complete overview of snoRNA-rRNA interactions mediated by Kre33 . Overall , we conclude that the defect of 18S rRNA processing in the helicase mutant is concomitant with or due to defective release of a subset of snoRNAs . Apart from 18S rRNA acetylation in eukaryotes , Kre33 catalyzes ac4C-12 acetylation of serine and leucine tRNAs [18] , which was confirmed by finding specific crosslinks of Kre33 with these tRNAs ( S1A Fig ) . Neither snR4 nor snR45 influences acetylation of these tRNAs indicating that they are exclusively involved in the acetylation of 18S rRNA . Conversely , Tan1 is indispensable for Kre33-mediated tRNA acetylation and does not contribute to acetylation of rRNA . These data demonstrate that Kre33 utilizes different adaptor molecules to target different substrates in eukaryotes . Conspicuously , the human homolog of Kre33 , NAT10 , has been shown to exhibit lysine acetyltransferase activity especially towards microtubules and histones . In view of the very high sequence conservation between NAT10 and Kre33 it is tempting to speculate that Kre33 targets similar proteins in yeast . Future protein-protein interaction studies should be directed to identify other Kre33 adapter molecules . Another box C/D associated protein , the 2′-O-ribose methyltransferase Nop1/Fibrillarin , has been shown to target modification of histone H2A on glutamine residues both in yeast and human [40] . With the identification of different adaptor molecules of Kre33 , it is now possible to uncouple acetylation of tRNA from that of rRNA , which provides a great opportunity to analyze the functional significance of each modification independently .
All yeast strains and plasmids used in the present study are listed in S2 Table . Yeast strains were grown at 30°C in YPD medium ( 1% w/v yeast extract , 2% w/v peptone , 2% w/v glucose ) or in synthetic dropout ( 0 . 5% w/v ammonium sulphate , 0 . 17% w/v yeast nitrogen base , 2% w/v glucose ) . All kre33 mutant strains used in the present study were generated by distinctly transforming plasmids carrying Kre33 ( pSH35 ) , kre33-K289A ( pSH35-a ) , kre33-H4545A ( pSH35-c ) , kre33-R637A ( pSH35-d ) in a heterozygous deletion mutant of kre33 as constructed previously [18] . Tetrad analysis was performed to isolate a haploid kre33 deletion mutant containing respective complementing plasmids . For growth analysis , yeast cells were grown over night in YPD medium and diluted to an OD600nm of 1 followed by 1:10 serial dilutions . From the diluted cultures , 5 μl were spotted onto YPD plates and incubated at 37°C , 30°C or 16°C . After an overnight growth in YPD medium , TAP or HTP tagged strains were diluted to an OD600nm of 0 . 1 and were grown to an OD600nm of 0 . 8 in 1 L YPD . Cells were collected by centrifugation and washed twice with ice-cold PBS . The cell pellet was resuspended in 1 volume of ice-cold TNM150 ( 50 mM Tris pH7 . 8 . 1 . 5 mM MgCl2 , 150 mM NaCl , 0 . 1% Igepal Ca-630 ( NP-40 ) , 5 mM β-mercaptoethanol ) + protease inhibitors ( Roche protease inhibitors cocktail tablets ) and disrupted by shaking with 2 . 5 volumes of Zirconia beads ( 5x1 min with 1min resting on ice between each round ) . 3 volumes of ice-cool TMN-150 was then added to lysate which was clarified using centrifugation at maximum speed for 20 minutes at 4°C . The lysates were incubated for 2 h , at 4°C with end-over-end rotation with 125μL of IgG Sepharose beads . that had been equilibrated with TNM150 buffer ( 2 times washing with 3 mL of TNM150 ) . The beads were washed 5 times with 1 mL of TNM150 ( w/o protease inhibitors and were collected and resuspended in 250 μL of TNM150 ( w/o protease inhibitors ) . For analysis of the protein , beads were directly boiled in protein loading buffer ( 4% SDS , 20% glycerol , 10% 2-mercaptoethanol , 0 . 004% bromophenol blue and 0 . 125 M Tris-HCl , pH approx . 6 . 8 ) and separated by SDS-PAGE and analyzed by Western blotting using anti-TAP antibody ( Roche ) . RNA was isolated using phenol-chloroform extraction and the snoRNAs were characterized by Northern blotting as described previously [32] . Percent enrichment was calculated as the percent fold change in the signal of snoRNA band in total cell extract/input ( T ) versus pellet/eluate ( P ) . CRAC was performed exactly as recently described [41] . Cells were cross-linked in the Vari-X-linker for 12 seconds and CRAC libraries were paired-end sequenced ( 50 bp ) on a HiSeq2500 at Edinburgh Genomics , University of Edinburgh . The data reported in this paper have been deposited in the Gene Expression Omnibus ( GEO ) database , www . ncbi . nlm . nih . gov/geo ( accession no . GSE87480 ) . For CRAC data analysis , data from two biological replicates were analyzed as described[41]; yielding essentially the same outcomes; the data set with the better coverage ( 1 , 737 , 418 reads in data-set II vs 361 , 408 reads in data-set I ) has been used for the presented figures . Hybrids of different RNAs crosslinked to Kre33 were identified in both data sets using the HYB-pipeline exactly as described [29] . As a control , both data sets were compared with the results of CRAC-experiments done with a variety of other RNA-binding proteins , which confirmed the specificity of the Kre33-crosslinks to 18S rRNA , tRNAs , snR4 and snR45 and the retrieved snoRNA-hybrids . Abundant snoRNAs that we routinely observe in our CRAC-data , such as U14 ( snR128 ) , U3 ( snR17A , B ) and snR190 , were not specifically enriched in the Kre33 data-sets . snoRNAs homologous to snR4 and snR45 were retrieved and aligned as described previously [4] . The C/D and D′/C′ motifs were identified by their conservation ( S2 and S3 Figs ) and secondary structure modeling was done on the premise that a similar structure would be formed by closely related RNA molecules and should rely on phylogenetic evidence , i . e . supported by compensatory base-changes in helices . Alignments were prepared with Jalview ( www . jalview . org ) and secondary structures generated with Varna ( http://varna . lri . fr/ ) . The base-pair interactions proposed to form the pseudoknot were identified by analyzing the alignments with SPuNC ( http://www . ibi . vu . nl/programs/spuncwww/ ) . The 3D structure prediction was carried out with amino acid sequence of yeast Kre33 using recent protocol [42] . Kre33 structure was modelled on the crystal structure of its E . coli homolog , TmcA [29] . USCF Chimera was used to generate the ribbon-model for Kre33 [43] . Mung bean nuclease protection assay and RP-HPLC analysis for acetylation were performed exactly as described before [18] . Sucrose gradient centrifugation for the pre-ribosome analysis and co-localization studies was performed as described previously [18] . A flask with 100 mL YPD media was inoculated with an overnight culture to a starting OD600nm of 0 . 2 and grown at 30°C to an OD600nm of 0 . 8 to 1 . 5 . In the hood , two 15 mL aliquots of yeast culture in 50 mL polypropylene tubes were treated with 300 μL of 95% ethanol with 1:4 , v/v DMS ( Sigma ) or without as a negative control and mixed vigorously for 15 seconds . Both aliquots were then incubated with shaking for 2 minutes at 30°C . The reaction was stopped by placing the tube on ice and adding 5 mL of 0 . 6 M β-mercaptoethanol and 5 mL of isoamyl alcohol . After addition of the stop solutions the tubes were vortexed for 15 seconds and then centrifuged at 3000xg at 4°C for 5 minutes . The cell pellets were washed with another 5 mL of 0 . 6 M β-mercaptoethanol . Total RNA was isolated from the DMS treated cell pellet using GTC ( 4 M guanidium isothiocyanate , 50 mM Tris-HCl pH 8 . 0 , 10 mM EDTA pH 8 . 0 , 2% w/v sodium lauroyl sarcosinate and 150 mM β-mercaptoethanol ) as described [18] . The sites of A and C methylation in snR4 and snR45 by DMS were mapped by primer extension using primers specific to snR4 ( snR4-Mod-probe: TATTAATAGTTAAAGCACCG ) and snR45 ( snR45-Mod-probe: ATTTTATAAAAGCGTCCTTG ) . Primer extension was carried out exactly as described previously [7] . Ribose methylation was analyzed by deoxynucleoside triphosphate ( dNTP ) concentration-dependent primer extension , exactly as described elsewhere [7] . Um898 in 25S rRNA was mapped with PE40_25S: TATCCTGAGGGAAACTTCGG , Um1269 and Gm1271 in 18S rRNA with PE-34_18S: TAAGGTCTCGTTCGTTATCGC . An rDNA sequence ladder was prepared to precisely map the location of the modifications .
|
Base-pairing of box C/D small nucleolar RNAs ( snoRNAs ) with the target RNA guides site-specific ribose methylation by fibrillarin ( Nop1 ) , a highly conserved methyltransferase . Some box C/D snoRNAs , like yeast U3 , U14 and U8 in higher eukaryotes are not involved in methylation but are essential for pre-rRNA processing . Biochemical and computational analyses have led to the identification and characterization of most box C/D snoRNA targets . Several snoRNAs classified as ‘orphan’ snoRNAs lack a guide sequence or have complementarity to RNA that is not characterized . Considering the recent number of studies underscoring association of box C/D snoRNAs with genetic disorders including Prader–Willi Syndrome ( PWS ) , cancer and obesity , it is feasible that apart from ribose methylation and RNA processing , these snoRNAs are involved in several other cellular processes . Here , we report a novel and unexpected function for two yeast orphan box C/D snoRNAs , snR4 and snR45 , in site-specific base acetylation . We demonstrate that these box C/D snoRNAs guide cytosine acetylation by Kre33 , a factor essential for assembly and processing of the 90S pre-ribosome . Kre33 is known to acetylate a subset of tRNAs with the help of the adaptor protein Tan1 . However , here we show Kre33 dependent acetylation of 18S rRNA is guided by orphan snoRNAs . Our results expand the functional repertoire of box C/D snoRNPs and highlight yet another remarkable cellular modularity , where a single enzyme targets different substrates by means of either protein or RNA adaptors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"transfer",
"rna",
"small",
"nucleolar",
"rnas",
"chemical",
"compounds",
"gene",
"regulation",
"enzymes",
"enzymology",
"nucleotides",
"organic",
"compounds",
"fungi",
"pyrimidines",
"sequence",
"motif",
"analysis",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"bioinformatics",
"proteins",
"gene",
"expression",
"chemistry",
"ribosomes",
"yeast",
"biochemistry",
"rna",
"ribosomal",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"post-translational",
"modification",
"acetylation",
"helicases",
"database",
"and",
"informatics",
"methods",
"cytosine",
"genetics",
"organic",
"chemistry",
"biology",
"and",
"life",
"sciences",
"chemical",
"reactions",
"physical",
"sciences",
"non-coding",
"rna",
"organisms"
] |
2017
|
Specialized box C/D snoRNPs act as antisense guides to target RNA base acetylation
|
Little is known about the role of viral genes in modulating host cytokine responses . Here we report a new functional role of the viral encoded IE1 protein of the murine cytomegalovirus in sculpting the inflammatory response in an acute infection . In time course experiments of infected primary macrophages ( MΦs ) measuring cytokine production levels , genetic ablation of the immediate-early 1 ( ie1 ) gene results in a significant increase in TNFα production . Intracellular staining for cytokine production and viral early gene expression shows that TNFα production is highly associated with the productively infected MΦ population of cells . The ie1- dependent phenotype of enhanced MΦ TNFα production occurs at both protein and RNA levels . Noticeably , we show in a series of in vivo infection experiments that in multiple organs the presence of ie1 potently inhibits the pro-inflammatory cytokine response . From these experiments , levels of TNFα , and to a lesser extent IFNβ , but not the anti-inflammatory cytokine IL10 , are moderated in the presence of ie1 . The ie1- mediated inhibition of TNFα production has a similar quantitative phenotype profile in infection of susceptible ( BALB/c ) and resistant ( C57BL/6 ) mouse strains as well as in a severe immuno-ablative model of infection . In vitro experiments with infected macrophages reveal that deletion of ie1 results in increased sensitivity of viral replication to TNFα inhibition . However , in vivo infection studies show that genetic ablation of TNFα or TNFRp55 receptor is not sufficient to rescue the restricted replication phenotype of the ie1 mutant virus . These results provide , for the first time , evidence for a role of IE1 as a regulator of the pro-inflammatory response and demonstrate a specific pathogen gene capable of moderating the host production of TNFα in vivo .
The β-herpesvirus human cytomegalovirus ( HCMV ) is a species-specific virus and a clinically important pathogen that can establish both acute and latent infections . The murine counterpart ( MCMV ) provides a useful model for studying CMV natural infection in its natural host . CMV has a dsDNA genome that is sequentially expressed in a hierarchical cascade , immediate early ( IE ) , early ( E ) and late ( L ) [1] . The MCMV IE1 protein has been implicated in the transcriptional activation of viral early genes in combination with the IE3 protein [2] as well as in the expression of cellular genes [3]–[5] . The IE1-induced activation of gene expression is not completely understood , although the ability of IE1 to interact with chromatin through histones [6] , [7] might be one mode of action responsible for its transactivating functions . The ability of MCMV IE1 protein to activate cellular gene expression has been documented for genes involved in immune signalling pathways , DNA metabolism and cell cycle control [3] , [4] , [8] , [9] . Recently , a single point mutation in MCMV IE1 has been shown to disrupt its capacity of trans-activating cellular genes ribonucleotide reductase and thymidylate synthase , involved in nucleotide metabolism [10] . IE1 is also a potent disruptor of promyelocytic leukemia gene product ( PML ) oncogenic domains ( PODs/ND10 ) [11] , [12] , which have been implicated in intrinsic cell immunity to infection [13]–[15] . In vitro , an IE1-deleted MCMV grows with the same efficiency as wild type MCMV in different cell types [11] . However , this mutant is severely attenuated in immune competent BALB/c mice as well as in SCID mice lacking the adaptive immune control , and shows a reduced doubling time in various organs of BALB/c mice after hematoablative treatment [10] , [11] . On the basis of these studies we have previously speculated about a putative role of IE1 in interfering with some early intrinsic or innate immune mechanism [11] , involving pro-inflammatory cytokine production or signalling . In this context , the homologous HCMV IE1 protein has recently been reported to counteract the type I interferon response by targeting principally STAT2 [16] . Moreover , in the case of HCMV , there are an increasing number of studies pointing to IE1 promoting a nuclear environment conducive for viral expression by modulating epigenetic regulation of the viral major immediate early promoter ( MIEP ) . In these studies , ND10 associated-proteins , in particular hDaxx , have been shown to be repressors of the MIEP [17] . In this scenario , the dispersion of ND10s by the IE1 protein at early time of infection is thought to increase MIEP transcription efficiency indicating a potential role of ND10s as part of an antiviral defence mechanism inactivated by IE1 [12] . However , it is important to note that in the absence of the ie1 gene HCMV , unlike MCMV or rat CMV [18] , displays growth impairment under conditions of low multiplicity of infection ( MOI ) on primary fibroblasts [19] , [20] . The growth phenotype of the HCMV ie1-deficient strains adds an additional level of complication that can be avoided by utilising a rodent CMV model . Overall , while the mode of action of IE1 has been extensively studied in different species of CMV , the functional relationship of this protein in the regulation of host-virus interaction pathways , especially in a more biologically relevant context such as in infection of MΦ or at a whole organism level , remains unknown to date . Macrophages ( MΦs ) play a central role in CMV infection with regard to viral dissemination , replication and establishment of latency [21] , [22] . At the host response level , MΦs constitute one of the principal effectors of innate immunity . Upon activation , MΦs produce a number of pro-inflammatory cytokines such as TNFα , IL1 , IL6 , IFNα/β and IL12 , which are reported to be key mediators of the response against MCMV [23]–[25] . Of these pro-inflammatory cytokines , TNFα represents a key player in innate immunity . It is produced in response to a wide range of pathogens and is a hallmark of inflammatory diseases . One of the main activities of this pro-inflammatory cytokine is to provide protection against pathogen invasion . It is therefore not too surprising that there exist a number of pathogens which have developed several mechanisms to inhibit or modulate different stages of the TNFα action , ranging from the blockage of TNFα binding to the receptor to inhibition of specific TNFα-induced responses , such as gene expression or caspase activation [26]–[28] . To date , both human and mouse CMV have been reported to block TNFα-mediated gene activation by interfering with cell signalling and TNFα receptor expression [29]–[31] . Others have shown that HCMV inhibits TNFα-induced caspase-dependent apoptosis by encoding viral inhibitors [32] . It has also been shown that MCMV blocks caspase-independent apoptosis by direct binding and degradation of receptor-interacting protein RIP1 by the viral M45 protein [33] , [34] . Despite the several known viral strategies to modulate TNFα-induced responses , there are only a few examples in the literature of viruses that interfere with TNFα production [35] , [36] . In contrast , numerous studies have shown that bacteria and parasites almost exclusively interfere with TNFα by blocking its production , specifically targeting p38 MAPK , JNK and NF-κB signalling pathways involved in the activation of the cytokine production . For instance , Yersinia outer protein ( Yop ) J has been reported to bind to members of the MAPK family and IκB kinase β , and interfere with the MAPK and NF-κB signal transduction responsible for activating TNFα production ( reviewed in [37] ) . In addition , Yersinia pestis was reported to also block TNFα production in MΦs by inhibition of MAPK activation by the antigenic proteins Low calcium response V ( LcrV ) and Yop B [38] . Also in monocytes/MΦs , Salmonella SptP protein reduces TNFα production by blocking the Raf/MAPK signalling pathway [39] and Escherichia coli K1 protein specifically targets NF-κB for inhibition of the pro-inflammatory response [40] . Whether any of the described cell-culture characterised viral or microbial pathogen-mediated suppression of TNFα production also occurs in vivo in an intact physiological system is not known . We report in vivo and in vitro studies disclosing a previously unrecognized biological role of MCMV in moderating the production of pro-inflammatory cytokines , in particular TNFα involving an IE1 dependent mechanism , detectable at the protein and transcriptional level , in both immune intact strains of mice and in a severe immune-ablative model of infection . While the loss of ie1 results in increased sensitivity of viral replication in macrophages to TNFα inhibition , in vivo the ablation of TNFα is insufficient to rescue the replication phenotype .
MΦs are a key cellular population for MCMV infection . Furthermore , replication of MCMV in primary bone marrow-derived macrophages ( BMMΦ ) reflects more closely the in vivo phenotype than the replication in fibroblasts [21] . Accordingly , our first experiments sought to characterize the level of infection of MCMVdie1 and parental and revertant MCMV strains in this particular cell population . Therefore , bone marrow ( BM ) cells were isolated from 10–12 week-old male BALB/c mice for selection of BMMΦ during 7 days of maturation in cell culture before use in infection studies . The F4/80+CD11b+ phenotype of the BMMΦ was confirmed by flow cytometry analysis prior to infection ( Figure S1 ) . First , we determined the virion DNA:PFU ratios for wild-type ( referred to as MCMV in this manuscript ) , ie1 deficient ( MCMVdie1 ) and revertant-virus ( MCMVrev ) infection stocks as reported in previous studies [10] , [41] . Figure 1A shows that MCMVdie1 develops a similar number of genome equivalents per PFU in comparison with MCMV or MCMVrev . Furthermore , Western blot analysis determining the level of expression of the MCMV early protein E1 24-hrs after infection of BMMΦ further indicated a similar level of early stage-infection of MCMVdie1 and control viruses ( Figure 1B ) . For later stages of infection , the viral growth was determined by standard plaque assays in a single-hit viral growth analysis in the fibroblast cell line NIH-3T3 as well as in BMMΦ ( Figure 1C and D , respectively ) . As expected the results in Figure 1C show that in fibroblasts MCMVdie1 had no defective growth in these cells [11] . However , infection of BMMΦ ( Figure 1D ) results in a small but significant difference in the growth of MCMVdie1 in comparison with MCMV and MCMVrev . This macrophage phenotype for MCMVdie1 infection is reflective of its in vivo phenotype [10] , [11] and raises the question of whether this might be due to a possible macrophage-specific pro-inflammatory cytokine response . On the basis of the attenuated growth phenotype of ie1 null mutant in infection of mice deficient in adaptive immunity ( SCID mice ) , we have previously speculated about a role for IE1 in countering intrinsic or innate cell immunity [11] . As indicated above , infected macrophages characteristically produce a vigorous and varied pro-inflammatory cytokine response , in particular TNFα and are therefore in contrast to other cells , such as NIH 3T3 fibroblast cells that are restricted in their repertoire of cytokine expression . Since ie1 is known to influence gene expression , it is plausible that IE1 can affect cytokine gene expression in monocyte/MΦ cells . Thus , to directly test the possibility of IE1 protein modulating the cytokine response in infected BMMΦ , we first investigated pro-inflammatory cytokine production after infection of BMMΦ . In these experiments , levels of several cytokines were measured at early times post infection prior to any new infectious virus production . BMMΦ were either mock infected or infected with MCMVdie1 and MCMV at an MOI of 1 . Supernatants were harvested at 10 hpi and flow cytometry-based Cytometric Bead Array ( CBA ) was performed for IFNγ , IL10 , IL12p70 and TNFα . As seen from Figure 2A none of the infections differentially altered IFNγ , IL10 or IL12p70 production . However , the TNFα production was only mildly induced by MCMV , but in contrast , MCMVdie1-infected BMMΦs showed a markedly pronounced TNFα response producing a >15-fold higher amount when compared to the levels seen for MCMV-infected cells . It is worth noting that the response can vary due to batch variability in primary macrophages cultures and viral stocks . Nevertheless , similar results have been consistently obtained from multiple independent BMMΦ preparations using different viral stocks . Furthermore , after infection of a MΦ cell line ( RAW 264 . 7 cells ) with MCMVdie1 or another ie1-mutant ( IE1stop ) , an approximately 15- to 20-fold higher production of TNFα is also observed in comparison with MCMV ( Figure 2B ) . In the case of the IE1stop mutant the IE1 open reading frame is selectively interrupted , without genetically resecting any further sequences , strongly suggesting that the IE1 protein is responsible for the suppression of the TNFα production and not any other viral protein , that might potentially originate from a the MCMV major IE region . To further characterise the modulation of TNFα by IE1 during infection we investigated the kinetics of TNFα production . In the following experiments BMMΦ were mock-infected or infected with either MCMVdie1 , MCMV or an ie3 defective MCMV ( MCMVdie3 ) [42] during a 24 h time course . MCMVdie3 is completely defective for viral replication , with viral gene and protein expression essentially restricted to the IE1 and IE2 proteins [41] and thus serves as an excellent comparator for the loss of ie1 in the MCMVdie1 mutant . Figure 2C shows that after MCMV infection BMMΦ produced detectable levels of TNFα from 4 h onward , until 24 h when it started to decrease . In agreement with the preceding experiment , MCMVdie1 generally induced higher levels of TNFα . Again , the levels of induction found in cellular supernatants from MCMVdie1-infected BMMΦ were higher than those induced by MCMV . It is noteworthy that MCMV does not completely inhibit TNFα production and this may reflect a level of leakiness derived from a pool of low IE1 expressivity of infected cells . Alternatively it could be that low levels of TNFα may be advantageous . In this time course experiment MCMVdie3 moderates the TNFα response with delayed kinetics to the MCMV and MCMVdie1 reaching similar levels as the MCMV by 24 h post-infection ( Figure 2C ) . The observation that MCMVdie3 develops lower levels of TNFα than wild-type MCMV may reflect the importance of an active viral gene transcription that produces double-stranded viral transcripts to induce a full TNFα response . This possibility is consistent with studies showing the role of the cytoplasmic detector for viral RNA RIG-I in inducing and sustaining a TNFα response to infection with a DNA-virus [43] . In such a scenario we would anticipate to see MCMV infection developing higher levels of TNFα than the MCMVdie3 . Thus , while an on-going infection is required to trigger a full TNFα response these results appear to indicate that mutation of the ie1 gene is strongly associated with increased production of the cytokine . To further evaluate whether the altered production of TNFα after infection with MCMVdie1 is due to the loss of ie1 gene function and not an accidental second site mutation in the recombinant virus , a revertant virus of MCMVdie1 was also tested . In these experiments BMMΦ were either mock-infected or infected with MCMV , MCMVdie1 and MCMVrev . Supernatants were used to test the levels of TNFα after 10 and 24 hpi . As shown in Figure 2D significant production of the cytokine was found only after infection with MCMVdie1 at both time points , whereas comparably low levels of TNFα were found after infection with MCMV and MCMVrev . Overall , the viral deletion mutant experiments indicate that the MCMV ie1 gene plays a previously undisclosed role in moderating the production of a key pro-inflammatory cytokine , namely TNFα , in infected BMMΦ . In our next experiments we sought to determine whether the production of TNFα is specifically associated with the productively infected cells , or bystander cells or both . For these studies immunofluorescence staining monitored the intracellular production of TNFα while staining for viral early E1 antigen simultaneously monitored infection . RAW 264 . 7 cells were mock-infected or infected with MCMV or MCMVdie1 . After 24 hpi , double labelling was performed for MCMV E1 protein and TNFα . As shown in Figure 3 , production of TNFα was only detectable in cells that also expressed the E1 protein . LPS was used as a positive control to trigger TNFα production independent of viral infection showing that all cells were competent for TNFα production ( Figure S2 ) . These experiments directly show that induced TNFα production is almost exclusively associated with infected cells and not by neighbouring bystander cells . These observations raise the possibility that IE1 might play a direct role in moderating the TNFα response in infected macrophages . To explore this possibility and to test whether IE1 alone is capable of moderating TNFα gene expression independent of the infection process , we next used transient transfection assays . To quantify the TNFα promoter activity we constructed a reporter plasmid , pTNF-gLuc , containing the gaussia luciferase reporter gene [44] , [45] under the control of the murine TNFα promoter/enhancer ( position −670 to +1 ) element . We first assessed the activity of the pTNF-gLuc using the cell line Bam25 [42] that stably expresses the viral IE-genes . For these experiments Bam25 or NIH3T3 cells were transfected with pTNF-gLuc together with the vector pGL3 ( Promega ) for normalisation of the reporter gene expression measurements . As the infection process results in activation of Toll-like-receptor ( TLR ) signalling , triggering the expression of pro-inflammatory cytokines , including TNFα [46] , we sought to ensure TLR activation in the absence of infection by treating transfected cells with bacterial lipopolysaccharide ( LPS ) . LPS is a known potent inducer for TNFα gene expression [47]–[50] . The transfected cells were incubated for 24 h , culture medium was changed and subsequently stimulated with 100 ng/ml LPS for 4 h . As a control for inhibition of the LPS induced TNFα expression , control cultures were pre-treated with 300 nM Trichostatin A ( TSA ) , an established inhibitor of the TNFα response , for 2 h prior to the LPS stimulation . As shown in Figure 4A , LPS can further induce the normalised reporter gene expression in transfected NIH3T3 cells , while the pre-treatment with TSA inhibits this induction . In contrast to this , LPS stimulation does not induce higher expression levels of gaussia luciferase in the Bam25 cell line . These experiments indicate that the expression of the viral IE-genes results in reduced capacity of LPS-induced TNFα activation . Since the Bam25 cells also express IE3 and IE2 proteins in addition to IE1 , we next sought to test if IE1 expression alone is sufficient to restrict LPS-induced reporter gene expression in MΦs . However , transfection of primary MΦs has a low efficiency and therefore we used RAW G9 cells for these experiments . RAW G9 cells were co-transfected with 125 ng of an IE1-expression plasmid , or as a negative control the pcR3 . 1 cloning vector ( Invitrogen ) , and pGL3 for normalisation . After 48 h incubation medium was changed and cells stimulated with 10 ng/ml and 100 ng/ml LPS respectively . The results shown in Figure 4B reveal that transfection with 125 ng IE1 plasmid was sufficient to block stimulation with both , low and high doses of LPS . These experiments support the suggestion that IE1 alone is sufficient to block LPS induced reporter gene expression . We next asked whether the absence of IE1 in the context of an infection of BMMΦ also has an effect on TNFα expression at the RNA level . Cells were mock infected or infected at an MOI of 1 with MCMVdie1 , MCMV or MCMVrev and harvested for total RNA extraction after 10 hpi . Total RNA was used in quantitative ( q ) RT-PCR to measure relative tnf transcript levels . Data was normalized against gapdh levels and the levels of tnf from MCMV-infected cells were used as a calibrator . Figure 5 shows relative levels of tnf transcripts . When compared to the mock-infected samples , MCMV infection induced expression of tnf RNA ( p<0 . 05 ) . However , TNFα mRNA levels in the absence of IE1 protein in MCMVdie1-infected BMMΦ were 2 . 5-fold higher than those seen for the parental and revertant MCMVs ( p<0 . 01 ) . MCMVrev induction of tnf expression was similar to that induced by MCMV . We conclude from these experiments that the viral-induced TNFα production is reduced by MCMV in an IE1-dependent manner in the context of infected MΦs . TNFα transcription is induced by different stimuli in a cell-type dependent manner [28] , [51]–[54] . The key signalling molecules , NFκB , JNK and p38 MAPK are a common set of targets by which microbial and fungal pathogens inhibit TNFα production [28] , [37] , [38] , [40] . However , in quantitative Western blot experiments involving infection of BMMΦs with MCMV , MCMVdie1 or MCMVrev analysis of IκBα , p38 and JNK proteins and their phosphorylated forms ( Figure 6A ) reveals that at 10 hpi there are no significant differences between the three different viruses tested ( for quantification see Figure S7 ) . All three viruses induce activation of both p38 and JNK kinases but not IκBα . The absence of IE1 upon infection did not result in a differential change in the level of IκBα or in the expression or phosphorylation levels of the tested kinases at 10 h post infection . It is possible that the time point analysed is too late to detect any quantitative differences and that they may be temporally masked by a range of cross-talking signalling events during the infection process . While lack of activation of NFκB by MCMV is consistent with our previous studies it is possible that its activation is only detectable at IE times of infection . For the purpose of exploring whether differences in the activation of NFκB may be observable at more immediate early times we have used a stably transfected cell line , RAW G9 cells [55] , to visualise the activation of NFκB . These cells express the NFκB subunit p65 as a GFP fusion protein . In rested cells the NFκB complex is distributed throughout the cytoplasm , producing a weak and diffuse GFP signal in the cells ( Figure S4 , mock treated sample ) . After LPS stimulation NFκB is activated and translocates to the nucleus , leading to an enrichment of GFP in the nucleus . The translocation of p65-GFP to the nucleus from the cytoplasm consequently develops a visibly concentrated nuclear GFP signal . We therefore infected RAW G9 cells for 45 min and observe enrichment in nuclear fluorescent cells for LPS but not for MCMV and MCMVdie1 infected samples ( Figure S4 ) . Quantification of the level of cytoplasmic and nuclear fluorescence indicates similar levels for MCMV and MCMVdie1 . This indicates that MCMV and MCMVdie1 are equally restrictive for NFκB signalling during the IE1-phase of infection . As noted in the previous section the expression of IE1 in the transient transfection reporter assays appears to have a similar inhibitory effect on the LPS-induced activity of the TNFα- promoter as the HDAC inhibitor TSA ( Figure 4A ) . The mechanism for the TSA-mediated inhibition of TNFα activation is known to work through inhibiting p38 phosphorylation in RAW 264 . 7 cells [56] . Therefore we sought to test if IE1 in the transient transfection assay can interfere with the phosphorylation of p38 after LPS stimulation . RAW cells were either left untreated or were transfected with pp89UC or pEYFP-C2 , an YFP expressing control vector . 48 h after transfection cells were stimulated with LPS for 15 min and pp38 levels were analysed by quantitative western blot analysis ( Figure S7 ) . Figure 6B shows , as expected , that treatment of mock-transfected cells with LPS leads to an increase in p38 phosphorylation . Notably , transfection with the IE1 plasmid ( pp89UC ) led to a high level of p38 phosphorylation that could not be further increased by subsequent LPS stimulation . In our system , the inability of LPS to induce higher levels of pp38 in IE1-expressing cells is not due to an exhaustion of the p38 pool , since transfection with the YFP control plasmid led to ∼1 . 4-fold higher signal for pp38 than the transfection with pp89UC ( Figure S7 ) . These experiments therefore indicate that IE1 might impart the ability to restrict the phosphorylation of p38 . We next sought to extend this observation to the infection system at IE-times of infection . We compared the induction of p38 phosphorylation by LPS ( 15 min ) in infected RAW cells at 4 and 10 hpi in comparison to mock-infected samples . As shown in Figure 6B the treatment of control cultures with LPS induces the phosphorylation of p38 . In contrast to this the virus infected samples showed a slightly weaker signal for pp38 than the mock infected samples at 4 hpi , indicating that the virus might be capable to moderate the phosphorylation of p38 . MCMVdie1 , however , showed no increased phosphorylation over the MCMV infection for both the non-stimulated and the LPS stimulated samples . Normalisation of the pp38 signal to the levels of p38 protein revealed that there was no detectable difference in levels of pp38 in the LPS stimulated samples ( Figure S7 ) . In accordance to this we found no increase in levels of phosphorylated p38 at 10 hpi . Under these experimental conditions quantification of the western blot and normalisation to p38 abundance actually revealed that treatment of MCMVdie1 infected cells induced slightly less pp38 compared to MCMV infected samples ( Figure S7 ) . To also check for differences in phosphorylation of JNK we subsequently determined the levels of pJNK on the same membranes and found that MCMV and MCMVdie1 induced the same level of pJNK signal as stimulation with LPS ( Figure S7 ) . Altogether these experiments provide evidence against a mechanism involving IE1 inhibition of p38 , NFκB or JNK signalling for the induction of TNFα in the context of an infected macrophage . While modulation of TNFα by a wide range of pathogens has been extensively studied in cell-culture ( reviewed in [28] ) , it remains to be determined whether such modulation occurs in vivo . Accordingly , biological significance of cell culture-based observations should be treated with some caution . Thus , to directly determine the physiological significance played by IE1 in moderating TNFα , we next studied the role of MCMV IE1 protein in vivo . As suggested above for BMMΦs in cell culture , IE1 modulates TNFα expression already at the transcript level . We therefore tested if this applies also to TNFα transcription in host organs relevant to CMV pathogenesis . The experiment was performed in immune compromised , γ-irradiated BALB/c mice , an established model for lethal , multiple-organ CMV disease [57] . In this model it was demonstrated previously [10] that wild type MCMV replicates in the liver with a doubling time of ∼19 h , whereas MCMVdie1 was found to be growth-attenuated with a doubling time extended to ∼34 h . Likewise , MCMVdie1 was found to be growth-attenuated also in spleen and lungs [10] . As suggested above for BMMΦs in cell culture , IE1 modulates TNFα expression . We therefore tested if this applies also to TNFα in the liver by quantitating steady-state levels of tnf transcripts with qRT-PCR . As seen in Figure 7A , TNFα gene expression in both infected groups is detectable above the baseline defined by uninfected liver tissue . At first glance , one might conclude that the TNFα gene expression level is modestly higher in MCMV-infected livers compared to MCMVdie1 . For correctly interpreting the gene expression data , however , one must consider the different viral burden in tissue due to the growth-attenuation of the ie1-deletion mutant [10] . To account for this problem , we normalized TNFα gene expression to the number of viral E1 transcripts determined by qRT-PCR ( Figure 7B ) . As shown in Figure 1 expression levels of E1 were comparable in the infected BMMΦs in the in vitro system . However , in the more complex in vivo system it is unclear at which level this impairment is manifest in the viral replication cycle . To exclude that an impaired E- or L-gene expression could influence our normalisation strategy for the qRT-PCR , we sought to use a method for normalisation that is independent of transcript levels in infected cells . To normalise for the different levels of infection in the sampled organs we therefore directly measured the number of infected cells by in situ immunostaining for the late major capsid protein ( MCP , M86 ) ( Figure 7C ) . Regardless of which normalization strategy was used , the normalised expression level of TNFα was found to be ∼10-fold higher in MCMVdie1-infected livers than in MCMV infected organs . For comparison , normalized gene expression levels of the cytokines IFNβ and IL10 were measured in order to further probe the role of IE1 in regulating the innate cytokine response . Interestingly , infection with the ie1-deletion mutant also induced significantly higher amounts of IFNβ transcripts ( Figure 7B and C ) as compared to MCMV . In accordance with the IL-10 expression in BMMΦs ( Figure 2A ) , IL10 transcription in the liver was induced by the infection ( Figure 7A ) but was not significantly influenced by IE1 ( Figure 7B and C ) . Altogether , TNFα , and to some degree also IFNβ , are suppressed on the transcript level in an ie1-dependent manner in vivo at a relevant organ site of CMV disease . We next determined if transcriptional control by IE1 also translates into TNFα protein levels in host tissues . In a first set of in vivo experiments , groups of 4 to 5 BALB/c and C57BL/6 immune competent mice were infected by the i . p . route , with the viral mutant and control strains . Viral yields and TNFα levels were determined at day 4 p . i . for selected organs . As seen in Figure 8 A and C , MCMVdie1 showed the expected attenuated phenotype in both mouse strains when compared to MCMV and MCMVrev [11] . Viral titres in spleen , liver , heart and lung from infected BALB/c mice were 6- , 12- , 21- and 35-fold reduced , respectively . The attenuation was even more dramatic in kidneys where the MCMVdie1 titres were reduced by a factor of >100 . In C57BL/6 , as in BALB/c mice , both MCMV and MCMVrev exhibited comparable replication ( Figure 8C ) . MCMVdie1 titres were significantly reduced in all organs examined with a 10-fold reduction in spleen and liver and over 100-fold reduction in kidneys . From this data we conclude that the attenuation shown by MCMVdie1 is not mouse strain-dependent , since in BALB/c , C57BL/6 and also in the 129 strain ( data not shown ) as well as in γ-irradiated mice [10] , the ie1-deletion mutant MCMVdie1 is not able to replicate as efficiently as MCMV or MCMVrev . As described above for the RNA expression studies , MCMV induces TNFα production in an acute infection [58]–[60] , and it is understood that TNFα levels positively correlate with the level of infection [59]–[61] . In agreement with these previous studies we also observed a positive correlation between TNFα protein levels and infectivity per gram of tissue in kidneys and heart ( Figure S3 ) . On this basis , TNFα was quantified and cytokine levels were calibrated against viral titres in those samples where infectious virus was detectable . Accordingly , normalized TNFα levels are shown in Figure 8B and D . At day 4 p . i . , MCMVdie1 induces by an order of magnitude higher levels of TNFα than those detected for MCMVrev in all organs tested except in the spleen of both BALB/c ( C ) , and C57BL/6 mice , and liver of C57BL/6 mice ( D ) . Taken together , these results support a role of IE1 in moderating the production of TNFα in vivo . Because of the attenuated phenotype of the ie1-deletion mutant , the virus titres per gram of tissue differ significantly between infected groups . It is therefore possible that an IE1 expressing virus is simply able to replicate more efficiently and consequently will block TNFα production by alternative mechanisms . We therefore sought to establish an equivalent level of infection ( PFU per gram of tissue ) by adjusting the initial infectious dose of the inoculums and determining TNFα production . In the following experiments , immune competent BALB/c mice were infected with 3×105 PFU of MCMV or 3×106 PFU of MCMVdie1 , and both viral titres and the absolute TNFα response were compared in different organs at days 4 and 7 post-infection . Under such conditions , viral titres of MCMV and of the ie1-deletion mutant were found to be comparable in all organs ( Figure 9 , left panels ) , except in the kidneys ( Figure 9C ) at day 4 p . i . In comparison to day 4 p . i . the observed reduction in titres by day 7 p . i , is indicative of the clearance of the virus from these various organs . TNFα levels after infection were measured in the different organs from viral-infected and mock-infected mice ( right panels ) . Results show that MCMV induced TNFα production after 4 days of infection in all organs tested , except in the heart ( Figure 9D ) . Importantly , there was also a marked increase in the levels of TNFα between MCMV and MCMVdie1 . In general , the mutant virus induced statistically higher levels of TNFα in spleen , liver and kidney after 4 days of infection; however , there was no difference in the heart ( Figure 9D ) . At one week of infection , viral clearance of both MCMV and MCMVdie1 has mostly taken place in all organs tested with the levels of TNFα having returned to mock levels except for a residual higher level in the spleen for the MCMV infection ( Figure 9A ) . Strikingly , and in stark contrast to the MCMV infection , TNFα levels remained elevated in MCMVdie1 infected animals . Altogether these results show that the production of TNFα can be significantly moderated by an ie1-dependent mechanism in an acute in vivo infection by MCMV . It has been shown by others that despite the protective effects of TNFα pre-treatment in vitro an in vivo pre-treatment does not increase survival rates of MCMV infected mice [62] . We therefore first sought to determine the effect of TNFα on MCMV and MCVMdie1 replication in macrophages in vitro . Differentiated macrophage cultures were pre-treated with 1 U/ml or 10 U/ml recombinant mouse TNFα ( Endogen , PIERCE; 10 µg/ml corresponded to 105 U/ml ) for 24 h prior to infection and viral replication monitored by plaque assay at day 3 p . i . As expected Figure 10A shows that pre-treatment with TNFα in vitro reduces viral titres of MCMV . Notably TNFα has a much stronger effect on the titres of MCMVdie1 , which are reduced to the limit of detection , indicating that MCMVdie1 is more susceptible to TNFα control compared to MCMV in vitro . We next sought to determine the contribution of TNFα in controlling viral replication in vivo by using mouse strains deficient either in TNFα expression ( TNF−/− ) or TNFα signalling ( TNFRp55−/− ) . For these investigations groups of mice were infected with either MCMVdie1 or MCMVrev as indicated in Figure 10B and viral titres were measured in organs at day 4 p . i . to investigate if abrogation of TNFα expression or signalling could enhance or rescue the replication of MCMVdie1 . In both tested mutant mouse strains the impairment of TNFα expression ( TNF−/− ) or TNF signalling ( TNFR−/− ) did not rescue impaired replication of the MCMVdie1 in the livers , spleens or other organs analysed ( see Figure S5 ) . To complement this experiment and to control for side effects of the genetic ablation of TNFα we also analysed the effects of antibody-mediated depletion of TNFα in vivo . Mice were infected with equal doses of MCMVdie1 and MCMVrev and anti-TNFα antibodies were injected at day 0 and 2 p . i . As shown in Figure S6 at 4 dpi no significant differences between the MCMV and MCMVdie1 viruses could be detected in all analysed organs . Together these results show that while TNFα is sufficient to inhibit MCMVdie1 in vitro it is not essential for inhibition in vivo and may indicate that in the more complex in vivo setting other host factors can complement for the loss of TNFα function .
In this study we have presented evidence revealing that a viral gene , ie1 , of MCMV is involved in altering the pro-inflammatory cytokine response , in particular TNFα production in vitro and in vivo . Our results not only identify a new and previously undisclosed functional role for IE1 in moderating the inflammatory response to infection , but also show for the first time the association of a specific pathogen-encoded gene to restrict TNFα protein and RNA production in vivo in the context of a natural infection . Several pathogens have been shown to modulate TNFα-induced response by a wide range of different mechanisms . For instance , direct interaction with TNFα has been reported for the M-T2 protein of the Myxoma virus avoiding TNFα-TNFR interaction [63] . African swine fever virus also targets TNFα-induced gene expression in infected-MΦs by a mechanism involving the viral protein A238L [64] . Inhibition of TNF receptor has also been shown for Adenovirus and Poliovirus [65] , [66] . Moreover , HCMV and MCMV have also been shown to down modulate TNF receptor expression [29]–[31] , blocking TNFα-induced gene expression in infected cells . However , there are only a few examples in the literature of viral proteins that interfere with TNFα production [36] . From the literature it can be seen that targeting the production of TNFα is a common feature in microbial infections , as seen for Salmonella , Yersinia and E . coli [37]–[40] . All known mechanisms by which microbial pathogens alter the TNFα production involve targeting p38 MAPK and JNK kinases and/or NF-κB activation ( reviewed in [28] ) . Here , we demonstrate that the viral IE1 protein of MCMV moderates TNFα production in infected BMMΦs . Moreover , although the exact mechanism by which IE1 exerts its effect on TNFα production remains open , our experiments studying the contribution of key signalling molecules p38 MAPK , and JNK , as well as NF-κB , suggest that IE1-induced modulation of TNFα production does not involve altering the function of these signalling proteins . Although , we do not exclude that the phosphorylation of JNK or other phosphatases is also influenced by other viral factors and therefore effects of IE1 deletion on phosphorylation of these signalling factors could be masked . Comparison of TNFα levels between MCMV and the MCMVdie3 mutant virus showed that MCMVdie3 infection produces much lower levels of TNFα compared to MCMV infection until 24 hpi . This indicates the importance of viral gene transcription for inducing and sustaining a full scale TNFα response . This observation is in good accordance with evidence showing that RIG-I mediated detection of Myxoma virus transcripts in human macrophages is necessary to induce a full TNFα response [45] . Furthermore we show that MCMV does not completely inhibit TNFα production . It has been demonstrated that CMV can reduce the effects of TNFα by down-regulation of the TNFα receptor during infection [29] and thereby increasing tolerance to extra-cellular TNFα levels . A major factor determining permissiveness of cells of the myeloid-monocytic lineage for infection with CMV is the state of maturation ( [67] and references therein ) and it has been demonstrated that TNFα also facilitates maturation of macrophages [68] . In addition TNFα can be pro-viral by initiating infection through signalling to the MIE enhancer . Taken together this indicates that low levels of TNFα may well be tolerated by CMV and could under certain conditions also have a pro-viral effect . Thus it may not be to the best advantage for CMV to completely abrogate TNFα production . IE1 has been described to have a very general effect on gene transcription levels including more genes than are controlled by p38 signalling directly and is better known for its intra-nuclear activities including the disruption of ND10 bodies [15] , [69] . In the case of HCMV , the ability of IE1 to disrupt ND10 bodies has been correlated with the disruption of an intrinsic defence mechanism involving nuclear repressor proteins , such as hDaxx and histone deacetylases ( HDACs ) , to inhibit viral immediate-early gene expression [17] . In case of MCMV , a direct interaction of IE1 with Daxx and HDAC2 has been demonstrated [12] in good accordance with the general induction of transcription caused by IE1 [9] , probably involving de-repression of chromatin . It has been demonstrated that expression of TNFα is partially regulated by chromatin re-modelling and that treatment with TLR ligands such as LPS initiates de-repression of chromatin in macrophages [47] , [48] . Therefore it is possible that the influence of IE1 on HDAC function could interfere with TNFα expression by disrupting the de-repression or maintaining repressive chromatin associated with the TNFα promoter . It remains to be determined how IE1 precisely inhibits TNFα expression while also potentially inducing a de-repression of chromatin . It is noteworthy that Trichostatin A ( TSA ) , which we used as a control reagent to block LPS induced TNFα gene expression [56] , is also an inhibitor of HDACs and therefore leads to a general de-repression of chromatin , while it is capable of inhibiting TNFα expression [50] , [56] . This presents parallels between TSA and IE1 and allows speculating that these molecules work potentially through a similar mechanism . A known mechanism for the effect of TSA on TNFα is mediated through the p38-inhibtor MAPK phosphatase-1 ( MKP-1 ) . In this case the stability of the interaction of MKP-1 with its target molecule , p38 MAPK , is regulated by the acetylation of the interaction site , which is negatively regulated by HDACs . Treatment with the HDAC inhibitor TSA increases the level of the acetylated form of MKP-1 and thereby links the inhibitory function of MKP-1 on the p38 signalling pathway to HDAC activity [56] . In accordance with this it has been furthermore demonstrated that LPS stimulation activates HDAC-3 [50] and is also known to induce expression of HDAC members in BMMΦs [70] . This would negatively regulate MKP-1 activity , increasing p38 signalling and therefore TNFα expression . Since it is established that IE1 interacts and inhibits HDAC activity [12] it raises the question if IE1 , at least partially , could function through this mechanism . In this scenario we would anticipate increased pp38 levels in MCMVdie1-infected cells . However , we do not detect any increase in pp38 levels in MCMVdie1 infected cells compared to MCMV in the first 10 hpi and thus strongly indicating that IE1 does not interfere with p38 phosphorylation in the context of infection . Although notably , studies have shown that changes in acetylation of proteins can take up to 9–11 h to occur in the case of histones [71] . This might indicate that IE1-mediated changes on MKP-1 acetylation become apparent at later stages of the infection and could therefore have a role in the late phase of replication , complementing other viral factors interfering with TNFα action , such as M45 [29] , [31] . A more thorough investigation of MKP-1 and its effects on MCMV replication and induction of TNFα will be necessary to clarify if IE1 inhibits p38 signalling . However , it is also noteworthy that the immune-modulatory cytokine IL10 is induced upon infection by MCMV in MΦs and in vivo and since IL10 is known to function as an anti-inflammatory mediator , it is possible that a host IL10 autocrine loop might be involved in modulating TNFα . While we cannot exclude the possibility for IL10 involvement in TNFα suppression we failed to detect any significant difference in expression of IL10 between MCMV with and without ie1 indicating that the mechanism for TNFα moderation does not involve regulation of IL10 . While many pathogens have been shown to counter-regulate TNFα production in cell culture , in particular in MΦs , little or no information is available to indicate whether this mode of regulation also occurs in an in vivo system . Our investigations show that the viral gene , ie1 , in the context of a natural infection contributes to a significant moderation of TNFα production in multiple organs in vivo . TNFα levels , relative to the amount of detected virus , in selected organs were significantly higher in animals infected with MCMVdie1 than in animals infected with MCMV or MCMVrev , and this was consistently observed in several genetic backgrounds and in different infection models . This is quite striking , as we would anticipate a significantly reduced inflammatory response to be associated with the severely attenuated in vivo growth by MCMVdie1 . In vivo we observed a relatively enhanced production of the pro-inflammatory cytokines TNFα and IFNß but not for the anti-inflammatory cytokine , IL10 , with MCMVdie1 infection . Because of the attenuated phenotype of the ie1-deletion mutant , the virus titres per gram of tissue differ significantly between infected groups . It is therefore possible that an IE1 expressing virus simply is able to replicate more efficiently in vivo and consequently blocks TNF production through alternative methods . While we have not investigated in the present study other similarly attenuated viruses in their ability to develop an elevated pro-inflammatory cytokine response , we have evaluated TNFα production levels in the context of establishing an equivalent level of infection per organ between the ie1 deficient and wild-type strains . These studies clearly indicate that even in the presence of comparable levels of infection , MCMVdie1 induces significantly higher levels of TNFα . Those levels were sustained for at least 1 week after infection , whereas wild-type MCMV-induced TNFα production had returned to the mock-infected levels by 7 days p . i . The results of our investigation provide the first demonstration of a counter-regulatory role encoded by the ie1 gene in moderating the TNFα cytokine production in the context of a natural infection in vivo . In accordance with the attenuated in vivo phenotype of MCMVdie1 we could demonstrate a higher sensitivity of MCMVdie1 replication in vitro compared to MCMV but neither genomic deletion of the TNFα gene , the TNFα receptor gene or in vivo depletion of TNFα by administration of anti-TNFα antibodies could rescue the attenuated MCMVdie1 phenotype , indicating that other anti-viral host factors complement the loss of TNFα function . In accordance to this we find a slight but significant increase in IFNb1 expression in infected organs . In summary , we identify a new biological role for the ie1 gene of MCMV involving the regulation of pro-inflammatory cytokines , especially TNFα production , in both in vitro and in vivo infections . This suggests a novel viral counter-immune strategy preventing a robust inflammatory response during an infection . Our findings demonstrate that IE1 , in addition to blocking intrinsic cell defences , also acts as a virulence factor contributing to viral modulation of the inflammatory response . This new role of ie1 may also have therapeutic anti-viral and anti-inflammatory applications in the future . In this regard , our results provide the first evidence of a viral strategy capable of suppressing pro-inflammatory TNFα production in vivo mediated by a pathogen-encoded gene .
All procedures involving animals and their care were approved by the Ethics Committee of the University of Barcelona ( Spain ) and were conducted in compliance with institutional guidelines , as well as with catalan ( Generalitat de Catalunya decree 214/1997 , DOGC 2450 ) laws and policies . In the United Kingdom experiments were approved by the University of Edinburgh Animal Procedures and Ethics Committee and were performed under licence from the United Kingdom Home Office under the Animals ( Scientific Procedures ) Act 1986 . In Germany , mice were bred and housed under specified pathogen-free conditions in the Central Laboratory Animal Facility of the Johannes Gutenberg-University , Mainz . Animal experiments were approved according to German federal law under permission number 177-07-04/051-62 . In Croatia all animals were bred and housed at the Breeding Facility of the Faculty of Medicine , University of Rijeka . Experiments were approved by the Ethics Committee of the University of Rijeka and were performed in accordance with the Croatian Law for Protection of Laboratory Animals ( matched with EU legislation ( DIRECTIVE 2010/63/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 22 September 2010 on the protection of animals used for scientific purposes ) . The murine fibroblast cell line NIH3T3 cells ( ATCC CRL1658 ) and the macrophage cell line RAW 264 . 7 ( ATCC TIB-71 ) were obtained from the American Type Culture Collection ( Manassas , VA ) . Primary murine embryonic fibroblasts ( MEFs ) were prepared from embryos of pregnant BALB/c mice on day 16 of gestation . RAW G9 cell line was constructed as described previously [55] and expresses a p65-GFP fusion protein under transcriptional control of the native p65-promoter . NIH3T3 , were cultured in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% calf serum ( CS ) and RAW 264 . 7 , RAW G9 and MEFs were cultured in Dulbecco's modified Eagle's medium ( DMEM ) supplemented 10% fetal CS ( FCS ) . All cell media also contained 2 mM glutamine and 100 U of penicillin/streptomycin per ml . The ie1-deficient mutant ( MCMVdie1 ) and corresponding revertant virus were described in [11] . The IE1stop mutant carries a stop codon at the 5′-end of exon 4 of the MCMV ie1 gene preventing the synthesis of the pp89 IE1 protein . Briefly , two successive PCR rounds were performed using the primer pairs IE1_stop_fw1 ( 5′-GCAATCTTACAGGACAACAGAACGCTCTACACTGGAGGATGACGACGATAAGTAGGG-3′ ) and IE1_stop_rv1 ( 5′-TACAAACCACTCTTATATTCCAGTGTAGAGCGTTCTGTTGCAACCAATTAACCAATTCTGATTAG-3′ ) , and IE1_stop_fw2 ( 5′-GATTGATAGTTCTGTTTTATCATGAGGTGTGCAATCTTACAGGACAACAGAACGCTCTACACTGGA-3′ ) and IE1_stop_rv2 ( 5′-CTGTCTTTCATATTCACCCACACAGAACACTTGAGTTATACAAACCACTCTTATATTCCAGTGTAGAGCG-3′ ) , respectively , to amplify a kanamycin resistance marker , followed by recombination in E . coli using the MCMV BAC pSM3fr [72] and applying the en passant mutagenesis procedure as described in [73] The parental BAC-derived MCMV strain MW97 . 01 ( [72] , named MCMV in this study ) and the recombinant MCMVs were propagated on NIH3T3 cells . Cells were grown to 70%–80% confluency and infected at an MOI of 0 . 01 with MCMV , MCMVdie1 and MCMVrev in DMEM supplemented with 2% CS . When cultures reached cytopathic effect , supernatants were harvested and kept at −70°C after clearing cellular debris . Viral titres were determined by standard plaque assays on MEFs . Bone marrow derived macrophages ( BMMΦ ) were prepared from 10–12 week-old male BALB/c mice as described previously [74] . Femur lavages were plated out at 5×105 or 8×105 cells per well ( 24- and 6-well plates , respectively ) and left for maturation for 7 days in DMEM:F-12 containing 10% FCS , 10% L929 conditioned media as a source of macrophage colony stimulating factor [75] , and 100 U of penicillin/streptomycin per ml . Plasmid pp89UC codes for the MCMV IE1 protein pp89 , carrying the insert of plasmid pIE 100/1 [2] in the pUC19 vector . IE1 expression is under control of its native MCMV major immediate early enhancer/promoter . Plasmid pEYFP-C2 was produced by transferring the EYFP ORF from pEYFP-C1 ( BD Biosciences Clonetech ) into the pEGFP-C2 vector ( BD Biosciences Clonetech ) , replacing the EGFP ORF using endonucleases AgeI and BsrGI . The expression plasmid pTNF-gLuc was produced by replacing the MCMV enhancer/promoter in the plasmid pmCherryP2AGLucKanR [76] with the TNFα enhancer/promoter ( −670 to +1 ) . To do so the TNF enhancer/promoter was synthesised by MWG/Operon including restriction sites for KpnI and BglII , used for the cloning procedure . To transfect cultured cells with expression plasmids we used the Lipofect LTX reagent ( Invitrogen ) . IE1 or TNF-reporter plasmids were mixed with the firefly vector pGL3 ( Promega ) for internal control of transfection efficiency . To ensure that all cultures were exposed to the same amounts of DNA during transfection , mixtures of plasmids were adjusted to the same total amount of DNA within one experiment with the cloning vector pCR3 . 1 ( Invitrogen ) . For transfection cell line specific protocols provided by Invitrogen were used ( detailed protocols available at: www . invitrogen . com/transfections ) and adjusted to the respective culture size as described in the NIH3T3 specific protocol . Gaussia luciferase assays were carried out as described previously [76] , with the exception that plain DMEM medium was used to produce coelenterazine working solution . Substrate ( 50 µl ) was mixed with 50 µl culture supernatant and measured with a POLARstar plate reader ( BMG Labtech , UK ) . To measure firefly luciferase activity we used the Luciferase Assay System ( Promega ) as described in the manual . In short , cells were lysed for 15 min on a rocking platform and 15 µl lysate were subsequently transferred into a black/white plate and mixed with 30 µl substrate for measuring luminescence in the POLARstar plate reader . To stimulate luciferase expression , cells were washed 1× in growth medium and then stimulated with LPS ( 10 or 100 ng/ml in growth medium , as indicated ) for 4 h before reporter gene activity in the culture supernatant was measured . Maturation of BMMΦ cells was tested by flow cytometry analysis for the expression of murine proteins specific for mature macrophages . Analyses were performed for F4/80 ( Caltag Laboratories , UK ) and CD11b ( eBiosciences , UK ) using a FACScan or FACSCalibur instrument . Cells were infected with the different viruses at an MOI of 1 , unless specified otherwise . After 1 h of adsorption , cells were washed in PBS and incubated in fresh DMEM:F12 supplemented with 10% FCS , 10% L929 , and 100 U of penicillin/streptomycin per ml . 8 weeks-old male BALB/c and C57BL/6 mice were obtained from Charles Rivers Lab ( Barcelona , Spain and Edinburgh , UK , respectively ) , C57/Bl6 TNF−/− mice were obtained from B&K Universal ( UK ) . Experiments with C57/Bl6 TNFRp55−/− mice [77] were conducted in the University of Rijeka . Animals were housed at the animal facilities ( University of Barcelona , University of Rijeka or Edinburgh University ) under pathogen-free conditions . Mice were intraperitoneally inoculated with 3×105 or 2–3×106 PFU of tissue culture-derived MCMV recombinants . At designated times mice were sacrificed and spleen , liver , kidneys , heart and lungs were removed , weighted and harvested as a 10% ( wt/v ) homogenate . Part of the tissue homogenate was sonicated and viral titres were determined on MEF by standard plaque assay . When infectious virus could not be detected in a particular organ , a titre corresponding to the limit of detection of the assay was assigned to that particular organ in order to calculate the median values . For in vivo analysis of TNFα transcription levels , female BALB/c mice were immune depleted and infected essentially as described in greater detail previously [78] . In brief , hematoablative conditioning of 8- to 9-week-old female mice was achieved by total-body γ-irradiation with a single dose of 6 . 5 Gy . Intraplantar infection at the left hind footpad was performed ∼2 h later with 105 PFU of either BAC-cloned virus MCMV wild-type [72] or MCMVdie1 [11] . Cytokine levels were determined from cell culture supernatants by flow cytometry ( BD Cytometric Bead Array , BD Biosciences ) and from tissue homogenates by ELISA ( mouse TNF-α/TNFSF1A DuoSet ELISA Development kit , R&D Systems Europe Ltd . ) following manufacturer's instructions . TNFα concentration was determined by reading the absorbance at 450 nm in a POLARstar OPTIMA Multifunction Microplate Reader ( BMG LabTech , UK ) . 50 µl of suspension containing 104 cells were added onto each dot of a Teflon coated microdot slide . Cells were incubated at 37°C for 24 h . After infection and treatment , cells were fixed with 4% paraformaldehyde for 10 min and permeabilized in 0 . 5% Triton X-100 for 3 min . After blocking for 1 h with 20% FCS , cells were stained with primary antibody Croma103 ( provided by S . Jonjic ) and TNFα ( Santa Cruz , SC-1351 ) and secondary antibody Alexa Fluor 488 rabbit anti-mouse IgG and Alexa FluorAR 594 donkey anti-goat IgG ( both from Invitrogen , CA ) , respectively . RAW G9 cells ( 5×103 per well ) were seeded in glass bottom optical 384 well plates . After 24 h cells were stimulated with 10 ng/ml LPS or infected with virus in 20 µl . Subsequently cells were fixed with 4% PFA for 30 min ( RT ) , quenched in 50 mM NH4Cl solution for 5 min and permeabilised with 0 . 5% Triton X-100 for 3 min . Cell nuclei were counterstained with DAPI . For analysis pictures were taken on an OPERA system ( PerkinElmer , USA ) with 40× magnification and for GFP nuclear translocation assay the corresponding standard script of the Acapella ( v2 . 3 ) analysis software was used . RNA from BMMΦ cells in 6-well plates was extracted by adding 200 µl Trizol reagent ( Invitrogen , CA ) and incubating for 5 min . Samples were then transferred to a 1 . 5 ml microfuge tube and 40 µl chloroform were added . Samples were incubated at room temperature for 15 min before being centrifuged ( 13 , 000 rpm , 4°C , 5 min ) . The upper aqueous layer was removed and 0 . 1 volumes 3 M NaOAc , 2 . 5 volumes EtOH were added . Samples were incubated ( −20°C , 60 min ) and then centrifuged ( 13 , 000 rpm , 4°C , 30 min ) . Supernatant was removed and the pellets washed in 200 µl 70% ( v/v ) ethanol and centrifuged as before . RNA pellets were then resuspended in 50 µl RNAse-free H2O . RNA quantity and quality was assayed by measuring the A260 and the A260/A280 ratio respectively using a Nanodrop ND-1000 ( Nanodrop Technologies , DE ) . RNA was isolated as described in detail previously [10] from whole livers shock-frozen in liquid nitrogen . For each sample , 2× Taqman PCR mix ( Applied Biosystems; CA ) was mixed with 40 U of Superscript III ( Invitrogen , CA ) . 4 µl total RNA was then added and each sample split into two reactions . A Taqman primer/probe set ( Applied Biosystems , CA ) for the gene of interest was then added to one reaction at the recommended concentration while a Taqman primer/probe set for GAPDH mRNA was added to the other reaction . Samples were then run on a MX1000P quantitative PCR thermal cycler ( Stratagene , CA ) . Samples were first heated to 50°C for 30 minutes then heated to 95°C for 10 minutes . Samples were then subjected to 40 cycles under Taqman standard conditions . Stratagene MXPro software was used to analyse the data . Quantification of β-actin and E1 transcripts was performed with homemade primers and probes as described previously , yielding amplicons of 88 bp [10] and 200 bp [79] , respectively . Quantification of IFNβ , IL10 and TNFα transcripts was done with custom-made TaqMan Gene Expression Assays . Reactions were performed in a total volume of 20 µl , including 4 µl of 5 X QIAGEN OneStep RT-PCR buffer , 1 µl of QIAGEN OneStep RT-PCR enzyme mix , 668 µM of each dNTP , 1 . 5 mM additional MgCl2 , 0 . 132 µM 5-carboxy-X-rhodamine as passive reference , and 0 . 6 µM of each β-actin primer and 0 . 26 µM of β-actin probe , or 1 µl of the corresponding 20× TaqMan Gene Expression Assay Mix ( in the case of IFNβ , IL10 , and TNFα ) . Reverse transcription was performed at 50°C for 30 min . The cycle protocol for cDNA amplification started with an activation step at 95°C for 15 min , followed by 40 cycles of denaturation for 15 sec at 95°C and a combined primer annealing/extension step for 1 min at 60°C during which data collection was performed . The efficiencies of the RT-PCRs were >90% throughout . Relative quantifications were made with the comparative CT ( cycle threshold ) method as described previously [10] , [79] . Number of viral genomes was determined by measuring copy numbers of the viral M115 gene as described previously [41] . Immunohistochemical staining of major capsid protein ( MCP; M86 ) present within inclusion bodies in the nuclei of infected cells was performed in liver tissue sections as described previously [10] . Whole lysate was extracted from non-infected and infected-BMMΦ using Beadlyte® Cell Signalling Lysis Buffer ( Milipore , UK ) , following manufacturer's instructions . BMMΦs were cultured for 7 days in 6-well plates at a seeding density of 106 cells/well . Prior to infection , cells were serum starved for 24 h . After infection cells were washed with ice-cold TBS and lysed in lysis buffer , containing Complete Miniprotease Inhibitor ( Roche , UK ) and Phosphatase Inhibitor Cocktail I and II ( both from Sigma , UK ) . Protein concentration was determined using the MicroBCA protein assay ( Pierce , UK ) , following manufacturer's instructions . Equal amounts of protein were mixed with 2× Laemmli Sample Buffer , containing 10% of DTT and loaded onto a 10% SDS-PAGE gels . Proteins were transferred to a PVDF membrane which was then probed with anti-phosphor-p38 , anti p38 , anti-phosphor-JNK , anti JNK ( Cell Signaling , UK ) , and anti IκBα ( Sigma , UK ) and incubated with HRP-conjugated anti mouse or rabbit IgG ( Cell Signalling , UK ) . Proteins were detected with ECL Plus Western Blotting Detection Reagents ( Amersham Biosciences , UK ) using VersaDoc imaging system 4000 . Densitometric analysis of the blots was performed by Quantity One software 4 . 5 . 0 . For pp38 MAPK and IE1 expression plasmid experiments , RAW 264 . 7 cells were washed with PBS and resuspended in whole-cell lysis buffer ( 50 mM Tris-HCl , pH 7 . 5 , 100 mM NaCl , 1% NP40 , protease inhibitors , and phosphatase inhibitors ) , and cell lysates were centrifuged at 4°C for 10 min and the collected supernatants were stored at −20°C . Protein concentration was measured by Pierce BCA assay ( Thermo Scientific ) . For Western blotting , proteins were separated by 10% SDS-PAGE , transferred to Immobilon-FL membranes ( Millipore ) , and probed with rabbit anti-p38α ( Santa Cruz , sc-27578 , 1∶2000 ) , mouse anti-pp38 MAPK ( Cell signalling , 9216L , 1∶1000 ) , and rabbit anti-β-actin ( Cell Signalling , 4970 , 1∶2500 ) diluted in PBST ( 0 . 1% Tween20 ) . For secondary anti-rabbit IR-680 ( Invitrogen , A21109 , 1∶10 , 000 ) , IR-800 anti-mouse ( Thermo Fisher Scientific , 35571 , 1∶10 , 000 ) , antibodies were diluted in PBST ( 0 . 1% Tween20 ) . For visualization , the Odyssey protocol ( LI-COR ) was followed . The fluorescence was quantified using ImageJ ( ver . 1 . 45s ) . Statistical analysis was performed in MATLAB ( 2007 , The MathWorks , Inc ) . Viral titres and cytokine levels from in vivo experiments were compared by using Mann-Whitney U test . Analysis from in vitro experiments was compared by Student's t-test . For data normalisation , values of respective control ( e . g . firefly activity ) for each sample were expressed relative to the average of the control in the respective experiment . This normalisation factor was then used to correct the corresponding measured value in the assay ( e . g . gLuc activity ) . ( Entrez Gene ID ) TNFa ( Tnf , 21926 ) , TNFR ( Tnfrsf1a , 21937 ) , p38 ( Mapk14 , 26416 ) , NFκB-p65 ( Rela , 19697 ) , JNK ( Mapk8 , 26419 ) , Ifnb1 ( 15977 ) , IL10 ( 16153 ) , CD11b ( Itgam , 16409 ) , F4/80 ( Emr1 , 13733 ) , Ifng ( 15978 ) , MKP-1 ( Dusp1 , 19252 ) , IL12A ( 3592 )
|
The suppression of the production rather than the blockage of action of the potent inflammatory mediator TNFα is a particular hallmark of anti-TNFα mechanisms associated with microbial and parasitic infections . Whether this mode of counter-regulation is an important feature of infection by viruses is not clear . Also , it remains to be determined whether a specific pathogen gene in the context of an infection in vivo is capable of modulating levels of TNFα production . In this study we disclose a virus-mediated moderation of TNFα production , dependent on the ie1 gene of murine cytomegalovirus ( MCMV ) . The ie1 gene product IE1 is a well-characterized nuclear protein capable of altering levels of host and viral gene expression although its biological role in the context of a natural infection is to date unknown . We provide evidence showing that ie1 is associated with a moderated pro-inflammatory cytokine response , in particular with TNFα production . Further , we show that the viral moderation of this cytokine is not only readily apparent in vitro but also in the natural host . The identification of a viral gene responsible for this mode of regulation in vivo may have therapeutic potential in the future in both anti-viral and anti-inflammatory strategies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"viral",
"diseases",
"cytomegalovirus",
"infection"
] |
2012
|
Ablation of the Regulatory IE1 Protein of Murine Cytomegalovirus Alters In Vivo Pro-inflammatory TNF-alpha Production during Acute Infection
|
Spatiotemporal regulation of cell migration is crucial for animal development and organogenesis . Compared to spatial signals , little is known about temporal signals and the mechanisms integrating the two . In the Caenorhabditis elegans hermaphrodite , the stereotyped migration pattern of two somatic distal tip cells ( DTCs ) is responsible for shaping the gonad . Guidance receptor UNC-5 is necessary for the dorsalward migration of DTCs . We found that BLMP-1 , similar to the mammalian zinc finger transcription repressor Blimp-1/PRDI-BF1 , prevents precocious dorsalward turning by inhibiting precocious unc-5 transcription and is only expressed in DTCs before they make the dorsalward turn . Constitutive expression of blmp-1 when BLMP-1 would normally disappear delays unc-5 transcription and causes turn retardation , demonstrating the functional significance of blmp-1 down-regulation . Correct timing of BLMP-1 down-regulation is redundantly regulated by heterochronic genes daf-12 , lin-29 , and dre-1 , which regulate the temporal fates of various tissues . DAF-12 , a steroid hormone receptor , and LIN-29 , a zinc finger transcription factor , repress blmp-1 transcription , while DRE-1 , the F-Box protein of an SCF ubiquitin ligase complex , binds to BLMP-1 and promotes its degradation . We have therefore identified a gene circuit that integrates the temporal and spatial signals and coordinates with overall development of the organism to direct cell migration during organogenesis . The tumor suppressor gene product FBXO11 ( human DRE-1 ortholog ) also binds to PRDI-BF1 in human cell cultures . Our data suggest evolutionary conservation of these interactions and underscore the importance of DRE-1/FBXO11-mediated BLMP-1/PRDI-BF1 degradation in cellular state transitions during metazoan development .
Cell migration is important for organogenesis and development of animals . Numerous extracellular guidance cues and receptors for the spatial control of cell migration have been identified and characterized [1] . However , little is known about the temporal regulation of cell migration and how the spatial and temporal signals are coordinated to generate a specific and reproducible pattern of cell migration during development . The bilobed gonad of C . elegans hermaphrodites develops from a four-cell primordium positioned in the ventral midbody [2] . The shape of the two symmetrical U-shaped gonadal arms is determined by the migratory paths of the two distal tip cells ( DTC ) , leader cells found at the tip of each arm [3] . The DTCs undergo three sequential phases of migration and re-orient twice during the three larval developmental stages , thus providing a paradigm for the study of the spatio-temporal regulation of cell migration in vivo [2] , [3] . In phase I during the L2 and early L3 stages , the anterior and posterior DTCs move centrifugally along the ventral body wall muscles towards the head or tail , respectively ( Figure 1A ) . In phase II , they turn 90 degrees and move from the ventral to the dorsal muscles , then , during phase III , they again turn 90 degrees and move centripetally along the dorsal body wall muscles and halt in the mid-body . Both orthogonal turns occur during the late L3 stage . The timing of these turns is regulated by a redundant function of the heterochronic genes daf-12 , dre-1 , and lin-29 [4] . A single mutation in any of these three genes has no effect on DTC migration , but mutation of 2 or 3 of the genes delays the L3-specific DTC migration pattern , which fails to take place even in L4 or the adult . lin-29 , daf-12 , and dre-1 encode , respectively , a zinc-finger transcription factor , a steroid hormone receptor similar to the vertebrate vitamin D and liver-X receptor , and an F-Box protein of an SCF ubiquitin ligase complex [4]–[6] , indicating that a complex mechanism , involving steroid hormone signaling , gene transcription , and protein degradation , is responsible for the temporal control of the dorsal turn . However , how these three genes function to do so is unclear . The dorsal migration of DTCs is regulated by the guidance receptors UNC-5 and UNC-40 ( a homolog of Deleted in Colorectal Cancer ) [7]–[9] , which drive DTCs to move away from the ventrally localized UNC-6 to the dorsal side [10] , [11] . Dorsally localized UNC-129/TGF-β also promotes DTC dorsal migration through UNC-5 and UNC-40 receptors [12] . Mutations in these genes disrupt the ventral-to-dorsal migration of DTCs . unc-40 appears to be transcribed in the DTCs throughout their migration [7] , whereas unc-5 is transcriptionally up-regulated at the time when the dorsal turn is initiated [13] . Precocious expression of unc-5 cDNA at the time when DTCs have not yet turned dorsalward induces unc-6-dependent precocious dorsal migration [13] . These data show that unc-5 is both necessary and sufficient for DTC dorsal migration and that the increase in unc-5 transcription is responsible , at least in part , for the initiation of DTC dorsal migration [8] , [9] , [13] . However , how unc-5 is temporally regulated to direct dorsal migration precisely at the late L3 stage is unclear . In this study , we performed genetic screening for mutants defective in DTC migration and isolated blmp-1 mutants . Our results showed that blmp-1 is a heterochronic gene that acts with daf-12 , dre-1 , and lin-29 in a regulatory circuit to control the correct timing of DTC migration . We also showed that this regulatory circuit is , at least in part , conserved in C . elegans and humans .
To identify genes that are important for the spatiotemporal regulation of DTC migration , we performed genetic screening for mutants defective in DTC migration , as described in the Materials and Methods , and isolated alleles tp5 and tk41 . A genetic complementation test showed that tp5 and tk41 were allelic , and that either allele failed to complement the previously identified mutation dpy-24 ( s71 ) ( dpy stands for dumpy , shorter than wild-type ) [14] . We mapped dpy-24 ( s71 ) to chromosome I , near stp124 , by sequence tag site ( STS ) mapping [15] ( Figure S1A ) . Three factor mapping using unc-40 and unc-75 and subsequent SNP mapping positioned dpy-24 ( s71 ) within the region between cosmids F45H11 and F37D6 . Cosmids covering this region were microinjected into the dpy-24 ( s71 ) mutant , and cosmid F25D7 rescued the DTC migration defect ( Figure S1B ) . The genomic DNA fragments corresponding to the 5 predicted open reading frames of F25D7 were individually amplified by long PCR and tested for their ability to rescue the dpy-24 ( s71 ) mutant and only F25D7 . 3 , which contained a single blmp-1 gene , had a rescue effect ( Figure S1B ) . In addition , F25D7 . 3 RNA interference ( RNAi ) phenocopied the dpy-24 ( s71 ) mutant ( Table 1 ) . These results demonstrated that F25D7 . 3 corresponded to the blmp-1 gene ( named for its sequence similarity to mouse Blimp-1 , see below ) [16] . The blmp-1 gene encodes a protein with 27% identity to mouse B lymphocyte-induced maturation protein 1 ( Blimp-1 ) and 26% identity to human positive regulatory domain I-binding factor ( PRDI-BF1 ) ( Figure S2 ) . Both Blimp-1 and PRDI-BF1 are thought to act predominantly as transcription repressors and are essential for the terminal differentiation of B and T cells [17] , [18] . As shown in Figure 2 , BLMP-1 , like Blimp-1 and PRDI-BF1 , is predicted to contain a positive regulatory ( PR ) domain , a nuclear localization signal ( NLS ) , and five Kruppel-type [ ( Cys ) 2- ( His ) 2] zinc fingers . The zinc fingers of both Blimp-1 and PRDI-BF1 have been shown to bind to target DNA and are essential for their transcriptional repression activities [19]–[21] . Alleles s71 , tk41 , and tp5 have , respectively , a non-sense mutation in codon 281 , 381 , or 434 , and are predicted to encode truncated proteins without zinc fingers ( Figure 2 and S2 ) . The deletion allele tm548 , which was isolated by a reverse genetic approach ( National Bioresource Project ) , has an 810 bp deletion , removing part of exon 3 and part of intron 3 ( Figure 2 and S2 ) and may result in a truncated BLMP-1 protein containing the first 254 amino acids of BLMP-1 and 17 amino acids encoded by the third intron . The four blmp-1 mutants , s71 , tk41 , tp5 , and tm548 , were found to have a similar set of defects , including a DTC migration abnormality ( shown for blmp-1 ( s71 ) in Figure 1 ) , a weak dumpy phenotype ( shown for blmp-1 ( s71 ) in Figure S3A ) , and a partially penetrant embryonic lethality ( shown for all four in Figure S3B ) . Wild-type blmp-1 genomic DNA rescued the dumpy phenotype and embryonic lethality of the blmp-1 ( s71 ) mutant ( Figure S3B ) , showing that these defects were caused by the loss of blmp-1 . The DTC migration patterns of these mutants were varied , but shared the common feature that the mutant DTCs had a shorter centrifugal phase I migration path and executed the dorsal turn at a point closer to the mid-body than wild-type DTCs ( Figure 1B–G , Table S1 ) , suggesting either slower movement and/or precocious initiation of the dorsal turn . However , we timed the movement of the DTCs during phase I migration and found that the blmp-1 mutant DTCs did not migrate significantly slower than the wild-type DTCs ( Table S2 ) . In addition , some DTCs migrated obliquely with respect to the dorsal-ventral axis until they reached the dorsal muscle ( Figure 1D ) ; such a migratory route is probably due to the simultaneous execution of centrifugal phase I and dorsal phase II migrations , support for a precocious execution of the dorsal turn at the time when phase I migration normally occurs . To examine whether the abnormal DTC migration pattern of the blmp-1 mutants was indeed caused by a precocious dorsal turn , we performed a time-course analysis of DTC migration in the wild-type and blmp-1 ( s71 ) mutant , using the division stages of the vulval precursor cell P6 . p as temporal developmental markers , as described previously [22] . P6 . p is generated in mid L1 , undergoes three rounds of cell division during L3 , and gives rise to eight descendants that constitute the vulva [23] . Figure 3A shows representative DIC images of wild-type ( a , b , e , f ) and blmp-1 ( s71 ) ( c , d g , h ) posterior gonadal arms in early L3 ( top panels ) and late L3 ( bottom panels ) . Figure 3B shows that , in the wild-type , the DTCs in more than 90% of worms underwent ventral-to-dorsal migration at the four-P6 . p cell stage and the DTCs in none made a dorsal turn before P6 . p cell division , whereas , in the s71 mutant , the anterior DTCs in 36% of worms and the posterior DTCs in 66% of worms had turned dorsalward before P6 . p divided . These results demonstrate that loss of blmp-1 causes a precocious initiation of DTC dorsal migration ( early L3 , rather than late L3 ) and that blmp-1 functions to prevent a precocious DTC dorsal turn . To determine the localization of BLMP-1 and explore its function , we raised polyclonal antibodies against bacterially expressed recombinant BLMP-1 ( Materials and Methods ) and used the affinity-purified antibodies to stain whole-mount animals . The results showed that BLMP-1 was localized in the nucleus and was detected in hypodermal , vulval , and intestinal cells ( Figure 4A ) , as well as DTCs ( Figure 4B ) . We next stained the mutant embryos with the blmp-1 mutations ( s71 , tm548 , tp5 and tk71 ) , which are loss-of-function recessive alleles by genetic tests ( Materials and Methods ) . Little or no signals were detected in these mutant embryos as shown in representative images in Figure 4C , demonstrating the specificity of the antibodies . Notably , BLMP-1 was seen in DTCs prior to the mid L3 larval stage ( two P6 . p-descent cells ) , but not during , or after , mid L3 stage , after the DTCs had undergone the dorsal turn ( Figure 4B ) . This result and the precocious dorsal turn phenotype of the blmp-1 mutant support a model in which BLMP-1 functions in DTCs before mid L3 stage to prevent these cells from undergoing a precocious dorsal turn . The localization of BLMP-1 in DTCs supports the model that BLMP-1 controls the timing of DTC dorsal migration in a cell-autonomous fashion . We then tested whether expression of blmp-1 cDNA in DTCs under the control of the lag-2 promoter Plag-2 is sufficient to rescue the DTC migration defect of the blmp-1 mutant . Plag-2 drives gene expression in DTCs , but not in body wall muscle or hypodermis , the structures on which DTCs migrate [24] . In blmp-1 mutants carrying the Plag-2 blmp-1 transgene , the percentage of worms with abnormal anterior or posterior DTC migration was reduced , respectively , from 82% to 20% and from 93% to 23% ( Figure 1G ) , similar levels to those seen when blmp-1 cDNA was expressed under the control of the blmp-1 endogenous promoter Pblmp-1 ( 40% or 19% for the anterior and posterior DTC , respectively; Figure 1G ) . This result further supports a cell-autonomous role of blmp-1 in DTC migration . Next , we tested whether blmp-1 may prevent dorsalward turning of DTCs by regulating a dorsal-ventral guidance system at the early larval stage . The dorsal migration of DTCs is regulated by the guidance receptors UNC-5 and UNC-40 ( a homolog of Deleted in Colorectal Cancer ) [7]–[9] , which drive DTCs to move away from the ventrally localized UNC-6 [10]–[11] . The observations that blmp-1 , unc-5 and unc-40 function cell-autonomously in the control of DTC migration [7] , [9] ( Figure 1G ) suggest that blmp-1 may prevent DTC precocious dorsalward turning by regulating unc-5 and/or unc-40 . unc-40 appears to be transcribed in the DTCs throughout their migration [7] , whereas unc-5 is transcriptionally up-regulated at the time when the dorsal turn is initiated [13] . Using the transgene Punc-5 ( 1 kb ) gfp , in which an approximately 1 kb sequence upstream of the unc-5 coding sequence was used to drive the GFP reporter , we confirmed this unc-5 expression pattern , i . e . unc-5 was expressed during and after , but not before , the DTC dorsal turn ( Figure S4 ) . Interestingly , this temporal pattern of unc-5 transcription is complementary to that of BLMP-1 expression in DTCs , as BLMP-1 was present in DTCs only before the dorsal turn ( Figure 4B ) . This raised the possibility that BLMP-1 inhibits unc-5 transcription and thus prevents DTCs from turning dorsalward . If this were the case , the correct timing of the disappearance of BLMP-1 from DTCs would alleviate this transcriptional inhibition and allow unc-5 transcription and the DTC dorsal turn . We tested this hypothesis by altering the temporal expression pattern of blmp-1 and examining their effects on unc-5 transcription . At the early L3 stage when the P6 . p cell has not yet divided , no wild-type worms contained DTCs that had turned dorsalward or showed any expression of the Punc-5 ( 1 kb ) ::gfp transgene ( Figure 5Ba ) , whereas , in some blmp-1 ( s71 ) mutants , DTCs at the same stage had performed the dorsal turn and displayed precocious unc-5 transcription ( Figure 5Bb ) . Thus , loss of blmp-1 causes precocious unc-5 expression , which coincides with the precocious dorsalward turning of DTCs . Next , we tested whether the blmp-1 precocious dorsal turn phenotype required unc-5 by examining and comparing the DTC dorsal migration patterns of the blmp-1 and unc-5 single mutants and the blmp-1; unc-5 double mutant . Approximately 48% anterior DTCs and 83% posterior DTCs failed to turn dorsalward in the unc-5 ( e53 ) mutant ( Table S3 ) . No precocious dorsal turn was observed in the blmp-1 ( s71 ) ; unc-5 double mutant , showing that the unc-5 ( e53 ) mutation blocked the precocious dorsal turn phenotype of the blmp-1 ( s71 ) mutant and that the blmp-1 precocious dorsal turn phenotype required unc-5 . Because BLMP-1 is present in the DTC before the dorsal turn , we examined the significance of blmp-1 down-regulation and its effect on the initiation of DTC dorsalward turning . To this end , we overexpressed blmp-1 using the transgene Pblmp-1::blmp-1::gfp , in which the fusion protein BLMP-1::GFP was expressed under the control of the promoter Pblmp-1 . In the resulting transgenic line , 10% of transgenic worms had DTCs that displayed normal BLMP-1::GFP down-regulation , underwent a normal dorsal turn ( Figure 5A , a , b ) , whereas 90% of transgenic worms showed persistent BLMP-1::GFP expression , even at the L4 stage , showing that BLMP-1::GFP was not appropriately down-regulated in these worms , and these worms showed no sign of dorsal turn ( Figure 5A , d , e ) . This retarded turn phenotype was in contrast to the precocious dorsal migration phenotype caused by loss of blmp-1 ( Figures 1C–G ) . Collectively , these results show that the BLMP-1 level is important regulation for DTC dorsal migration and that the timely disappearance of BLMP-1 allows the DTCs to turn dorsalward , hence switching their migration phase from centrifugal phase I migration to ventral-to-dorsal dorsal phase II migration . To test whether blmp-1 overexpression might repress unc-5 expression in DTCs and thus resulted in the retarded turning phenotype , we expressed the transgene Pblmp-1::blmp-1::gfp in worms carrying the Punc-5 ( 1 kb ) ::mCherry reporter . In the resulting transgenic line , 10% of transgenic worms had DTCs that displayed normal BLMP-1::GFP down-regulation , underwent a normal dorsal turn , and showed Punc-5 ( 1 kb ) ::mCherry expression ( Figure 5A , a–c ) , whereas 90% of transgenic worms showed persistent BLMP-1::GFP expression , and these worms showed no sign of dorsal turn or unc-5 expression ( Figure 5A , d–f ) . Thus , constitutive blmp-1 expression in the late larval stage represses unc-5 transcription and blocks the DTC dorsal turn . These results confirmed the causal relationship of the reciprocal patterns of BLMP-1 expression and unc-5 transcription in DTCs and support the model in which BLMP-1 inhibits unc-5 transcription and thus prevents DTCs from turning dorsalward during early larval development . The heterochronic genes daf-12 , dre-1 , and lin-29 function redundantly to specify the temporal identity of DTCs and prevent DTCs from undergoing retarded ventral-to-dorsal migration [4] . We tested the genetic interaction of blmp-1 with daf-12 , dre-1 , and lin-29 in the temporal control of DTC dorsal turn . For daf-12 , we used the null allele rh61rh411 . Because complete loss of dre-1 or blmp-1 caused lethality [4] , [25] and the lin-29 ( n546 ) ; blmp-1 ( s71 ) double mutant was very sick and could not be maintained as homozygote , we used viable alleles and/or RNAi for dre-1 , blmp-1 and lin-29 to analyze the genetic interactions of these genes . The precocious phenotype of the blmp-1 ( s71 ) mutant was partially suppressed by a single mutation of lin-29 , dre-1 , or daf-12 or combined mutations of any 2 or all 3 ( Table 1 ) . For example , 93% of blmp-1 ( s71 ) mutants had a precocious DTC migration defect , whereas 54% of the blmp-1 ( s71 ) ; lin-29 ( RNAi ) double mutants , 73% of the blmp-1 ( s71 ) ; dre-1 ( dh99 ) double mutants , and 31% of the blmp-1 ( s71 ) ;daf-12 ( rh61rh41 ) double mutants displayed a precocious turn defect . This suggests that the blmp-1 precocious phenotype may require the activity of lin-29 , dre-1 , or daf-12 , and that blmp-1 may function upstream of , or in parallel with , lin-29 , dre-1 , and daf-12 to prevent a precocious DTC dorsal turn during early larval development . Conversely , the blmp-1 mutation also partially suppressed the retarded dorsal migration of the double or triple mutants of lin-29 , dre-1 , and daf-12 ( Table 1 ) . For example , 98% of the dre-1 ( dh99 ) ; daf-12 ( rh61rh411 ) double mutants showed retarded DTC migration , whereas 12% , 73% , and 15% of the blmp-1 ( 71 ) ; dre-1 ( dh99 ) ; daf-12 ( rh61rh411 ) triple mutants showed , respectively , wild-type , precocious , or retarded DTC migration . This suggests that the retarded phenotype of the lin-29 , dre-1 and daf-12 double and triple mutants may require blmp-1 activity and that lin-29 , dre-1 , and daf-12 may function upstream of , or in parallel with , blmp-1 to prevent a delay in the DTC dorsal turn during the late larval stage . Thus , two distinct regulatory hierarchies in these heterochronic genes are likely employed to specify the temporal identities of DTCs at the early and late larval stages , and the switch from the “blmp-1-on” state to the “blmp-1-off” state during developmental progression may determine the timing of the DTC dorsal turn . The observation that mutation of daf-12 , dre-1 , or lin-29 partially suppressed the blmp-1-related precocious DTC dorsal migration defect ( Table 1 ) prompted us to examine whether blmp-1 negatively regulated the transcription of daf-12 , dre-1 , or lin-29 . Using chromatin immunoprecipitation coupled with high-throughput DNA sequencing ( ChIP-seq ) , the modENCODE Consortium has shown that BLMP-1 binds to the upstream sequence of lin-29 , but not that of daf-12 or dre-1 , at the early larval stage [26] . This suggested that BLMP-1 might negatively regulate lin-29 expression . To test this possibility , we analyzed and compared lin-29 transcription in the wild-type and blmp-1 mutants using the transcriptional reporter Plin-29::gfp , in which gfp was expressed under the control of the lin-29 promoter Plin-29 ( Figure 5Ca ) . We confirm that the transcription of lin-29 starts at approximately mid L3 stage and continues during L4 [4] , [27] . We found that knockdown of blmp-1 using RNAi resulted in precocious expression of Plin-29::gfp at the L2 stage ( Figure 5Cb , c ) , showing that blmp-1 represses lin-29 transcription at the L2 stage . To investigate the significance of this blmp-1-mediated lin-29 down-regulation during DTC phase I migration in L2 , we precociously expressed lin-29 under the control of the Plag-2 promoter using the Plag-2::lin-29 transgene . In the wild-type animals carrying this transgene , 27% and 9 . 5% of the anterior or posterior DTCs , respectively , underwent a precocious dorsal turn ( Figure 1G ) . This result shows the functional importance of blmp-1-mediated repression of lin-29 transcription in preventing precocious DTC dorsal migration during early larval development . The observation that the constitutive expression of blmp-1 in DTCs throughout larval development prevented them from performing the dorsal turn ( Figure 5A ) highlights the importance of BLMP-1 down-regulation in promoting the dorsal turn . Two observations suggested that daf-12 , lin-29 , and dre-1 might be responsible for BLMP-1 down-regulation . First , like worms constitutively expressing blmp-1 ( Figure 5Ad–f ) , the double and triple mutants of daf-12 , lin-29 , and dre-1 had a retarded DTC migration phenotype ( Table 1 ) . Second , loss of blmp-1 partially suppressed the retarded phenotype of the double and triple mutants ( Table 1 ) , showing that the defect in the double and triple mutants required blmp-1 activity . We therefore examined BLMP-1 levels in mutants defective in daf-12 , lin-29 and/or dre-1 using immunostaining with anti-BLMP-1 antibody . As mentioned above , in the wild-type control , BLMP-1 was not detected in DTCs at the L4 stage ( Figure 4B ) and a similar result was observed in the daf-12 , dre-1 , and lin-29 single mutants . In contrast , persistent BLMP-1 expression in L4 stage was seen in DTCs from the double mutants dre-1 ( dh99 ) ; lin-29 ( n546 ) ( 100% of 30 worms scored ) , dre-1 ( dh99 ) ; daf-12 ( rh61rh411 ) ( 20% of 60 scored ) , and lin-29 ( RNAi ) ; daf-12 ( rh61rh411 ) ( ∼5% of 106 scored ) ( examples shown in Figure 4D ) . The intensity of the persistent BLMP-1 signal was much weaker in the lin-29 ( RNAi ) ; daf-12 ( rh61rh411 ) double mutant than in the other double mutants and bleached too quickly to be photographed by our imaging system . The daf-12 ( rh61rh411 ) allele is molecular null , while lin-29 ( n546 ) and dre-1 ( dh99 ) are partial loss-of-function [4] , [5] . These results suggest that DRE-1 acts together with transcription factor LIN-29 or , to a lesser extent , with transcription factor DAF-12 for efficient down-regulation of BLMP-1 and correct timing of the control of DTC dorsal migration . To determine how BLMP-1 was down-regulated by lin-29 , daf-29 , and dre-1 , we used two gfp reporters in an in vivo expression assay to examine whether blmp-1 expression was repressed at the transcriptional level by transcription factors LIN-29 and DAF-12 and at the post-transcriptional level by the F-Box protein DRE-1 . We first generated a transcriptional reporter Pblmp-1::dgfp ( Figure 6A ) , in which dGFP ( destabilized GFP ) was controlled by the Pblmp-1 promoter . dGFP contains a PEST sequence and has a shorter half-life than normal GFP [28] and therefore allows sensitive detection of promoter activity . In animals carrying the integrated Pblmp-1::dgfp transgene , the DTCs of 75 . 6% of worms expressed GFP in the early and mid L3 stages , while only 4 . 3% expressed GFP in the late L3 and L4 stages ( Figure 6B and 6C ) , suggesting that blmp-1 transcription is strongly repressed during , and after , late L3 stage . Next , we tested whether daf-12 or lin-29 was responsible for blmp-1 transcriptional repression by examining Pblmp-1::dgfp transgene expression in the absence of daf-12 and/or lin-29 . As shown in Figure 6C , in early and mid L3 , loss of either daf-12 or lin-29 did not affect the percentage of worms with DTCs expressing dgfp . However , during the late L3 and L4 stages , loss of lin-29 , but not daf-12 , increased the percentage of worms with DTCs expressing dGFP in late L3 and L4 . For example , only 4 . 3% or 2 . 4% of wild-type or daf-12 mutant worms , respectively , had DTCs expressing dGFP , whereas 39 . 6% of lin-29 ( RNAi ) worms had dGFP-expressing DTCs . This result shows a differential requirement for lin-29 ( stronger ) and daf-12 ( weaker ) for blmp-1 transcriptional repression during , and after , the late L3 stage ( Figure 6C ) . In addition , 78 . 4% of the daf-12 ( rh61rh411 ) ; lin-29 ( RNAi ) double mutants had DTCs expressing dGFP ( Figure 6C ) . These results suggest that daf-12 plays a non-essential , but auxiliary , role in the repression of blmp-1 transcription . We also tested the involvement of dre-1 in blmp-1 transcriptional repression . No effect on the Pblmp-1::dgfp transcription level was observed when the dre-1 ( dh99 ) mutation was introduced into the wild-type or the daf-12mutant ( Figure 6C ) . Thus , dre-1 probably plays no role in blmp-1 transcriptional repression . Interestingly , blmp-1 seems to be required for its own expression . During early and mid L3 stages , approximately 75% of the wild-type worms had DTCs expressing dGFP from the Pblmp-1::dgfp transgene , but only 9% of the blmp-1 ( s71 ) worms had DTCs expressing dGFP . Previous genetic and biochemical data have shown that the F-Box protein DRE-1 functions in an SCF ubiquitin ligase complex , which contains CUL-1 ( a cullin scaffold protein ) , SKP-1 ( a SKP1-like adaptor that binds to the F-Box protein ) , and RBX-1 ( an RBX ring finger that bridges to the E2 ubiquitin conjugating enzyme ) , and is important for the temporal control of somatic and gonad development [4] and the timing of tail spike cell death [29] . This raised the possibility that DRE-1 may regulate BLMP-1 stability through ubiquitin-mediated proteolysis . Like dre-1 RNAi , RNAi for skp-1 , rbx-1 , or cul-1 caused a retarded DTC migration phenotype in the daf-12 ( rh61rh411 ) single mutant [4] ( Table S4 ) , consistent with a model in which DRE-1 targets protein ( s ) for proteolysis in an SCF ubiquitin ligase complex during the temporal regulation of the DTC dorsal turn . Using the transgene Plag-2::gfp::blmp-1 , in which BLMP-1 was tagged with GFP and expressed under the control of the Plag-2 promoter ( Figure 7A ) , we next examined whether DRE-1 destabilized BLMP-1 . Two independent transgenic lines carrying an extrachromosomal transgene array were generated . In line 1 , approximately 27% of worms expressed GFP in DTCs during early L3 and the percentage decreased to 16 . 9% during mid L3 to L4 , showing approximately 38% down-regulation , and a similar level of down-regulation ( 35 . 5% ) was observed in line 2 ( Figure 7Aa , b and C ) . In contrast , no down-regulation was observed in the control line carrying the integrated transgene qIs56[Plag-2::gfp] , in which gfp was expressed under the control of the Plag-2 promoter ( Figure 7Ba , b and C ) . These results show that BLMP-1 levels drop significantly when entering the mid L3 stage . Although the difference may be due to differences in sensitivity of the gfp reporters , given the previous result that blmp-1 transcriptional repression was detected in late , but not mid , L3 stage , this suggests that BLMP-1 degradation occurs approximately 3 hours earlier than transcriptional repression . Next , we examined whether dre-1 was required for this decrease in GFP::BLMP-1 fusion protein levels . In two independent transgenic lines of the dre-1 mutant , the percentage of worms with DTCs expressing GFP::BLMP-1 during the early L3 stage was similar to that seen during the mid L3-to-L4 stage ( Figure 7Ac , d , and C ) , showing that dre-1 is required for GFP::BLMP-1 degradation . Since loss of dre-1 completely blocked the decrease in GFP::BLMP-1 levels , it is unlikely that any other gene acts in a redundant fashion with dre-1 to regulate BLMP-1 stability . The F-box protein of an SCF ligase complex binds to substrates and targets them for ubiquitin-mediated proteolysis [30] . To test the idea that BLMP-1 might be the target of DRE-1 in the SCFDRE-1 ligase complex , we examined whether DRE-1 and BLMP-1 physically interacted by co-immunoprecipitation in mammalian cell cultures . When Myc-tagged dre-1 and HA-tagged blmp-1 were transfected into HEK293T cells , then lysates were subjected to immunoprecipitation with anti-Myc antibody and Western blotting with anti-HA antibody , BLMP-1 was co-immunoprecipitated with DRE-1 , while no signal was seen in the singly transfected controls ( Figure 8A ) . The reciprocal experiment using immunoprecipitation with anti-HA antibody and Western blotting with anti-Myc antibody showed that DRE-1 was co-immunoprecipitated with BLMP-1 ( Figure 8B ) . These results demonstrate that DRE-1 binds to BLMP-1 in HEK293T cells . We next examined whether FBXO11 and PRDI-BF1 , the respective human orthologs of DRE-1 and BLMP-1 , also associate in HEK293T cells and found that Myc-tagged FBXO11 was co-immunoprecipitated with HA-tagged PRDI-BF1 in lysate of cells co-transfected with both , but not in lysate of cells transfected with only Myc-tagged FBXO11 ( Figure 8C ) . This result shows association of FBXO11 and PRDI-BF1 in human cell cultures . In addition , HA-tagged PRDI-BF1 also pulled down endogenous CUL1 in co-immunoprecipitation experiments in HEK293T cells ( Figure 8C ) , demonstrating the association of FBXO11 , BLMP-1 , and CUL1 in an SCF complex and suggesting that FBXO11 may regulate PRDI-BF1 stability through ubiquitin-mediated proteolysis . These results demonstrate a conserved interaction between DRE-1/FBXO11 and BLMP-1/PRDI-BF1 in both humans and C . elegans . The regulation of BLMP-1/PRDI-BF1 stability by DRE-1/FBXO11 in an SCF ubiquitin ligase complex may also be conserved in evolution .
In this study , we identified a conserved transcription factor , BLMP-1 , as an essential component of the heterochronic hierarchy during gonad development , and provided evidence that BLMP-1 levels are critical for the timing of DTC dorsal migration during gonadogenesis . BLMP-1 was present in DTCs only before the dorsal turn and disappeared when the DTC was about to make the dorsal turn . Loss of blmp-1 resulted in precocious unc-5 transcription and DTC dorsal migration , whereas constitutive expression of blmp-1 delayed both events . These data show that blmp-1 controls the temporal identity of DTCs by preventing them from undergoing a precocious dorsal turn . In addition , our results provide a molecular mechanism by which daf-12 , dre-1 , and lin-29 promote the correct timing of DTC dorsal turn by timely down-regulation of blmp-1 . In this model , lin-29 and daf-12 repress blmp-1 transcription , which abolishes the synthesis of blmp-1 mRNA , while dre-1 mediates BLMP-1 degradation . Together , these two negative regulatory systems result in the efficient elimination of blmp-1 activity , and thus alleviate the repression of the DTC dorsal turn . Interestingly , daf-12 , dre-1 , and lin-29 appeared to contribute to BLMP-1 down-regulation to different extents . For example , in the dre-1; lin-29 , dre-1; daf-12 , and lin-29; daf-12 double mutants , 100% , 20% , or 5% of worms , respectively , showed persistent BLMP-1 expression beyond late L3 in the immunostaining experiment . This suggests that , of these three genes , loss of dre-1 has the strongest effect on BLMP-1 down-regulation and loss of daf-12 has the weakest effect . Using transcriptional and translational gfp reporters , it appeared that the DRE-1-dependent protein degradation process occurred at the mid L3 stage , slightly earlier than the transcriptional repression mediated by lin-29 and daf-12 ( late L3 stage ) , although this difference could be due to different sensitivities of the gfp reporters . The notion that lin-29 is more significant than daf-12 in the repression of blmp-1 was further supported by the observation that , using the Pblmp-1::dgfp reporter , loss of lin-29 increased blmp-1 transcription in the late L3 to L4 stages , but loss of daf-12 had no detectable effect on blmp-1 expression ( Figure 6C ) . Further experiments are necessary to test whether LIN-29 may inhibit blmp-1 transcription by directly binding to the blmp-1 genomic sequence . It is intriguing that only 20% of the dre-1; daf-12 double mutants had DTCs with detectable persistent BLMP-1 , whereas 98% of these double mutants showed the retarded phenotype ( Table 1 ) . It is possible that , in the mutant , BLMP-1 persists at a low level beyond the detection limit of our system , but sufficient to block lin-29 transcription and therefore abolish the DTC dorsal turn in the absence of daf-12 . On the other hand , the retarded DTC migration phenotype of the lin-29; daf-12 double mutant may not be caused solely by persistent BLMP-1 at the undetectable low level , but also by the loss of both transcription activators lin-29 and daf-12 , which function in a redundant fashion in the transcriptional activation of unc-5 ( see below and Figure S5 ) . As shown in Figure 5 A , BLMP-1::GFP in worms carrying Pblmp-1::blmp-1::gfp was present and persisted beyond late L3 stage and the transgenic worms frequently showed a retarded DTC migration defect . A similar result was observed in worms carrying Plag-2::blmp-1::gfp ( Huang and Wu , unpublished results ) . Interestingly , wild-type worms carrying Plag-2::blmp-1 had normal DTC migration ( Figure 1G ) , suggesting that BLMP-1 in the transgenic worms was properly degraded beyond L3 stage . These data and the observation that GFP::BLMP-1 expressed from the transgene Plag-2::gfp::blmp-1 was degraded suggest that GFP tag at N-terminus or C-terminus make a difference in GFP fusion BLMP-1 degradation . Using ChIP-seq , the modENCODE Consortium showed that BLMP-1 binds to the upstream sequence of unc-5 in vivo [26] , suggesting that it might inhibit unc-5 expression by binding to the regulatory region of the gene . In addition , using the Punc-5 ( 4 . 6 kb ) ::gfp transgene , we found that inactivation of either daf-12 or lin-29 slightly reduced unc-5 transcription , while loss of both genes completely abolished it ( Figure S5 ) . These results are consistent with the notion that the transcription factors DAF-12 and LIN-29 act together in a redundant fashion to activate unc-5 transcription and promote the DTC dorsal turn . The consensus DAF-12 binding sequence can also be identified in the unc-5 promoter ( T . F . Huang and Y . C . Wu , unpublished results ) , raising the possibility that DAF-12 may directly bind to unc-5 for its transcriptional activation . The DTC dorsal turn can be characterized by a specific transition in which the DTC moves irreversibly from phase I centrifugal migration into phase II dorsal migration . Phase I centrifugal migration occurs in the “blmp-1-on” state ( high BLMP-1 and low UNC-5 levels ) and phase II dorsal migration occurs in the“blmp-1-off” state ( low BLMP-1 and high UNC-5 levels ) . A double-negative feedback loop , in which two genes mutually repress each other directly or indirectly , is commonly utilized to generate switch-like bistable responses during the progression of cellular and developmental processes [31] . Our data suggest that lin-29 and blmp-1 act in a double negative feedback loop that helps maintain DTCs in one of the bistable “blmp-1-on” and “blmp-1-off” states . On the basis of our results , we propose a molecular model for the switch-like process of the DTC dorsal turn . In L2 and early L3 , BLMP-1 is expressed and represses lin-29 transcription ( Figure 9 ) . The double-negative feedback loop keeps DTCs in the “blmp-1-on” state , thus repressing DTC dorsal migration . In late L2 , the hormones known as dafachronic acids ( DA ) are generated from dietary cholesterol and bind to the ligand binding domain of DAF-12 [32] . The DAF-12-DA complex promotes the L2-to-L3 transition [32] , but is insufficient to repress blmp-1 transcription . During early to mid L3 , dre-1 expression is initiated [4] , which reduces BLMP-1 levels , probably through ubiquitin-mediated proteolysis . The decrease in BLMP-1 levels shifts the steady state toward low BLMP-1 levels and high LIN-29 levels ( blmp-1 off ) . In late L3 , accumulation of LIN-29 locks the DTCs in the “blmp-1-off” state through the negative feedback loop , thus allowing DTCs to switch to dorsal migration . This gene regulatory circuit integrates the temporal and spatial signals and coordinates with overall development of the organism to direct DTC dorsal migration during organogenesis . Expression of lin-29 is negatively regulated by lin-42 [33] . LIN-42 is similar to the Period ( Per ) family of circadian rhythm proteins and functions as a member of the heterochronic pathway , regulating temporal cell identities [33] , [34] . However , how lin-42 regulates the expression of lin-29 is not clear . lin-42 also genetically interacts with daf-12 . During development , when conditions are unfavorable , due to starvation , crowding or high temperature , daf-12 promotes diapauses and formation of duaer larvae [5] , [35] , [36] . Previous studies show that daf-12 represses expression of lin-42 during dauer formation [37] and that lin-42 antagonizes the dauer-inducing signal from ligand-free DAF-12 [38] . However , whether daf-12 and lin-42 also regulate each other in the timing of DTC dorsalward turn during gonadaogenesis needs to be examined in the future . In addition to ventral-to-dorsal migration , blmp-1 affects DTC migration along the AP axis . As shown in Figure 1D , some DTCs migrated obliquely with respect to the dorsal-ventral axis in blmp-1 mutants , suggesting simultaneous execution of centrifugal phase I and dorsal phase II movement . This result implies that the mutual exclusion of dorsalward and centrifugal migration in wild type animals was impaired in the blmp-1 mutants . Moreover , as shown in Figure 1E and 1F and Table S1 , the reversal of migration direction was frequently observed in the centripetal phase III migration of blmp-1 mutants . Expression of unc-5 by the lag-2 promoter from transgene Plag-2::unc-5 resulted in DTC migration defects , including the reversal of the migration direction during the centripetal phase III migration ( Huang , T . F . and Wu , Y . C . , unpublished results ) , similar to those seen in blmp-1 mutants ( Table S1 ) . Further experiments are necessary to test whether the phase III DTC migration defect of blmp-1 mutants may be caused by an accumulative high level of unc-5 in the DTCs . Previous studies have shown that blmp-1 mutants have a dumpy phenotype , a weak cuticle sensitive to oxidative stress , and defective adult alae , showing that blmp-1 is important for normal body length , oxidative stress resistance , and alae formation [39] . In addition , blmp-1 is important for pharynx and [40] male tail morphogenesis [41] . Under Nomarski optics , we confirmed the blmp-1 alae defect , as 40% of blmp-1 ( s71 ) mutants had incomplete alae and the rest had no alae in the adult stage ( Figure S3C ) . The alae are continuous ridged cuticular structures synthesized by the lateral epidermal seam cells at the larval to adult ( L/A ) transition and serve as a specific marker of adult fate . The seam cells undergo asymmetric cell divisions at each larval stage and exit the cell cycle at the L/A transition . The blmp-1 ( s71 ) mutant was found to have a normal number of seam cells at the late L4 and early adult stage ( Figure S3D ) , indicating that seam cell division was normal . These results and the abnormal alae phenotype suggest that blmp-1 is essential for the adult , but not the larval , fate of the epidermal seam cells . The dre-1 mutant shows precocious formation of adult alae [4] . We therefore tested the genetic interaction between dre-1 and blmp-1 in the seam cell heterochronic hierarchy . About 8% of dre-1 ( dh99 ) mutants had adult alae at the early L4 stage ( Figure S3D ) , confirming a role of dre-1 in preventing the adult fate of the seam cell [4] . At the adult stage , 35% of dre-1 ( dh99 ) mutants had incomplete alae ( Figure S3Cc and D ) . Interestingly , the blmp-1; dre-1 double mutant had a weaker defect in alae formation than the blmp-1 single mutant , as 23% of double mutants and 60% of blmp-1 single mutants had no alae ( Figure S3C and D ) . This result shows that dre-1 partially suppresses the blmp-1 alae abnormality and , therefore , blmp-1 may genetically act upstream of , or in parallel to , dre-1 in the specification of the adult seam cell fate , at least in terms of alae formation . blmp-1 ( tm548 or s71 ) mutants have a weak uncoordinated movement phenotype [39] . Although blmp-1 expression has been previously observed in neurons using reporter transgene assays [26] , we failed to detect BLMP-1 in neurons . It is possible that our anti-BLMP-1antibody system was not sensitive enough to detect a low amount of protein in neurons . Alternatively , the neuronal expression of blmp-1 revealed by the transgene may be ectopic and not reflect the endogenous expression pattern . Like the blmp-1 mutations , lin-42 RNAi causes both a precocious DTC dorsal turn [33] and precocious lin-29 expression in L2 , one stage earlier than in the wild-type [33] . However , the lin-42 ( RNAi ) and blmp-1 mutations induce precocious dorsal migration at different larval stages , i . e . in L2 for lin-42 ( RNAi ) [33] and early L3 in the case of blmp-1 ( Figure 3 ) . Similar to loss of blmp-1 , precocious expression of lin-29 under the control of the lag-2 promoter caused the DTCs to undergo a precocious dorsal turn in early L3 , but not L2 . These results suggest that lin-42 regulates most , if not all , of the genes necessary for the DTC dorsal turn , while blmp-1 regulates the temporal expression of only a subset , including lin-29 . DRE-1 has two human orthologs FBXO10 and FBXO11 , which are localized in the cytoplasm and nucleus , respectively [29] . FBXO10 and DRE-1 mediate , respectively , the degradation of anti-apoptotic protein Bcl2 or CED-9 to promote the death of a cell [29] . Recently , FBXO 11 has been shown to target the pro-oncogene product BCL6 for degradation and to be inactivated in diffuse large B cell lymphomas [42] . BCL6 , a zinc finger transcription factor , regulates the transcription of a variety of genes involved in B cell development , differentiation , and activation [43] , [44] and is overexpressed in the majority of patients with aggressive diffuse large B cell lymphoma [45] . Using the Clustal Omega multiple sequence alignment website http://www . ebi . ac . uk/Tools/msa/clustalo/ , we found that Bcl6 shares sequence similarity with BLMP-1 ( 20% ) and PRDI-BF1 ( 24% ) . These observations and our result showing that DRE-1/FBXO11 and BLMP-1/PRDI-BF1 are associated in human cell cultures indicate that DRE-1 , FBXO10 and FBXO11 target different proteins for degradation in different cellular or developmental contexts , i . e . DRE-1 targets BLMP-1 and CED-9 , FBXO10 targets Bcl2 and FBXO11 targets PRDF-BF1 and Bcl6 . It will be interesting to determine how target specification is regulated .
C . elegans strains were cultured at 20°C on NGM agar inoculated with E . coli OP50 , as described previously [46] . The N2 Bristol strain was used as the reference wild-type strain . The mutations used were as follows: LGI , blmp-1 ( s71 , tk41 , tm548 , tp5 ) , lin-29 ( n546 ) ; LGIII , unc-119 ( ed3 ) ; LGV , dre-1 ( dh99 ) ; LGX daf-12 ( rh61rh411 ) . The blmp-1 ( tm548 ) mutant was generated and provided by the National Bioresource Project in Japan . Strains CB4856 and RW7000 were used for single-nucleotide polymorphism ( SNP ) mapping [15] , [47] . Strain OS1841 carrying the transgene Plin-29::GFP was kindly provided by S . Shaham [48] . To test whether the tk41 , tm548 , s71 or tp5 mutation is recessive or dominant , homozygous mutant hermaphrodites were crossed to wild-type males carrying the integrated sur-5::gfp transgene . The cross-progeny hermaphrodites carrying the sur-5::gfp transgene were scored for the dumpy ( Dpy ) and DTC migration phenotypes using Nomarski microscopy . In these crosses , all heterozygous cross-progeny hermaphrodites were wild-type , showing that these mutations are recessive . In complementation tests , the tk41 , tm548 or tp5 homozygous hermaphrodites were crossed to s71 males carrying the sur-5::gfp transgene , the cross-progeny hermaphrodites with the transgene were scored for the Dpy and DTC migration phenotypes using Nomarski microscopy . In these crosses , all of the mutations tested failed to complement s71 . We positioned blmp-1 ( s71 ) on the basis of the Dpy phenotype within the region between snpF45H11 and snpY106g6h , which correspond to cosmids F45H11 and F37D6 , respectively , using three-factor mapping with unc-40 and unc-75 and subsequent SNP mapping as previously described [47] . The following cross demonstrates that blmp-1 is located between unc-40 and snpY106g6h: from a blmp-1 ( s71 ) unc-75/++ ( CB4856 ) hermaphrodite 37/37 Dpy non-Unc recombinant progeny segregated neither snpY106g6h nor snpF59C6 , and 6/6 nonDpy Unc recombinant progeny segregated snpY106g6h and snpF59C6 . From an unc-40 blmp-1 ( s71 ) /++ ( CB4856 ) hermaphrodite 85/91 nonDpy Unc recombinant progeny segregated snpY106g6h and snpF59C6 , 1/91 recombinant progeny segregated snpY106g6h and 5/91 recombinant progeny segregated neither snpY106g6h nor snpF59C6 . The following cross demonstrates that blmp-1 is located right of snpF45H11: from an unc-40 blmp-1 ( s71 ) /CB4856 hermaphrodite 3/171 Dpy nonUnc recombinant progeny segregated snpF45H11 . We performed genetic screening for mutants defective in DTC migration under a dissecting microscope , as described in Nishiwaki , 1999 , and isolated the mutation tk41 . In an independent screen for mutants with a DTC migration defect using Nomarski microscopy , we isolated the mutation tp5 . The DTC migratory patterns of the wild-type and mutants were determined by observing the shape of the gonadal arms in the adult stage . Worms were mounted on a 4% agar pad containing 20 mM NaN3 and observed on a microscope with Nomarski optics . For the time course analysis of DTC migration , wild-type and blmp-1 ( s71 ) mutants at the indicated time points were collected and scored under the Nomarski microscope . To obtain the 5′ end of blmp-1 cDNA , the first three exons were amplified by RT-PCR using a forward primer corresponding to the SL1 sequence and the reverse primer blmp-1_exon_3/r ( see Table S5 for detailed information ) . The PCR product was fused with the blmp-1 cDNA fragment from the yk487b7 clone by fusion PCR [49] , and the resulting product was cloned into the vector pGEM-T Easy ( Promega ) to generate pYW687 , containing the full-length blmp-1 cDNA . To construct Plag-2::blmp-1 ( pYW798 ) , blmp-1 cDNA was PCR-amplified from pYW687 using primers DPY-24-KpnI/f and DPY-24-KpnI/r and the product inserted into pPD49_26/Plag-2 via the KpnI site . Two unc-5 transcriptional gfp constructs were generated . The Punc-5 ( 4 . 6 kb ) DNA fragment was PCR-amplified from C . elegans genomic DNA using primers Punc-5_4 . 6 kb/f and Punc-5_4 . 6 kb/r and inserted into the vector pGEM-T Easy . The Punc-5 ( 4 . 6 kb ) fragment of the resulting construct was then inserted into the vector pPD95 . 77 ( A . Fire ) via SphI/SalI sites to generate Punc-5 ( 4 . 6 kb ) ::gfp . A similar approach was used to generate Punc-5 ( 1 kb ) ::gfp . The Punc-5 ( 1 kb ) fragment was PCR-amplified from genomic DNA using primers Punc-5_1 kb/f and Punc-5_1 kb/r and cloned into the vector pGEM-T Easy , then the Punc-5 ( 1 kb ) fragment was excised from the resulting plasmid and inserted into the vector pPD95 . 75 ( A . Fire ) via the XmaI site . To generate Punc-5 ( 1 kb ) ::mCherry , the gfp fragment of the plasmid Punc-5 ( 1 kb ) ::gfp was replaced by mCherry cDNA . The Punc-5 ( 1 kb ) fragment was expected to contain the regulatory region for proper unc-5 function in guiding DTC migration , as the Punc-5 ( 1 kb ) ::unc-5::gfp plasmid is sufficient to rescue the DTC migration defect of the unc-5 mutant ( our unpublished data ) . The Punc-5 ( 1 kb ) ::unc-5::gfp plasmid was generated by fusion PCR by fusing two PCR-amplified products corresponding to Punc-5 ( 1 kb ) ::unc-5 ( cDNA ) and gfp::unc-54 3′ UTR from plasmid pU5HA ( J . Culotti ) or pPD95 . 75 , respectively . The Pblmp-1::dgfp fragment was generated by fusion PCR by fusing two PCR products corresponding to Pblmp-1 and the region containing dgfp and the unc-54 3′ UTR . Pblmp-1 was PCR-amplified from genomic DNA using primers D24-5end5kb and d24Pgfp/r , and the region containing dgfp and unc-54 3′UTR was PCR-amplified from pPD95 . 75PEST ( pYW807 ) , which contains dgfp , using primers GFP/f and D . The two PCR products were then mixed and fused by fusion PCR using primers d24-5kbf-nest and D′ . The Pblmp-1::blmp-1::gfp fragment was generated by fusion PCR by fusing two PCR products corresponding to blmp-1 genomic DNA and the region containing gfp and unc-54 3′ UTR from the pPD95 . 75 plasmid [50] . blmp-1 genomic DNA was PCR-amplified from wild-type genomic DNA using primers D24-5end5kb and d24gfp/r , and the region containing gfp and the unc-54 3′ UTR was PCR-amplified from the pPD95 . 75 plasmid using primers GFP/f and D , then the two PCR products were fused by fusion PCR using primers d24-5kbf-nest and D′ . The primers used in this work are listed in Table S6 . Transgenic worms were generated by microinjection of the indicated plasmid , as described previously [51] . For genetic rescue experiments , cosmids and plasmids were microinjected into the indicated strains and the indicated phenotype of the stably transmitting lines scored using DIC optics . Pblmp-1::blmp-1::gfp ( 20 ng/µl ) was co-injected with the unc-119 rescuing plasmid ( 100 ng/µl ) [52] into the unc-119 ( ed3 ) mutant to generate tpEx49 transgenic worms . For the unc-5 transcription assay , Punc-5 ( 4 . 6 kb ) ::gfp , Punc-5 ( 1 kb ) ::gfp , or Punc-5 ( 1 kb ) ::mCherry ( 20 ng/µl ) was co-injected with the marker Pmyo-2::gfp ( 2 ng/µl ) , which expresses gfp in the pharynx [26] , [53] , as described previously [51] . To determine how blmp-1 was down-regulated during larval development , the indicated plasmids ( 20 ng/µl ) were co-injected with Pmyo-2::gfp ( 2 ng/µl ) into wild-type worms to generate transgenic worms , and the resulting transgenes were crossed to the indicated heterochronic mutants . For the blmp-1 cell autonomy assay , Plag-2::blmp-1 or Pblmp-1::blmp-1 ( 20 ng/µl ) was co-injected with Pmyo-2::gfp ( 2 ng/µl ) into wild-type worms to generate worms carrying the respective transgenes , then the blmp-1 ( s71 ) mutation was introduced into these transgenic worms by crossing to generate the blmp-1 ( s71 ) mutant carrying either the Plag-2::blmp-1 or Pblmp-1::blmp-1 transgene . The transgenes used in this work were listed in Table S5 . The blmp-1 cDNA fragment from the plasmid pYW687 was digested with EcoRI and cloned into the vectors pGEX5X-1 ( GE Healthcare ) and pRSET B ( Invitrogen ) at their EcoR I sites to generate the respective constructs pYW802 and pYW691 . The GST-BLMP-1 and HIS-BLMP-1 fusion proteins were present in inclusion bodies and were purified using standard methods [54] . Polyclonal rabbit antibodies against GST-BLMP-1 were generated and affinity-purified using 6His-tagged BLMP-1 as described previously [55] and the purified antibodies recognized GST-BLMP-1 , but not GST , on a Western blot , indicating their BLMP-1 specificity . The immunostaining method was adopted from a previously published protocol [56] , with slight modification . The animals were fixed for 1 h at 4°C in 80 mM KCl , 20 mM NaCl , 10 mM EGTA , 5 mM spermidine , 0 . 03 mM PIPES , 25% methanol , and 2% formaldehyde , then incubated overnight at 4°C with a 1∶100 dilution of the purified anti-BLMP-1 antibodies in PBS , 0 . 2% BSA , 0 . 5% Triton X-100 ( PBT ) , then for 2 h at room temperature with rhodamine red-X ( RRX ) -conjugated donkey-anti-rabbit IgG antibodies ( Jackson ImmunoResearch Laboratories; 1∶100 in PBT ) . For immunostaining of the lin-29 ( RNAi ) ;daf-12 ( rh61rh411 ) mutants , the worms were mounted on a gelatin-chromic potassium sulfate-subbed slide and immunostained with anti-BLMP-1 antibodies , as described previously [57] . Worms were freeze-cracked and fixed in 95% ethanol for 10 min , then in 2% paraformaldehyde for 10 min , then incubated sequentially overnight at room temperature with purified anti-BLMP-1 antibodies ( 1∶1000 in PBT ) , followed by RRX-conjugated donkey-anti-rabbit IgG antibodies ( Jackson ImmunoResearch Laboratories; 1∶1000 in PBT ) . They were then mounted with 2 µl of DABCO anti-bleaching reagent ( Fluka ) and 1 µl of DAPI ( 0 . 5 µg/ml ) and observed using a Zeiss Axioplan 2 microscope equipped with a digital camera ( AxioCam; Carl Zeiss , Inc . ) . To knock down blmp-1 , skr-1 , cul-1 , rbx-1 , dre-1 , or lin-29 , RNA interference ( RNAi ) was performed either by feeding worms with bacteria expressing the double stranded RNA for the indicated genes or by injecting the double stranded RNA for the indicated genes as described previously [58] , [59] . To generate the lin-29 RNAi construct , the region corresponding to exons 3–9 of lin-29 was PCR-amplified from the yk1430g04 plasmid , which contains lin-29 cDNA ( Y . Kohara , Japan ) , and cloned into the L4440 vector . The blmp-1 , srk-1 , cul-1 , and rbx-1 RNAi constructs were obtained from the Ahringer RNAi library . HEK293T cells were grown in Dulbecco's modified Eagle's medium ( Thermo Inc ) containing 10% fetal bovine serum , 100 U/ml of penicillin , and 100 µg/ml of streptomycin ( all from Life technologies ) and were transfected with the indicated plasmids using Lipofectamine 2000 ( Invitrogen ) . MG132 ( 20 µM ) was added to the medium 2 h before the cells were harvested to block protein degradation by the proteasome . Cells were harvested using pre-chilled PBS and lysed using 1% NP-40 lysis buffer with protease inhibitor ( Roche ) . HA-tagged or Myc-tagged fusion proteins were immunoprecipitated by incubation overnight at 4°C with anti-HA or anti-Myc agarose beads ( Sigma ) and bound protein identified by Western blotting using antibodies against HA ( Covance ) , Myc ( BD Biosciences ) , or CUL1 ( Zymed ) . Antibodies against tubulin ( Sigma ) or GAPDH ( Abnova ) were used on the loading controls .
|
The migratory path of DTCs determines the shape of the C . elegans gonad . How the spatiotemporal migration pattern is regulated is not clear . We identified a conserved transcription factor BLMP-1 as a central component of a gene regulatory circuit required for the spatiotemporal control of DTC migration . BLMP-1 levels regulate the timing of the DTC dorsal turn , as high levels delay the turn and low levels result in an early turn . We identify and characterize upstream regulators that control BLMP-1 levels . These regulators function in two ways , i . e . by destabilization of BLMP-1 through ubiquitin-mediated proteolysis and by transcriptional repression of the blmp-1 gene to down-regulate BLMP-1 . Interestingly , blmp-1 also negatively controls these regulators . Our data suggest that a dietary signal input acts together with a double-negative feedback loop to switch DTCs from the “blmp-1-on” to the “blmp-1-off” state , promoting their dorsal turn . Furthermore , we show that some protein interactions in the circuit are conserved in C . elegans and humans . Our work defines a novel function of the conserved blmp-1 gene in the temporal control of cell migration , and establishes a gene regulatory circuit that integrates the temporal and spatial inputs to direct cell migration during organogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"and",
"life",
"sciences",
"developmental",
"biology"
] |
2014
|
BLMP-1/Blimp-1 Regulates the Spatiotemporal Cell Migration Pattern in C. elegans
|
During meiotic recombination , induced double-strand breaks ( DSBs ) are processed into crossovers ( COs ) and non-COs ( NCO ) ; the former are required for proper chromosome segregation and fertility . DNA synthesis is essential in current models of meiotic recombination pathways and includes only leading strand DNA synthesis , but few genes crucial for DNA synthesis have been tested genetically for their functions in meiosis . Furthermore , lagging strand synthesis has been assumed to be unnecessary . Here we show that the Arabidopsis thaliana DNA REPLICATION FACTOR C1 ( RFC1 ) important for lagging strand synthesis is necessary for fertility , meiotic bivalent formation , and homolog segregation . Loss of meiotic RFC1 function caused abnormal meiotic chromosome association and other cytological defects; genetic analyses with other meiotic mutations indicate that RFC1 acts in the MSH4-dependent interference-sensitive pathway for CO formation . In a rfc1 mutant , residual pollen viability is MUS81-dependent and COs exhibit essentially no interference , indicating that these COs form via the MUS81-dependent interference-insensitive pathway . We hypothesize that lagging strand DNA synthesis is important for the formation of double Holliday junctions , but not alternative recombination intermediates . That RFC1 is found in divergent eukaryotes suggests a previously unrecognized and highly conserved role for DNA synthesis in discriminating between recombination pathways .
Meiosis reduces the genomic complement of the cell by half in preparation for fertilization and is essential for sexual reproduction . Recombination is a key event in meiotic prophase I and is important for homolog pairing , bivalent formation and proper homolog segregation [1] , [2] . According to the double-strand break repair ( DSBR ) model [3] ( Figure 1A ) , largely based on molecular studies in yeast and supported by genetic analyses in other organisms [1] , [4] , meiotic recombination is initiated by SPO11-catalyzed DSBs [5] , which are processed to yield 3′ single-strand DNA ( ssDNA ) overhangs and stabilized by replication protein A ( RPA ) [6] . RPA is displaced by RecA-like proteins RAD51 and DMC1 to form a nucleoprotein filament , which searches for a homologous template and promotes strand invasion to form a joint molecule in a process called single end invasion ( SEI ) , thereby providing a 3′ end as a primer for DNA synthesis in the nascent D loop [7] . Arabidopsis thaliana has five RPA homologs , one of them is required for meiotic recombination; unlike the yeast RPA , it likely functions downstream of RAD51 [8] . Subsequently , second DSB end capture results in a double Holliday Junction ( dHJ ) that is resolved to yield crossovers ( COs ) and non-crossovers ( NCOs ) [9] , [10] . Alternatively , the invading strand dissociates from the D-loop and re-anneal to the other DSB end to form a NCO via synthesis dependent strand annealing ( SDSA ) [11] . The formation of both COs and NCOs requires DNA synthesis , but few factors for DNA synthesis have been functionally analyzed in meiotic recombination . In DNA replication , continuous 5′ to 3′ leading strand synthesis requires DNA polymerase ( Pol ) α-primase to synthesize a short primer and Pol ε , which can sometimes be replaced by Pol δ [12] , [13] . Lagging strand synthesis is more complex and requires synthesis and ligation of a series of “Okazaki fragments” , which are initiated from short RNA-DNA primers produced by Pol α-primase [14] , [15] . The primer is recognized by the RFC complex , which facilitates the dissociation of Pol α-primase and loading of Proliferating Cell Nuclear Antigen ( PCNA ) . PCNA then recruits Pol δ to the primer-template duplex in a process called ‘polymerase switching’ . The discontinuous Okazaki fragments are then processed by DNA endonucleases ( such as DNA2 and Fen1 ) and ligated to complete lagging strand synthesis [14] . In addition to the ATP dependent PCNA loading , RFCs was shown to greatly stimulate Fen1 activity [16] , and to participate in the NER ( nucleotide excision repair ) process [17] . Because many DNA synthesis genes are essential for mitotic growth , null mutants are lethal , precluding the analysis of their meiotic defects . Consequently , the role of DNA synthesis in meiotic recombination has been tested genetically only in a few instances . A four amino acid deletion near the C-terminus of yeast Pol δ and rpa1 mutations in Arabidopsis and rice result in meiotic recombination defects [8] , [18] , [19] . Because both Pol δ and RPA1 are required for leading and lagging strand DNA synthesis , it is not known whether meiotic recombination requires the lagging strand synthesis factors . Indeed , meiotic recombination models include only leading strand synthesis , probably because the amount of leading strand synthesis using the 3′ single-strand invasion end as a primer seems sufficient . A recent analysis of the Arabidopsis male meiocyte transcriptome revealed the expression of several DNA synthesis genes [20] . Among these , the DNA replication factor RFC1 ( the ortholog of the yeast and animal RFC1 genes ) was verified by real-time PCR and in situ mRNA hybridization ( data not shown ) , suggesting a function in male meiosis . Here we report a partial loss-of-function rfc1 mutant that has normal vegetative and floral organ development , but displays reduced fertility and meiotic defects . This rfc1 mutant provides an opportunity to test the role of lagging strand synthesis in meiotic recombination . Our analyses demonstrate that the rfc1−/− plants form multivalents and are defective in CO formation via the interference-sensitive pathway ( Type I ) , supporting the idea that lagging strand synthesis is probably important for dHJ formation . Because DNA synthesis is a highly conserved process and the Type I pathway produces the majority of COs in budding yeast , mammals , and flowering plants , a proposed role for lagging strand synthesis in CO formation has important implications for human reproductive health and crop production .
To test RFC1 function , we obtained three alleles ( Figure 1 and Figure S1 ) . rfc1-1 is a point mutant with defects in vegetative development and somatic DNA repair [21]; it has reduced seed production , sheds both viable and nonviable pollen grains , and suffers from mild meiotic defects ( Figure S1E–S1H ) . rfc1-2 ( SALK_140231 ) is a T-DNA insertional allele with reduced fertility; rfc1-3 is also an T-DNA insertional allele but causes seed lethality and can only be maintained as a heterozygote [22] . rfc1-2−/− has normal leaf number , biomass , floral organ identity and number ( Figure 1B–E ) , indicating that it does not have impaired mitotic DNA replication . However , rfc1-2−/− plants have greatly reduced fertility ( Figure 1C ) and produce short seedpods with few seeds ( Figure 1G and 1I ) . F1 progeny derived from pollinating rfc1-2−/− pistils with wild type pollen were fully fertile , but mutant and normal plants segregated in the F2 progeny with a ratio of 2 . 53∶1 instead of the expected ratio of 3∶1 ( p<0 . 05 ) ( Table S1 ) . To verify the decreased transmission of the rfc1-2 allele , we pollinated rfc1-2−/+ plants with wild type pollen and found ∼50% ( 293/577 ) of progeny with the mutant allele ( Table S1 ) . When wild type pistils were pollinated with pollen from an rfc1-2−/+ plant , only 32% ( 105/329 ) of the progeny inherited the mutant allele ( Table S1 ) , indicating reduced transmission of rfc1-2 through the male but not the female gametophytes . Additionally , rfc1-2−/− anthers had few viable pollen grains ( ∼20/anther; n = 50 ) , in contrast to ∼500/anther in wild type ( Figure 1J and 1K ) . In addition , unlike the wild type tetrads with four microspores ( Figure 1L ) , rfc1-2−/− anthers contained polyads with five to eight microspores ( Figure 1M and 1N ) , indicating a meiotic defect . Because RFC1 is essential for the mitotic cell cycle in yeast and is highly conserved in eukaryotes [23] , we expected it to be essential in Arabidopsis as well . Indeed , rfc1-3−/+ plants produce about 51% ( 178/349 ) defective seeds and no homozygous progeny ( n = 385 ) , suggesting that seeds homozygous for rfc1-3 were lethal . We estimated RFC1 expression in rfc1-2−/− plants and found that it was transcribed upstream of , but not spanning the T-DNA insertion ( Figure S1C ) , indicating that a truncated RFC1 transcript is produced . Probing western blots with a polyclonal antibody against RFC1 revealed a ∼110 kDa band corresponding to the predicted molecular weight of RFC1 in the wild type , but not in rfc1-2−/− plants ( Figure S1D ) , indicating that rfc1-2−/− lacked the intact protein . The normal mitotic phenotype of rfc1-2−/− suggests replication is not affected; in addition , analysis of meiotic chromosomes in rfc1-2−/− spo11-1−/− ( below ) indicated that premeiotic replication produced all 20 chromatids . We analyzed male meiosis using chromosome spreads to compare wild type and rfc1-2−/− . From leptotene to pachytene , wild type and rfc1-2−/− meiotic chromosome morphologies were generally similar ( Figure 2A–2C and 2F–H ) , but rfc1-2−/− pachytene chromosomes sometimes had small “bubbles” ( 31% , 26/85 ) , with incomplete synapsis . At diplotene , wild type meiocytes had associated condensed homologs ( Figure 2D ) , but the rfc1-2−/− chromosomes were less compact with regions that were thinner than normal ( Figure 2I ) . At diakinesis , unlike the five intact bivalents in wild type ( Figure 2E ) , rfc1-2−/− meiocytes ( n = 76 ) had 0 ( 18 ) , 1 ( 24 ) , 2 ( 25 ) , 3 ( 7 ) , or 4 ( 2 ) bivalents , and association between more than two chromosomes ( Figure 2J ) . The mean number of bivalents in rfc1-2−/− was 1 . 36 . At metaphase I , the mutant bivalents were often slender with only one crossover ( Figure 2P ) . In addition , four or more rfc1-2−/− chromosomes often formed multivalents in which one chromosome was associated with two other chromosomes ( Figure 2U–2V ) . At anaphase I and later stages , chromosome fragmentation was observed ( Figure 2Q–2T ) . The defects in bivalent formation and chromosome segregation are consistent with the formation of polyad microspores and reduced pollen viability . To verify that the rfc1-2−/− phenotype is caused by the T-DNA insertion in the RFC1 gene , we introduced a wild type RFC1 gene into the rfc1-2 mutant and rescued the mutant fertility and meiosis defects ( Figure S2A–S2D ) . To test RFC1 function in meiosis using plants with even less RFC1 activity than the rfc1-2 allele , we performed reciprocal crosses between rfc1-2−/+ and rfc1-3−/+ and identified rfc1-2/rfc1-3 trans-heterozygous progeny using PCR . The trans-heterozygote was very small , suggesting a defect in mitotic growth , unlike rfc1-2−/− plants; nevertheless , the rfc1-2/rfc1-3 meiocytes showed meiotic phenotypes similar to rfc1-2−/− ( Figure S2E–S2H ) . We also generated transgenic plants with an RNAi construct of RFC1 driven by the meiosis-specific DMC1 promoter and obtained similar meiotic phenotypes to rfc1-2−/− mutants ( Figure S2I–S2L ) , even when pollen viability and fertility was more severely affected than in rfc1-2−/− . These results demonstrate that lesions in RFC1 cause the meiotic and fertility defects , and also indicate that the meiotic phenotypes in rfc1-2−/− plants are due to a meiosis-specific loss-of-function , rather than the effect of a toxic protein . To test whether the rfc1-2−/− bivalents occured between homologs or non-homologs , we used FISH to analyze chromosome pairing and synapsis . In several species , telomeres cluster on the nuclear envelope during leptotene/zygotene in a configuration called a “bouquet” , which facilitates pairing and synapsis [24] . Arabidopsis appears to lack a classic bouquet but is thought to achieve the same function by clustering telomeres around the nucleolus before leptotene [25] . We observed a similar pattern in both wild type and rfc1-2−/− mutant spreads ( Figure 3A–3B ) and rfc1-2−/− samples were also normal at zygotene ( data not shown ) . At pachytene , wild type cells had 8–10 telomere foci ( n>20 ) , consistent with five pair of closely associated homologs ( Figure 3C ) ; however , ∼45% rfc1-2−/− mutant cells ( n = 30 ) had more than 10 ( but fewer than 20 ) foci ( Figure 3D ) , indicating partial separation of telomeres . To test pairing defects in rfc1-2−/− plants , we performed chromosome-specific FISH using a BAC clone ( F19K16 ) located in a telomere-proximal region of chromosome 1 . Similar to the telomere result , the F19K16 signals in wild type and mutant were similar at leptotene and zygotene ( Figure 3E and 3F; Figure S3 ) . At pachytene , wild type had only one signal representing the closely synapsed chromosome 1 arm ( Figure 3G ) , but ∼40% of the rfc1-2−/− cells ( n = 43 ) had two signals ( Figure 3H ) , indicating that the two chromosome 1 arms were at least partially separated . The observation of two separate F19K16 signals in the thin chromosome regions further indicated that the homologs were not properly paired or synapsed at this region in the mutant ( Figure S3E and S3G ) . FISH with a centromere probe revealed the same number of signals between wild type and mutant at leptotene and pachytene ( n>60 ) ( Figure 3I–3L ) , indicating that the mutant had no obvious pairing and synapsis defects near the centromeres , similar to the results with a 45S rDNA probe ( Figure S3I–S3T ) . At diplotene , wild type cells had pairs of closely spaced F19K16 signals corresponding to partially separated homologs ( Figure S3B ) , whereas more widely spaced F19K16 signals were observed in mutant cells ( ∼50% , n = 40 ) ( Figure S3D ) . Wild type and mutant metaphase I cells both had 10 centromere signals ( Figure 3M and 3N ) ; however , mutant cells often had multivalents with four or more chromosomes ( Figure 3N1 ) , indicating non-homologous association . In addition , wild type had only one F19K16 signal ( Figure 3S ) , indicating close association of the two chromosome 1's , but the mutant often had two foci ( Figure 3T ) , suggesting that two chromosome 1's had associated with non-homologs . At anaphase I , wild type chromosomes segregated equally with 5 centromeric foci in each group ( Figure 3O and Q ) , but mutant chromosomes often missegregated ( Figure 3P and 3R ) . Acentric chromosome fragments , including some with F19K16 foci , were seen near the equator ( Figure 3P and 3R–3V ) , possibly resulting from illegitimate non-homologous recombination . Similar results were also obtained using another BAC probe F1N21 ( not shown ) . To further analyze synapsis in rfc1-2−/− , we examined the distribution of ASY1 and ZYP1 , which mediate formation of the synaptonemal complex ( SC ) [26] , [27] . Immunolocalization of both proteins is similar in wild type and rfc1-2−/− from leptotene to pachytene ( Figure 4A–4L ) , except that rfc1-2−/− pachytene chromosomes sometimes had a “bubble” lacking the ZYP1 signal ( Figure 4M and 4N ) . Therefore , axis formation as indicated by the signal of the lateral element protein ASY1 was normal in rfc1-2−/− , but the lack of signal for the central element protein ZYP1 in the “bubble” regions suggested a partial defect in synapsis . The rfc1-2−/− abnormalities in pairing and bivalent formation suggest possible defects in meiotic recombination . In yeast , meiotic recombination is initiated by SPO11-generated DSBs [5] ( Figure 1A ) and one of the Arabidopsis homologs , SPO11-1 , is also important for DSB formation , chromosome pairing and bivalent formation [28] . RAD51 in yeast is crucial in homolog dependent single strand invasion [5] ( Figure 1A ) and its Arabidopsis homolog ( RAD51 ) is required to process SPO11-1 induced DSBs [29]–[30] . Failure to repair SPO11-1-induced DSBs in rad51 and other mutants results in chromosome fragmentation , which is absent in the spo11-1 rad51 double mutant [30] . Because RFC1 is a DNA synthesis factor and DNA synthesis is proposed to occur after RAD51-dependent single strand invasion and the formation of SEI , we hypothesized that chromosome fragmentation in rfc1-2−/− depends on SPO11-1 and that rfc1-2−/− could not suppress the defects of rad51 . To test our hypotheses , we found that the meiotic prophase I of spo11-1-1−/− rfc1-2−/− double mutant ( Figure 5C–5D ) was similar to that of the spo11-1-1−/− with many univalents ( Figure 5A–5B ) , but different from that of rfc1-2−/− ( Figure 2J and 2P ) . To test for epistasis of rad51-3−/− and rfc1-2−/− , chromosome spreads from rad51-3−/− were prepared and lacked bivalents or multivalents ( Figure 5E–5F ) , but rfc1-2−/− had both ( Figure 2J and 2P ) . The rad51-3−/− rfc1-2−/− double mutant resembled rad51-3−/− , without any multivalents ( Figure 5G–5H ) , indicating that rad51 is epistatic to rfc1-2−/− . Therefore , these results support the hypothesis that RFC1 acts after RAD51 to promote recombination . If RFC1 acts downstream of RAD51 , it should not be required for RAD51 loading onto meiotic chromosomes . Immunofluorescence showed that RAD51 foci were similar in wild type and rfc1-2−/− at leptotene ( Figure 6A and 6E ) . At zygotene and pachytene wild type had an average of 230±51 ( n = 30 ) and 50±12 ( n = 35 ) , RAD51 foci respectively ( Figure 6B and 6C ) . rfc1-2−/− meiocytes had similar numbers of RAD51 foci ( 241±55 , n = 30 ) at zygotene ( Figure 6F ) ( p>0 . 05 ) , indicating that RAD51 loading is not compromised in rfc1-2−/− . However , unlike the reduction of RAD51 foci from late zygotene to late pachytene in the wild type , the RAD51 foci persisted in rfc1-2−/− ( 220±50 foci , n = 39 ) at pachytene ( p<0 . 001; Figure 6G ) . This suggests that in rfc1-2−/− RAD51unloading is delayed or new RAD51 foci were generated during pachytene . In addition , wild type DMC1 signals were punctate at pachytene ( Figure S4C ) , but in rfc1-2−/− there were more signals that were distributed along the chromosomes ( Figure S4G–S4H ) . The persistence of RAD51 and DMC1 foci in rfc1-2−/− suggested that RFC1 is important for the normal processing of RAD51/DMC1-bound single-end invasion ( SEI ) intermediates or RFC1-dependent intermediates are important for the homeostasis of DSB formation . Arabidopsis , like yeast and humans , has at least two distinct classes of COs [31]–[33] . Interference-sensitive COs ( Type I ) require MSH4/MSH5 and PTD [34] , [35] , whereas interference-insensitive COs ( Type II ) depend on MUS81 [36] . To determine whether RFC1 affects one or the other of the two CO pathways , we generated double mutants with rfc1-2−/− and examined chromosome spreads . We found that msh4-1−/− rfc1-2−/− ( Figure 5K–5L ) and ptd-1−/− rfc1-2−/− ( Figure 5O–5P ) double mutants had phenotypes of chromosome entanglement and fragmentation similar to those of the rfc1-2−/− single mutants . The mean numbers of bivalents of the double mutant msh4-1−/− rfc1-2−/− ( n = 18 ) and ptd-1−/− rfc1-2−/− ( n = 19 ) were 1 . 27 and 1 . 47 , respectively . These results suggest that RFC1 acts upstream of MSH4 and PTD in the Type I CO pathway . In contrast , the mus81-1−/− rfc1-2−/− double mutant lacked bivalents or multivalents at metaphase I , but had entangled chromosomes ( Figure 5S–5T ) , suggesting that formation of multivalents or chromosome association in rfc1-2−/− also depends on MUS81 . This idea is further supported by the observation that residual viable pollen grains in rfc1-2−/− are virtually eliminated in the mus81-1−/− rfc1-2−/− double mutant . Wild type , mus81-1−/− and rfc1-2−/− anthers ( n = 50 ) had approximately 500 , 350 and 20 viable pollen grains , respectively ( Figure 5U–5W ) , while mus81-1−/− rfc1-2−/− anthers contained no viable pollen , with only occasional abnormally large pollen grains ( Figure 5X ) . These results indicate that RFC1 likely acts in the same pathway as MSH4 and PTD . We hypothesize that the diminished pollen viability of rfc1-2−/− is caused by a reduction of COs . The residual viable pollen in rfc1-2−/− allowed us to measure CO frequency using a unique pollen-based visual assay system [37] . We crossed rfc1-2−/+ with two lines each carrying two or three transgenic markers on chromosomes 2 or 3 encoding different fluorescent proteins ( CFP , YFP and RFP ) ( Figure 7A ) , which are expressed from the pollen-specific LAT52 promoter so their expression in the pollen is directly correlated with the segregation of the markers . As a result , it is possible to distinguish using fluorescence microscopy whether linked markers have experienced an intervening CO by analyzing F2 individuals that were heterozygous for the markers and either RFC1 wild type or rfc1-2−/− . Because COs facilitate proper homolog segregation , the recombination frequency from the small number of viable pollen of rfc1-2−/− is expected to be an over-estimate of the overall frequency among all pollen grains , most of which were dead . CO frequency in the Arabidopsis genome is uneven with hot and cold regions [38] ( Figure 7A ) . We found that for a pair of markers in a hot region on chromosome 3 , the frequency in rfc1-2−/− was lower ( 19 . 01% ) than wild type ( 21 . 73% ) ( p≪0 . 001 ) ( Figure 7A , Table S2 ) . In contrast , in a chromosome 2 cold region with three markers , the recombination frequencies in the viable rfc1-2−/− pollen between any two markers were higher than observed in the wild type ( Figure 7A and Table S2 ) . The frequency of double COs ( one in each adjacent interval ) in rfc1-2−/− was 1 . 34% , and did not differ significantly ( p = 0 . 50 ) compared to the expected value of 1 . 18% derived from the product of the individual CO frequencies in the adjacent intervals . However , it was higher than the observed double CO frequency of 0 . 02% in the wild type ( p≪0 . 001 ) ( Figure 7A and Table S2 ) . These results suggest that wild type COs in this region were largely Type I , but COs in the rfc1-2−/− were Type II .
Mutations in genes encoding the RFC complex cause lethality in mammals and yeast [39] , [40] , making it difficult to study their functions in meiosis . Similarly , the Arabidopsis rfc1-3 mutation is also lethal , consistent with a conservation of RFC1 function for mitotic DNA replication in plants . Furthermore , Liu et al . ( 2010 ) identified a point mutation in RFC1 as a suppressor of ros1 and showed that rfc1-1−/− is small , flowers early and has reduced fertility , indicating that RFC1 is important for Arabidopsis vegetative and reproductive development . This approach also identified similar roles for other components of the DNA replication machinery , such as polymerases α , δ , ε and RPA2A [41] , suggesting that the DNA replication machinery is essential for vegetative somatic development in plants . In this study , we have characterized the rfc1-2 mutant , which has normal vegetative and floral organ development , but has defects in fertility and meiosis . The observations that rfc1-2 is recessive and can be rescued by wild type trans-complementation , and that both the rfc1-2/rfc1-3 trans-heterozygote and RFC1-RNAi transgenic lines had similar meiotic defects indicate that rfc1-2 is a meiosis-specific hypomorphic allele . The rfc1-2−/− phenotypes of reduced pollen viability and fertility are the result of reduced bivalents , multivalent formation , and chromosome fragmentation , as supported by FISH using two chromosome-specific BACs . Proper homolog association depends on repair of SPO11-induced DSBs to produce COs , which in turn require DNA synthesis . It is likely that the reduction of RFC1 function during meiosis blocked normal homolog association . The importance of DNA synthesis for meiotic recombination is also supported by the observation in budding yeast that a non-lethal allele of POL3 has meiotic recombination defects , but normal mitotic growth [19] . Normal meiotic homolog interactions include pairing , synapsis and recombination . Synapsis in rfc1-2−/− occurred , but was sometimes incomplete . Double mutant analysis suggested that RFC1 is in the SPO11-1-dependent pathway , as supported by the observation that the meiotic chromosome fragmentation in rfc1-2−/− depends on SPO11-1-generated DSBs; furthermore , RFC1 probably acts downstream of RAD51 , as it is not required for the loading of RAD51 or DMC1 onto chromosomes . The prolonged localization of RAD51 and DMC1 foci at late pachytene in rfc1-2−/− suggest that RFC1 may promote the dissociation of RAD51 and DMC1 [42] , [43] . RFC1 likely acts in the same meiotic recombination pathway as several known genes , such as the MRN complex , COM1 , RAD51 , BRCA2 and MND1/HOP2 [1] , [4] . rfc1-2−/− differs from mutants of these genes in having paired homologs with some “bubbles” and some discontinuous ZYP1 at pachytene . Crossover interference influences the distribution of COs in most organisms . Several species , including Arabidopsis , budding yeast , mouse and humans have two classes of crossovers: Type I COs are sensitive to interference and Type II COs are not [31]–[33] . The Type I pathway depends on the ZMM proteins ( such as MSH4 and MER3 ) , PTD , and MLH1/3 [4] , [34] , [35] , whereas the Type II pathway is typically a minority class in these organisms and depends in part on the Mus81-Mms4 endonuclease [31] , [36] , . The idea that rfc1-2−/− affected Type I CO formation , but not Type II is supported by several lines of evidence: ( 1 ) rfc1-2−/− has reduced bivalent formation , similar to msh4−/− and ptd−/− , but more severe than mus81−/−; ( 2 ) rfc1-2−/− double mutants with msh4−/− or ptd−/− are more similar to rfc1-2−/− single mutants , but rfc1-2−/− mus81−/− is more severe than either rfc1-2−/− or mus81−/−; ( 3 ) COs in rfc1-2−/− showed no interference . In yeast meiosis , the Msh4/5 complex is thought to stabilize the crossover specific SEI and subsequently binds to dHJ [45] , [46] . The meiosis-specific MER3 helicase promotes RAD51-mediated D-loop extension , and a defect in MER3 causes dramatically delayed homolog alignment accompanied by persistent RAD51 signals [47] , [48] . Similarly , rfc1-2−/− also had a large number of persistent RAD51 and DMC1 foci , suggesting that their processing was delayed . It is possible that RFC1 promotes the dissociation of RAD51 and DMC1 and processing of the SEI intermediates to form dHJs , with the help of MSH4 and MER3 . The loss of RFC1 function causes a failure to dissociate RAD51 and DMC1 , thereby blocking the Type I pathway for CO formation . Instead , the Type II COs are formed between homologs and non-homologs . Alternatively , continued RAD51 and DMC1 foci in late pachytene could be a result of the generation additional DSBs when fewer Type I COs are formed in the rfc1 mutant , as reported for yeast and mouse meioses [42] , [43] . As shown in Figure 1A , in classical and revised DSB repair models , DNA synthesis is essential for both CO or NCO pathways and is initiated from single-stranded DNA ( ssDNA ) generated by 5′-3′ resection of the DNA ends . RAD51 binds to the resulting ssDNA tails to initiate pairing and strand invasion of homologous duplex DNA . Most SEI intermediates form NCOs , and a few form COs . The invading single strand DNA serves as primer for leading strand DNA synthesis before the recombination pathways diverge . The idea that the amount of DNA synthesis is different between these pathways is strongly supported by a recent analysis using BrdU incorporation [49] . Furthermore , genome-wide analysis in yeast and Arabidopsis indicate that the conversion tracts associated with CO are much longer than those associated with NCO [50]–[52] , consistent with more extensive synthesis during CO formation . RFC1 is a single-copy gene in animals , fungi and plants and plays essential roles in loading Pol α-primase for the RNA-DNA primer synthesis , which occurs repeatedly for the production of Okazaki fragments in lagging strand synthesis . Although primer synthesis is also needed to initiate replicative leading strand synthesis , it is not needed for the continuation of leading strand synthesis . Furthermore , as described earlier , leading strand synthesis in meiotic recombination uses the invading strand as the primer , bypassing the need for primer synthesis . Therefore , we propose that RFC1 is important for lagging strand synthesis during dHJ formation during meiotic recombination ( Figure 7B ) . The results from rfc1−/− and RFC1 RNAi plants , as well as double mutants with msh4−/− or ptd−/− , indicate that RFC1 is important for CO formation via the Type I pathway . Furthermore , rfc1−/− mutant phenotypes and genetic analysis with mus81−/− indicate that RFC1 is not required for the Type II pathway . It is possible that the more extensive DNA synthesis in dHJ formation require synthesis on both strands , as is true for the coordinated synthesis on both strands during replication [13] . If there is only leading strand synthesis , an extended region of single-strand DNA would form on the non-template strand of the duplex DNA , which might be unstable and deleterious . In contrast , lagging strand synthesis results in both sides being double-strand , promoting longer DNA synthesis in a way similar to DNA replication and possibly rendering leading strand synthesis unnecessary after second-end capture . In yeast , DSBs are directed to either CO or NCO repair early during recombination , before SC formation [53] . Our analysis of RFC1 function suggests that it plays an early role in promoting dHJ formation by facilitating more extensive DNA synthesis . According to the “ends-apart” model , one DSB end pairs with a homolog chromatid with subsequent DNA synthesis and dHJ formation , whereas the other end remains associated with its sister chromatid [7] , [54] . rfc1-2−/− pachytene chromosomes sometimes had “buddles” , suggesting incompleting synapsis , supporting that idea that dHJs facilitate synapsis [53] . It is possible that more DNA synthesis from both leading and lagging strands results in more stable joint molecules that allow the loading of SC proteins . In the absence of RFC1-dependent dHJs , only MUS81-dependent Type II COs are formed , without homolog bias , as non-homologous multivalents were observed ( Figure 7C ) . Alternatively , recombination with a sister chromatid is possible , as seen in yeast [55] . Recently two groups both demonstrated that aberrant joint molecules formed in the yeast rmi1 or top3 mutants and were resolved by either SGS1 or Mus81-Mms4 [56]–[58] . Moreover , two studies showed recently that the Arabidopsis homolog of the human Fanconi anemia complementation group M ( FANCM ) gene was not only required for the Type I CO formation , but also necessary for the increased number of COs in msh4−/− via the MUS81-dependent pathway [59] , [60] . Because the Mus81-Eme1 or Mms4 function is conserved in yeast [44] , animal [61] , and Arabidopsis [62] , those SEIs that failed to form dHJs in the rfc1-2−/− or fancm−/− mutants were likely processed by MUS81-dependent activities . In summary , we have demonstrated that the highly conserved DNA replication factor RFC1 is required for meiotic recombination and CO formation , and for normal pollen viability and male fertility . Defects in RFC1 function cause abnormal CO formation , which is dependent on MUS81 function . Our results provide strong evidence that lagging strand DNA synthesis is critical for the formation of interference-sensitive COs in Arabidopsis and we offer a model in which RFC1 mediated lagging strand synthesis promotes CO formation via dHJs .
The mutant materials were described as referenced: rfc1-1 and rfc1-2 ( SALK_140231 ) [21] , rfc1-3 ( SALK_146845 ) , spo11-1 ( SALK_045787 ) , rad51-3 ( SAIL_873_C08 ) [29] , msh4-1 ( SALK_136296 ) [35] , mus81-1 ( SALK_107515 ) [36] and ptd-1 ( SALK_127447 ) [34] . Double mutants were identified in the F2 generation by PCR using primers as described in Table S3 . Wild type was Columbia ( Col-0 ) except for rfc1-1 , which was in the C24 background . Plants were grown in a greenhouse under constant 22°C with 16 hr light/8 hr dark . Plants were photographed with a Canon digital camera ( Canon , Tokyo , Japan ) . Pollen grains were stained with Alexander red . Dissected tetrads were stained with 0 . 01% fuchsin basic . Chromosome spreads of wild type and mutants were prepared as described previously [63] and stained with 1 . 5 µg/ml 4 , 6-diamidino-2-phenylindole ( DAPI ) . Images of chromosome spreads were obtained using a Zeiss Axio Imager A2 microscope ( Zeiss , Heidelberg , Germany ) . The images were organized using Photoshop CS ( Adobe Systems , Mountain View , CA ) . The total proteins from inflorescences were prepared as described previously [64] . The protein amount was estimated with a Bio-Rad Protein Assay Kit as specified by the manufacturer ( Bio-Rad , Berkeley , CA , USA ) . The extraction buffer was supplemented with a protease inhibitor cocktail ( catalog No . 1836170; Roche , Mannheim , Germany ) . Proteins were separated on 10% SDS-PAGE and transferred onto PVDF membranes ( catalog No . RPN1416F; Amersham Biosciences , Piscataway , NJ , USA ) . The membranes were blocked overnight at 4°C in TBST ( TBS plus 0 . 05% Tween20 ) containing 5% defatted milk , and incubated in polyclonal anti-RFC1 antibody at a final concentration of 0 . 1 µg/ml . After washed in TBST , the membranes were incubated with 1∶2 , 000 dilution of peroxidase-conjugated goat anti-rabbit antibody ( catalog No . 03116930001; Roche Diagnostics , Indianapolis , Indiana , USA ) for 1 hr and then washed thoroughly . Immunoblotting bands were detected using the ECL PlusTM Reagents ( catalog No . RPN2132; Amersham Biosciences ) according to the manufacturer's protocol . To rescue the rfc1-2 mutant , the full-length cDNA of RFC1 was amplified using primers ( oMF-1000 and oMF-1001 ) with KOD plus DNA polymerase ( Toyobo , Tokyo , Japan ) . The PCR product was purified and ligated into pEASY-T1 Simple ( Transgen , Beijing , China ) and verified by DNA sequencing . The confirmed fragment in pEASY-T1vector was digested with KpnI and SacI for subsequent ligation to the modified p1301 ( http://www . cambia . org ) vector driven by the 35S promoter . To generate the RFC1 RNAi plants , the 334 bp RFC1 specific fragment upstream of the AAA domain was amplified using primers oMF2082 and oMF2083 with restriction sites NcoI/Xbal and ApaI/SalI , respectively . The PCR product was first cloned into vector pEASY-T1 Simple ( Transgen , Beijing , China ) for verification by sequencing . The sense and antisense fragment digested by NcoI-ApaI and SalI-Xbal respectively were cloned into the same sites in pMeioDMC1-Intron sequentially . Both constructs were introduced into Agrobacterium tumefaciens GV3101 for transforming the heterozygous rfc1-2 plant lines and wild type , respectively , using a floral-dip method [65] . Positive T1 plants were screened on 0 . 5×MS medium containing 25 mg/L hygromycin and transferred to soil in greenhouse under 22°C , 16 h light/8 h dark . Chromosome spreads were performed as described ( Ross et al . , 1996 ) . Slides with chromosome preparations were dehydrated with an ethanol series ( 70% , 90% and 100% ) prior to being used for FISH . The 180 bp repetitive sequence of centromere was described previously [66] . The telomere ( clone pAtT4 ) [67] , 5S rDNA [68] , 45S rDNA [68] , and two BAC clones ( F19K16 and F1N21 ) located on the chromosome 1 upper arm were labeled as probes according to the protocol provided by Nick Translation Kit ( No: 11570013910 Roche Diagnostics , Indianapolis , IN , USA ) . FISH analysis was conducted according to a published procedure [69]–[70] . Anti-DIG-rhodamine Fab fragment ( Cat#1207750 Roche Diagnostics ) was used as second antibody to detect probe labeled with DIG . Chromosomes were counterstained with 4 , 6-diamidinophenylindole ( DAPI ) ( Vector Laboratories , Burlingame , CA , USA ) . Chromosome images were captured under the Zeiss Axio Imager A2 fluorescence microscope with a high resolution microscopy camera AxioCam MRc Rev . 3 FireWire ( D ) . Peptide antisera were raised in rabbits against the amino acid sequence of NAVQQQDDEETQHGPF ( AtRAD51 ) , EREENDEDEDLFEMIDK ( AtDMC1 ) , NCSQASQ DRRGRKTS ( AtASY1 ) , GSKRSEHIRVRSDNDNVQD ( AtZYP1 ) , GSSGSRKAAGKGRG RGK ( AtRFC1 ) conjugated to KLH ( GL Biochem , Shanghai , Ltd: www . glschina . com ) . Western blot was performed to verify the individual antigen for antibodies using the total Arabidopsis inflorescence proteins ( data not shown ) . Specificity of the purified anti-RAD51 and DMC1 antibodies was confirmed using the rad51 , dmc1 and rfc1 mutants as negative controls . The specificity of the anti-RFC1 antibodies was verified using a western with the immunizing peptide as a competitive antigen , according to a previously described procedure ( Blocking with Immunizing Peptide ( BL ) Protocol: www . abcam . com/technical ) . Immunofluorescence was performed as previously described with minor modifications [71] . The primary antibodies with diluted 1∶100 ( ASY1 , RAD51 and DMC1 ) or 1∶200 ( ZYP1 ) in blocking buffer were added to the slides covered with parafilm and incubated overnight at 4°C in a moisture chamber . The slides were washed with washing buffer II ( 1×PBS+0 . 1% Tween 20 ) three times and 15 min for each time . The secondary antibody ( goat anti-rabbit ( H+L ) , lot: 835724 , Invitrogen , Foster City , CA , USA ) , with 1∶500 dilution in blocking buffer was added to slides , covered with parafilm and incubated in humidified atmosphere at 37°C for 60 min in the dark . The slides were washed with washing buffer II three times for 15 min and mounted in vectashield antifade medium ( Vector Laboratories , Burlingame , CA , USA ) with 1 . 5 µg/ml DAPI . Images were taken using an AxioCam HRc ( Zeiss ) camera . rfc1-2−/− plants were crossed to lines carrying transgenic markers ( M ) encoding the fluorescent protein ( dsRED2 , eYFP and/or CFP ) expressed from the post-meiotic pollen-specific promoter LAT52 in a qrt1-2−/− background . F2 rfc1-2−/− M−/+ plants were selected by monitoring pollen fluorescence according to a previous procedure [36] . Pollen grains were scored as recombinant or parental by monitoring the expression of the fluorescent markers . CO frequencies between any two transgenic markers or double CO frequencies in adjacent intervals were calculated using the following formula: ( recombinant pollen/total viable pollen grains ) *100% . All photographs were taken using the Zeiss Axio Imager A2 fluorescence microscope with a high resolution microscopy camera AxioCam MRc Rev . 3 FireWire ( D ) . All of the data were analyzed statistically using Microsoft Excel 2007 ( Microsoft ) for calculating mean and SE and T-test . The reported P values are either exact values or Gaussian approximations . Arabidopsis Genome Initiative gene identifiers are as follows: RFC1 ( At5g22010 ) ; SPO11 ( At2g13170 ) ; RAD51 ( At5g20850 ) ; MSH4 ( At4g17380 ) ; MUS81 ( At4g30870 ) ; PTD ( At1g12790 ) .
|
Meiotic recombination is important for pairing and sustained association of homologous chromosomes ( homologs ) , thereby ensuring proper homolog segregation and normal fertility . DNA synthesis is thought to be required for meiotic recombination , but few genes coding for DNA synthesis factors have been studied for possible meiotic functions because their essential roles in the mitotic cell cycle make it difficult to study their meiotic functions due to the lethality of corresponding null mutations . Current models for meiotic recombination only include leading strand DNA synthesis . We found that the Arabidopsis gene encoding the DNA REPLICATION FACTOR C1 ( RFC1 ) important for lagging strand synthesis promotes meiotic recombination via a specific pathway for crossovers ( COs ) that involves the formation of double Holliday Junction ( dHJ ) intermediates . Therefore , lagging strand DNA synthesis is likely important for meiotic recombination . Because DNA synthesis is a highly conserved process and meiotic recombination is highly similar among budding yeast , mammals , and flowering plants , the proposed function of lagging strand synthesis for meiotic recombination might be a general feature of meiosis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology",
"plant",
"cell",
"biology",
"gene",
"function",
"model",
"organisms",
"molecular",
"genetics",
"arabidopsis",
"thaliana",
"chromosome",
"biology",
"plant",
"genetics",
"biology",
"plant",
"and",
"algal",
"models",
"cell",
"biology",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
The DNA Replication Factor RFC1 Is Required for Interference-Sensitive Meiotic Crossovers in Arabidopsis thaliana
|
Infectious prions contain a self-propagating , misfolded conformer of the prion protein termed PrPSc . A critical prediction of the protein-only hypothesis is that autocatalytic PrPSc molecules should be infectious . However , some autocatalytic recombinant PrPSc molecules have low or undetectable levels of specific infectivity in bioassays , and the essential determinants of recombinant prion infectivity remain obscure . To identify structural and functional features specifically associated with infectivity , we compared the properties of two autocatalytic recombinant PrP conformers derived from the same original template , which differ by >105-fold in specific infectivity for wild-type mice . Structurally , hydrogen/deuterium exchange mass spectrometry ( DXMS ) studies revealed that solvent accessibility profiles of infectious and non-infectious autocatalytic recombinant PrP conformers are remarkably similar throughout their protease-resistant cores , except for two domains encompassing residues 91-115 and 144-163 . Raman spectroscopy and immunoprecipitation studies confirm that these domains adopt distinct conformations within infectious versus non-infectious autocatalytic recombinant PrP conformers . Functionally , in vitro prion propagation experiments show that the non-infectious conformer is unable to seed mouse PrPC substrates containing a glycosylphosphatidylinositol ( GPI ) anchor , including native PrPC . Taken together , these results indicate that having a conformation that can be specifically adopted by post-translationally modified PrPC molecules is an essential determinant of biological infectivity for recombinant prions , and suggest that this ability is associated with discrete features of PrPSc structure .
The conformational conversion of the host-encoded prion protein ( PrP ) is a central pathogenic event in the prion diseases [1] . In healthy individuals , PrP adopts a fold that is rich in α-helix , termed PrPC , and is post-translationally modified by the incorporation of N-linked glycans and a C-terminal glycosylphosphatidylinositol ( GPI ) anchor . In individuals suffering from prion disease , PrPC is misfolded into a β-sheet rich conformation , termed PrPSc , which is capable of acting as a template for the conformational conversion of additional PrPC molecules into PrPSc . This self-propagating activity of PrPSc is referred to as autocatalysis and is thought to underlie the infectious nature of the prion diseases . A critical prediction of the protein-only hypothesis is that autocatalytic PrPSc molecules should be infectious . A number of in vitro techniques for generating misfolded , autocatalytic PrPSc conformers have been developed and refined , including the cell-free conversion assay [2] and the serial protein misfolding cyclic amplification ( sPMCA ) technique [3 , 4] . With few exceptions , PrPSc conformers derived from post-translationally modified , native PrPC substrates have been highly infectious when bioassayed in wild-type animals [4–8] . In contrast , various autocatalytic PrPSc conformers derived from recombinant PrP substrates lacking post-translational modifications have displayed large variations in specific infectivity levels as determined by bioassay in wild-type animals [9–15] . The structural and functional basis of this striking variability in specific infectivity between different autocatalytic recombinant PrPSc molecules remains unknown . Recently , using only bacterially expressed recombinant PrP and a single endogenous phospholipid cofactor molecule , phosphatidylethanolamine ( PE ) , as substrates , Deleault et al . successfully produced high titer ( 2 . 2 x 106 LD50 U/μg PrP ) , chemically defined mouse prions in vitro [10] . Interestingly , it was observed that the prions produced from these minimal components always formed into a single infectious strain with unique , novel biological properties regardless of the seed originally used to template the in vitro reactions . Importantly , when this novel prion strain was subsequently propagated in the absence of PE cofactor , a new misfolded recombinant PrP conformer was produced , which could also self-propagate in sPMCA reactions , but which surprisingly failed to cause disease upon injection into wild-type mice [10] . This new autocatalytic conformer , which we refer to as protein-only PrPSc , therefore had a >105-fold lower level of specific infectivity as compared to the PrPSc conformer produced in the presence of PE cofactor , which we refer to as cofactor PrPSc . We saw an opportunity to identify structural and functional properties associated with recombinant PrPSc infectivity by directly comparing these two related PrPSc conformers , which share the same origin and autocatalytic behavior , but differ strikingly in biological infectivity .
Cofactor and protein-only PrPSc are distinct misfolded recombinant PrP conformers that differ >105-fold in their specific infectivity for wild-type mice [10] . While both of these conformers demonstrate autocatalytic activity when used to seed sPMCA reactions containing recombinant PrP substrate ( Fig 1A and [10] ) , only cofactor PrPSc also demonstrates autocatalysis when used to seed sPMCA reactions containing normal brain homogenate as the substrate ( Fig 1B and [10] ) . The complete failure of protein-only PrPSc to function as a seed for conversion reactions containing native PrPC substrate ( Fig 1B , left sample group ) provides a logical explanation for this conformer’s lack of infectious activity in vivo , and may apply more generally to other recombinant PrPSc conformers which demonstrate low levels of specific infectivity in bioassays . Using cofactor PrPSc as a well-matched control , we therefore sought to gain structural and mechanistic insight into the substrate-dependence of protein-only PrPSc autocatalytic activity as a means to understand the structural and functional determinants of recombinant PrPSc infectivity . To help focus the structural comparison between cofactor and protein-only PrPSc molecules , we first tested whether the protease-resistant cores of both conformers , which contain approximately two thirds of the residues of mature full-length PrP , have the same substrate-specific activity as their respective parent PrPSc molecules in in vitro propagation experiments ( Fig 1 ) . We found that in sPMCA reactions containing recombinant PrP substrate and defined cofactors , both full-length and truncated cofactor and protein-only PrPSc molecules function as competent seeds which faithfully propagate the characteristic PK-resistant bands associated with their parent PrPSc molecules ( Fig 1A ) [10] . Moreover , in in vitro conversion reactions containing native , brain-derived PrPC substrate we found that full-length and PK-digested cofactor PrPSc drive the conversion of native PrPC ( Fig 1B , third and fourth panels ) , while full-length and truncated protein-only PrPSc do not ( Fig 1B , first and second panels ) , indicating that functional differences in the in vitro activity of cofactor and protein-only PrPSc molecules can be localized to the PK-resistant core . Epitope mapping of the cofactor and protein-only PrPSc PK-resistant cores ( S1 Fig ) , revealed that both are C-terminal PrP fragments that include the 6D11 epitope ( residues 93–109 , with 97–100 as the major determinants of binding [16] ) ( S1 Fig , top panel ) and the extreme C-terminus ( S1 Fig , bottom panel ) . Based on these results , we focused our subsequent structural analyses on C-terminal residues beginning at glycine 89 ( G89 ) , the primary PK-cleavage site for PrPSc 27–30 [17] . To compare the structures of cofactor PrPSc and protein-only PrPSc molecules , we performed hydrogen/deuterium exchange MS ( DXMS ) on these two conformers generated in parallel from the same OSU prion strain seed ( Fig 2A ) . The OSU prion strain was originally synthesized by Wang et al . using recombinant PrP , total liver RNA and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol ( POPG ) [15] , and it has previously been referred to as the OSU strain by Deleault et al . [10] . OSU-seeded cofactor and protein-only PrPSc samples used for structural analysis in this study were generated with high conversion efficiency in sPMCA ( S2 Fig ) and then purified prior to DXMS analysis by a series of ultracentrifugation steps described in Materials and Methods . Co-sedimentation of significant quantities of non-specifically aggregated , protease-sensitive PrP was ruled out in the OSU-seeded cofactor PrPSc DXMS sample due to its near complete conversion to PK-resistant PrPSc ( 96% PK-resistant conversion efficiency , S2 Fig ) . To assess the potential contribution of non-specifically aggregated , protease-sensitive PrP to the OSU-seeded protein-only PrPSc sample analyzed by DXMS ( 77% PK-resistant conversion efficiency , S2 Fig ) , we mock-seeded protein-only PMCA reactions and subjected the resulting material to the DXMS purification protocol ( S3 Fig , top panel ) . We did not detect any non-specifically aggregated PrP in these mock-seeded PMCA reactions ( S3 Fig , top panel , samples S0 vs P3 ) , indicating that the protein-only PrPSc DXMS sample does not contain appreciable quantities of non-specifically aggregated , protease sensitive PrP . Regional solvent accessibility of cofactor and protein-only PrPSc was determined by incorporating deuteration data from 188 overlapping C-terminal peptides ( Fig 2C ) recovered in a representative experiment from each deuterium-labeled PrPSc sample . This high density of overlapping deuterated peptides provides PrPSc solvent accessibility measurements with resolution down to segments of ~5 amino acids . In examining and discussing the results of this study , specific regions of the PrP primary sequence are referred to either by explicit residue numbering , based on the mouse PrP sequence , or with reference to the location of known secondary structural elements in monomeric , α-helical recombinant PrP ( α-PrP ) . For example , the domain corresponding to residues 178–216 could also be described as α2-α3 because it encompasses the second and third α-helices in α-PrP . The C-terminal cores of cofactor and protein-only PrPSc are both substantially protected from solvent exchange ( Fig 2A ) as compared to α-PrP ( S4 Fig ) . This solvent protection is consistent with widespread conversion to β-sheet secondary structure and/or the formation of large solvent-excluding aggregates [20] . Within the misfolded , solvent-protected PrPSc core there are large regions in which cofactor PrPSc and protein-only PrPSc have remarkably similar solvent accessibility profiles—in particular , the regions containing residues 118–143 and 165–230 . However , there are also specific domains in which the cofactor and protein-only conformations can be distinguished by solvent accessibility . Most clearly , the domain encompassing residues 144–163 , corresponding to α1 and β2 in α-PrP [21] , is more solvent-exposed in protein-only PrPSc than in cofactor PrPSc ( Fig 2A ) . The relatively exposed structure of the α1-β2 domain was preserved , and in fact accentuated , in an independently prepared protein-only PrPSc sample ( S5 Fig ) . In addition , residues 91–110 appear to be slightly more exposed in protein-only PrPSc than in cofactor PrPSc while residues in the palindromic region ( amino acids 111–120 ) are more obviously protected in protein-only PrPSc . Differences in these relatively N-terminal regions may contribute to the differing susceptibility to PK cleavage observed for the cofactor and protein-only PrPSc conformations ( Fig 1 and S1 and S2 Figs ) . Deleault et al . [10] originally used three prion strains with distinct infectious phenotypes to seed the chemically-defined , recombinant PrP conversion system used to produce cofactor and protein-only PrPSc molecules . Interestingly , the three input strains converged into a single strain with a novel biological phenotype upon propagation in sPMCA reactions containing only recombinant PrP substrate and a single cofactor . We used DXMS to examine the structures of the two additional cofactor PrPSc samples generated by Deleault et al . in sPMCA reactions that were initially seeded with mouse prion strains distinct from the OSU strain ( 301C- and ME7-seeded cofactor PrPSc ) ( Fig 2B ) . The results revealed that the PK-resistant cores of all three cofactor PrPSc molecules have nearly identical solvent accessibility profiles ( Fig 2A and 2B ) , consistent with convergence into a single cofactor PrPSc conformation . As was the case with OSU-seeded cofactor and protein-only PrPSc , we assessed the contribution of non-specifically aggregated , protease-sensitive PrP to the DXMS data for 301C- and ME7-seeded cofactor PrPSc by determining sample conversion efficiency ( S2 Fig ) and performing a mock-seeding experiment ( S3 Fig , bottom panel ) . 301C-seeded cofactor PrPSc is almost entirely converted to PK-resistant PrPSc ( 99% PK-resistant conversion efficiency , S2 Fig ) , ruling out any significant contribution of non-specific PrP aggregation to the presented DXMS data . Mock-seeding of cofactor-supplemented PMCA reactions resulted in the recovery of approximately 8% of the starting material as non-specifically aggregated , protease-sensitive PrP ( S3 Fig , bottom panel , samples S0 vs P3 ) . For ME7-seeded cofactor PrPSc ( 82% PK-resistant conversion efficiency , S2 Fig ) , we therefore estimate that such non-specifically aggregated PrP accounts for no more than 2% of the sample analyzed by DXMS ( ie . 8% of the unconverted , PK-sensitive material yields the estimate for non-specifically aggregated , PK-sensitive PrP , or ~1 . 4% of the total input PrP , which is then divided by the sum of the PK-resistant and PK-sensitive insoluble PrP , or ~83 . 4% of the total input PrP ) . The data from cofactor PrPSc and protein-only PrPSc sample replicates were aggregated , yielding 63 shared peptides for which individual deuteration curves could be plotted ( S6 and S7 Figs ) . Interestingly , peptides that include residues N-terminal to G89 were less frequently recovered and showed irregular deuteration profiles specifically in cofactor PrPSc samples ( S6 and S7 Figs ) . Having identified the α1-β2 domain by DXMS as a region of conformational divergence in our cofactor and protein-only PrPSc samples , we sought to confirm this finding using additional biochemical and biophysical approaches . The α1-β2 region contains a portion of the epitope for 15B3 , a well-characterized PrPSc-specific conformational antibody [18] . The regions of PrP primary structure that comprise the discontinuous 15B3 epitope are shown schematically in Fig 2A . In a single immunoprecipitation experiment , 15B3 efficiently pulled down all three of our cofactor PrPSc samples ( Fig 3 , top three panels ) , but only weakly bound protein-only PrPSc ( Fig 3 , bottom panel ) , indicating a disruption of the 15B3 conformational epitope , consistent with our DXMS results . We further sought to confirm a conformational difference between cofactor and protein-only PrPSc in the α1-β2 domain using Raman spectroscopy ( Fig 4 ) . Analysis of the Raman spectra acquired from these two conformers identified multiple Raman shifts that could be assigned to tyrosine residues , which are plentiful in the PrP C-terminus and specifically enriched in the α1-β2 domain ( 6 of 11 total C-terminal tyrosines ) . By Raman spectroscopy , protein-only PrPSc appears to contain more exposed tyrosine residues than cofactor PrPSc as evidenced from the increased ring ν ( C = C ) intensity at ~1620 cm-1 ( Fig 4 , left panel ) , the 850 cm-1/830 cm-1 ratio being greater than 1 ( Fig 4 , right panel ) [22] , and the increased ring ν ( CH ) intensity at ~3075 cm-1 ( S8 Fig , left panel ) [23 , 24] , consistent with our DXMS results . In addition , and also consistent with our DXMS data , protein-only PrPSc appears to contain more exposed CNH groups than cofactor PrPSc as indicated by the increased intensity in the 1530–1580 cm-1 Amide II region , corresponding to ν ( CN ) and δ ( CNH ) Raman shifts ( S8 Fig , right panel ) , as well as an increased ν ( CN ) intensity at ~3300 cm-1 ( S8 Fig , left panel ) . These exposed CNH groups likely originate from the 4 exposed arginine ( R ) and single exposed glutamine ( Q ) , asparagine ( N ) and tryptophan ( W ) residues in the α1-β2 domain , or from CNH-containing side chains in the N-terminal portion of the PK-resistant PrPSc core ( residues ~91–115 ) . Our DXMS data suggests that structural differences between cofactor and protein-only PrPSc are limited to specific domains ( Fig 2A ) and that these structural differences affect the ability of recombinant PrPSc to convert native PrPC ( Fig 1B ) . As conversion substrates , α-PrP and native PrPC share the same primary sequence , but PrPC also contains bulky N-linked glycans and a GPI anchor as post-translational modifications . Therefore , we hypothesized that limited conformational differences might dramatically alter a recombinant conformer’s infectious activity by impinging on spatial regions that would be occupied by N-linked glycans or a GPI anchor should PrPC adopt the same conformation . To test this hypothesis , we partially purified native PrPC and performed enzymatic deglycosylation with PNGase F . The resulting PrPC molecules , which uniformly contain a GPI anchor as the sole post-translational modification , were used as the substrate in sPMCA experiments seeded with cofactor and protein-only PrPSc ( Fig 5 and repeated in S9 Fig ) . Like brain-derived prions , recombinant cofactor PrPSc was able to template the conversion of unglycosylated PrPC ( Fig 5 and S9 Fig , middle and right sample groups ) , whereas protein-only PrPSc failed to function as an autocatalytic seed in the same substrate ( Fig 5 and S9 Fig , left sample group ) . Note that initial conversion of unglycosylated PrPC substrate molecules to a PK-resistant form was detected after seeding with both cofactor and protein-only PrPSc and 24 h of intermittent sonication , as indicated by the appearance of PK digestion products of slightly higher apparent molecular weight than the respective input seeds ( Fig 5 and S9 Fig , left and middle sample groups , round 1 vs 0 ) . In the case of protein-only PrPSc , this higher molecular weight product is diluted out in proportion to the initial seed ( Fig 5 and S9 Fig , left sample group , round 2 vs 1 ) , suggesting stoichiometric , as opposed to autocatalytic , PrP conversion . In contrast , the higher molecular weight band generated from infectious PrPSc seed was able to propagate independently of the initial PrPSc seed , suggesting autocatalytic PrP conversion ( Fig 5 and S9 Fig , left and middle sample groups , rounds 1–3 ) . This result indicates that the existence of bulky N-linked substrate glycans is not solely responsible for the failure of recombinant protein-only PrPSc to function as a seed for native PrPC . Moreover , by isolating the effect of a single substrate post-translational modification on recombinant PrPSc function , this result provides proof of principle that such modifications may impair the efficient replication of certain recombinant PrPSc conformers in vivo .
A misfolded conformer of the prion protein , PrPSc , is an essential , and possibly the sole , component of infectious prions [1] . Although PrPSc is known to be rich in β-sheet [25–27] , a high-resolution structure of this PrP conformer is lacking , and the structural features of PrPSc that determine its infectious activity remain obscure . Recently , Deleault et al . used a chemically-defined , minimal PrP conversion system and identical seeding material to generate two distinct , autocatalytic recombinant PrPSc conformers that differ >105-fold in their specific infectivity [10] . One recombinant conformer , produced in the presence of PE cofactor molecules and termed cofactor PrPSc , had a titer in normal C57BL mice nearly equivalent to that of brain-derived PrPSc ( 2 . 2 x 106 LD50 U/μg PrP ) , while the other conformer , produced in the absence of cofactor molecules and termed protein-only PrPSc , failed to cause disease in the same host , even at the most concentrated dose tested . In the present study , we have taken advantage of the uniquely controlled opportunity presented by these two PrPSc conformers , which share the same origin and autocatalytic behavior but differ strikingly in their biological activity , in order to investigate the structural and functional determinants of recombinant PrPSc infectivity . By comparing the structures of these two conformers using DXMS , we find that cofactor and protein-only PrPSc molecules have remarkably similar solvent accessibility profiles within the PK-resistant PrPSc core ( Fig 2A ) . Indeed , they are virtually indistinguishable by this measure in the domains that correspond roughly to α2-α3 in α-helical PrP ( ~ residues 165–230 ) , and to the stretch of residues between the hydrophobic domain and the start of α1 ( ~118–143 ) ( Fig 2A ) , both displaying significant protection from solvent exchange , consistent with widespread conversion to β-sheet secondary structure . The degree of solvent protection , especially N-terminal to β2 , distinguishes cofactor and protein-only PrPSc from synthetic PrP amyloids [27–31] , and is most consistent with previous DXMS studies using brain-derived and recombinant prions [27 , 32 , 33] . Like these recent DXMS studies , the data from the present study are consistent only with those models of PrPSc structure that involve a complete refolding of the PrPC C-terminal α-helices to β-sheet—for example , the parallel in-register β-sheet architectures proposed by Cobb et al . [34] and more recently by Groveman et al . [35] . Interestingly , the modest increase in solvent accessibility seen at residues 187–196 in all four PrPSc conformers studied here ( Fig 2 ) corresponds well with the proposed loop of the native disulfide hairpin predicted by both of these models . In addition to broad similarities between cofactor and protein-only PrPSc in solvent accessibility , we have identified two specific domains in which the cofactor and protein-only PrPSc conformers can be conformationally distinguished: most clearly within the domain that corresponds to α1 through the C-terminus of β2 in PrPC ( ~ residues 144–163 ) , but also within a domain at the N-terminus of the PK-reistant core , comprising residues ~91–115 ( Fig 2A ) . In both of these domains , protein-only PrPSc appears to be more exposed to solvent exchange than cofactor PrPSc . Given the role PE cofactor molecules play in the formation of cofactor , but not protein-only , PrPSc [10] , it is likely that the relatively solvent-protected structural features selectively associated with cofactor PrPSc are cofactor-induced . Moreover , the fact that the PK-resistant cores of all three cofactor PrPSc samples , derived from distinct prion strains but propagated in the same chemically-defined system , are highly similar ( Fig 2A and 2B ) suggests that the convergence of biological strain properties observed by Deleault et al . [10] is associated with a convergence of PrPSc structure . The α1-β2 domain , which appears to adopt different conformational states in cofactor and protein-only PrPSc ( Figs 2–4 ) , has previously been identified as a region of PrP that has important implications for PrP misfolding . For example , several PrPSc-selective conformational antibodies are known to have epitopes that reside within this domain [18 , 36] , and small deletions towards the C-terminus of this domain produce PrP molecules that do not readily form PrPSc and , in fact , function as dominant-negative inhibitors of PrPSc replication in full-length PrP substrates [37] . Moreover , in the complete absence of the α1-β2 domain , a redacted ‘miniprion’ is capable misfolding to form PrPSc , but does not cause disease when inoculated into animals expressing full-length PrP [38 , 39] . Interestingly , the α1-β2 domain lies adjacent the β2-α2 loop , a region of PrP known to play an important role in prion formation and interspecies prion transmission [40–43] . Less is known about the second domain ( amino acids ~91–115 ) in which cofactor and protein-only PrPSc appear to adopt different conformational states ( Fig 2 and S5 Fig ) . This domain includes the so-called ‘fifth site’ for Cu2+ binding [44–46] . It is important to acknowledge that any comparisons drawn between DXMS results in the present study and the activities of cofactor and protein-only PrPSc are correlations only , and that it is possible the structural features underlying PrPSc-associated activities such as autocatalysis and infectivity may be subtle and/or beyond the resolution of the DXMS approach . Similarly , it should be acknowledged that DXMS provides a measure of the average deuterium incorporation at a given amide proton position over a population of PrPSc molecules . It has been proposed that PrPSc exists as a heterogeneous conformational mixture of so-called quasi-species [47] , but it is not known—for the recombinant PrPSc conformers studied here , or any other PrPSc preparation—what proportion of PrPSc molecules exhibit autocatalytic or infectious activity . Therefore , the solvent accessibility profiles obtained in this study are representative of conformational differences at a PrPSc population level , and may not be representative of rare , and potentially biochemically/biologically active , components within a given PrPSc sample . In addition , it should be made clear that when interpreting ribbon diagrams , as in Fig 2 , similar solvent accessibility profiles do not guarantee similar tertiary and/or quaternary structures , although by using a set of matched peptides with dense , overlapping coverage of the region of interest ( Fig 2C ) to compare the solvent accessibility of the cofactor and protein-only PrPSc conformers , we have increased confidence in such an interpretation of the data . Finally , we do not claim that a relatively solvent inaccessible conformation of either of the two structurally divergent domains identified in the present study is required for PrPSc infectivity generally . The data presented here are specific to the situation in which seed and substrate are both of the mouse PrP sequence , and it is possible that different PrP sequences may have different infectivity-associated conformational spaces . Consistent with a sequence-specific interpretation of our results , it has previously been shown that native mouse prions also appear to have a relatively solvent-protected structure in the α1-β2 domain [27] , while a recent study showed that this same domain is relatively solvent exposed in native human prions [33] . The dissociation of in vitro autocatalytic activity and infectivity seen in some , but not all , recombinant PrPSc conformers [10 , 14 , 15] presents an interesting functional question: why is it that a PrPSc conformer capable of self-replication in recombinant PrP substrate fails to function as a template for native PrPC conversion in vivo ? One obvious possibility relates to substrate complexity: although α-PrP and native PrPC share similar secondary structures [21 , 48] , native PrPC is a more complex conversion substrate due to the post-translational addition of N-linked glycans and a GPI anchor , and due its location within the membrane environment of cells . Using our cofactor and protein-only recombinant PrPSc seeds as a controlled pair , we performed in vitro conversion experiments in which we , in a step-wise manner , modified native PrPC substrate to make it more and more like α-PrP in an effort to identify the factor ( s ) that prevent protein-only PrPSc from converting PrPC in vivo . Remarkably , the functional difference between cofactor and protein-only PrPSc persisted even after extracting native PrPC from the membrane environment ( Fig 1B and [10] ) and removing all N-linked glycans ( Fig 5 and S9 Fig ) , suggesting that neither of these factors is responsible for preventing protein-only PrPSc from converting native PrPC in vivo . Unfortunately , the complementary experiment , in which the GPI anchor of PrPC is selectively removed and that modified PrPC substrate used in conversion reactions is not possible for two reasons: 1 . Delipidated PrPC is a poor conversion substrate , even for sPMCA experiments seeded with native prions [49] , and 2 . Detection of delipidated GPI-anchored proteins by Western blotting is technically challenging [50] . Nevertheless , we infer from the available data that the functional difference between cofactor and protein-only PrPSc is mostly likely attributable to differing abilities of these two conformers to template the conversion of wild-type mouse PrP substrates containing a GPI anchor . Interestingly , Kim et al . have previously demonstrated that the GPI anchor plays an important role in the in vitro formation of native PrPSc conformers [49] . To integrate the results of our structural and functional comparison of cofactor and protein-only PrPSc , we propose a model to account for the striking variation in specific infectivity observed for recombinant PrPSc conformers [9–15] ( Fig 6 ) . In this model , the PrP polypeptide backbone , represented by recombinant PrP , is capable of adopting a wide variety of autocatalytic conformations . However , post-translationally modified , native PrPC can only adopt a subset of these conformations , and this subset represents the infectious recombinant PrPSc conformers . From previous studies of PrP and other proteins , there is evidence to suggest that post-translational modifications can alter or restrict protein folding pathways . Indeed , N-linked glycans are known broadly to have chaperone-like effects and to contribute to protein stability [51] . In the case of PrP , it has been observed that the presence of glycans can alter the rate of amyloid fibril formation [52 , 53] , and that by restricting available PrP glycoforms it is possible to control prion strain susceptibility [54] . Whether GPI anchors play a role in protein folding or misfolding is less clear . The loss of the GPI anchor of PrP ( which is concomitant with a significant reduction in PrPC glycosylation in vivo [55] ) appears to have only a modest effect on PrPSc structure [56] , but substantially alters the biochemical properties of PrPSc and promotes the formation of fibrillar aggregates [57 , 58] and interferes with PrPSc replication in vitro [49] . Interestingly , the co-expresssion of anchorless and wild-type PrPC molecules in vivo appears to enhance host susceptibility to recombinant PrP amyloid fibrils [59] , while the experimental addition of a GPI anchor to the amyloidogenic yeast protein , Sup35p , prevents the formation of fibrillar structures , leading instead to the formation of PrPSc-like , non-fibrillar aggregates [60] . While the data from the present study specifically point to a role for the GPI anchor of native mouse PrPC in restricting the range of recombinant PrPSc conformers that possess infectious activity , we cannot exclude the possibility that N-linked glycans also influence the infectivity-associated recombinant PrPSc conformational space . In fact , we speculate that the boundaries of this subset of the conformational space are likely to be highly context-dependent , determined by a complex interplay between the polypeptide sequence of a given PrPC substrate molecule and all of its associated post-translational modifications . In conclusion , we report for the first time a structural and functional comparison between two autocatalytic recombinant PrPSc conformers that share the same origin and biochemical behavior , but differ >105-fold in infectious titer for wild-type mice . Based on our findings , we suggest that those autocatalytic recombinant PrPSc conformers which are also highly infectious contain specific structural features in a limited number of PrP domains , and that these features may be required in order to accommodate specific substrate post-translational modifications during native PrPC misfolding in vivo .
Cofactor and protein-only PrPSc [10]were generated by sPMCA as described [10] . Briefly , 100–200 ul reactions containing 6 μg/ml recombinant mouse PrP 23–230 ( recombinant PrP ) in conversion buffer [20 mM Tris ( pH 7 . 5 ) , 135 mM NaCl , 5 mM EDTA ( pH 7 . 5 ) , 0 . 15% Triton X-100] were supplemented with either brain-derived cofactor [10] for cofactor PrPSc propagation , or water for protein-only PrPSc propagation . Reactions were seeded with 1/10th volume of converted cofactor or protein-only PrPSc PMCA product and sonicated with 15-s pulses every 30 min for 24 h at 37°C . After 24 h of PMCA , 1/10th volume of the reaction was used to seed fresh substrate cocktail and the 24 h sonication program was repeated . PMCA reactions were sonicated in microplate horns using a Misonix S-4000 power supply ( Qsonica ) set to amplitude 50–60 . Sample tubes were sealed with Parafilm ( Bemis Company ) and the sonicator horn was soaked in 100% bleach prior to switching to propagation of a different PrPSc species in order to prevent cross-contamination . Formation of cofactor and protein-only PrPSc was monitored by digestion of PMCA samples with proteinase K ( PK ) and Western blotting . Samples were treated with 25 μg/ ml PK ( Roche ) for 30 min at 37°C , and digestion reactions quenched by the addition of SDS-PAGE loading buffer and heating to 95°C for 10 min . SDS-PAGE and Western blotting were performed as described previously [10] using mAb 27/33 , unless otherwise specified . All centrifugation was done at 4°C . Cofactor and protein-only PrPSc PMCA products were treated or mock-treated with PK as described above and the reaction quenched by the addition of PMSF to 5 mM final concentration . To remove PK , excess lipids , and any soluble recombinant PrP digestion products , digested PrPSc was washed twice with nOG wash buffer ( 1% n-octyl-beta-D-glucopyranoside ( nOG ) , 150 mM NaCl , 8 . 3 mM Tris pH 7 . 2 ) by centrifugation at 100 , 000 rcf for 1 h , with resuspension by sonication ( 60-s pulse , 70 amplitude ) followed by brief vortexing . After the second wash , samples were pelleted by centrifugation at 100 , 000 rcf for 1 hr and resuspended in conversion buffer by sonication and vortexing to a recombinant PrP concentration of 6 μg/ml for use in epitope mapping and sPMCA experiments . Epitope mapping of the cofactor and protein-only PrPSc PK-resistant cores was performed using mAbs 6D11 [61] and R2 [62] . Aliquots of identical purified samples were run on SDS-PAGE , transferred to PVDF membrane , and processed independently by Western blotting using trays and containers that had never been in contact with anti-PrP antibodies . For normal brain homogenate sPMCA , brain homogenates were prepared at 10% ( w/v ) in conversion buffer ( PBS containing 1% ( v/v ) Triton X-100 and cOmplete protease inhibitor cocktail ( Roche ) ) from healthy C57BL/6 mice , as described by Castilla et al . [5] . Homogenates were clarified by brief sonication ( F60 Sonic Dismembrator ( Fisher Scientific ) , three 5-s pulses at ~1 . 8 amplitude ) , followed by centrifugation at 500 rcf for 15 min . Clarified substrates were then seeded with 1/10th volume of purified PrPSc samples , and sPMCA carried out as described above , with 20-s sonication pulses every 30 min . For deglycosylated PrPC sPMCA , diglycosylated native PrPC was first purified from normal mouse brain and then fully deglycosylated by treatment with PNGase F ( New England Biolabs ) as described [63] . Deglycosylated PrPC sPMCA reactions [63] were seeded with 5% ( v/v ) of recombinant or brain-derived PrPSc and sonicated as described above for normal brain homogenate sPMCA . All centrifugation was done at 4°C . Samples were prepared for deuterium exchange as described previously [32] with the following modifications . For each PrPSc species , converted PMCA cocktail was washed twice with nOG wash buffer ( 1% n-octyl-beta-D-glucopyranoside ( Anatrace ) , 150 mM NaCl , 8 . 3 mM Tris pH 7 . 2 ) by centrifugation at 100 , 000 rcf for 1 h , with resuspension by 60 s of sonication at 70 amplitude followed by brief vortexing . After the second wash , samples were pelleted by centrifugation at 100 , 000 rcf for 1 hr and resuspended in a volume of mock labeling buffer ( 150 mM NaCl , 8 . 3 mM Tris , pH 7 . 2 ) by sonication and vortexing immediately prior to the initiation of deuterium exchange . Deuterium exchange was initiated by the addition an equal volume of labeling buffer ( D2O containing 150 mM NaCl , 8 . 3 mM Tris , pH* 7 . 2 , where pH* is the pH meter reading without taking into account the hydrogen isotope effect ) and samples were incubated at room temperature ( 22°C ) . During the last 30 min of labeling , samples were pelleted by centrifugation at 100 , 000 rcf . Labeling buffer was removed and the samples were quenched on ice for 2 min with ice-cold quench buffer [0 . 8% formic acid , 6 . 4 M guanidine hydrocholride , 150 mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) ( Pierce ) ] . Quenched samples were diluted with 3 volumes of ice-cold acid diluent ( 0 . 8% formic acid , 16 . 6% glycerol ) , transferred to chilled autosampler microvials , frozen on crushed dry ice , sealed and stored at -70°C until analysis . For all PrP samples , including those described below , Western blotting of quenched material was performed and confirmed the presence of 1–2 μg recombinant PrP per sample vial . Deuterium exchange and quenching of normally folded , α-helical recombinant PrP ( α-PrP ) was performed as described above for cofactor and protein-only PrPSc , with the following modifications . Recombinant PrP was resuspended to 1 . 0 mg/ml in water and an equal volume of 2x labeling buffer ( D2O containing 300 mM NaCl , 16 . 6 mM Tris , pH* 7 . 2 ) was added to initiate deuterium exchange . Thirty minutes prior to quenching , an aliquot of the labeling reaction was placed at 4°C to replicate the temperature change experienced by PrPSc samples during centrifugation . Deuterium-labeled α-PrP was then quenched on ice for 2 min by the addition of 1 . 25 volumes of ice-cold quench buffer containing 700 mM TCEP . To the quenched samples was added 1 . 30 volumes of ice-cold acid diluent prior to aliquoting into autosampler microvials and freezing on dry ice , as described above . Equilibrium-deuterated samples were prepared by resuspension of recombinant PrP in 2 . 5 or 6 . 0 M guanidine hydrochloride solution containing a 1:1 molar ratio of protons:deuterons by mixing appropriate quantities of H2O , D2O , guanidine HCl and guanidine ( D6 ) DCl ( Cambridge Isotope Laboratories , Andover , MA ) . Deuterium exchange was allowed to proceed for 72 hours at room temperature ( 22°C ) prior to quenching as described above for α-PrP . To estimate the fraction of non-specifically aggregated PrP that could potentially co-sediment during the purification of PrPSc samples for DXMS , mock-seeded PMCA reactions were performed using PMCA cocktail supplemented with brain-derived cofactor or water . After 24 h of PMCA , the mock-seeded PMCA reactions were purified by ultracentrifugation as described above for DXMS samples and the fraction of the input PrP that was recovered as non-specifically aggregated , insoluble PrP was quantified by Western blot . Measurement of deuterium incorporation by LC-MS was performed as described previously [32] , with the following modifications . Samples were loaded onto the in-line immobilized fungal protease XIII column at a rate of 60 μl/min , allowed to digest for 3 min , and then pushed onto the in-line immobilized pepsin column at a rate of 20 μl/min . Peptides were collected during pepsin digestion on a C18 trap column ( Michrom MAGIC C18AQ , 0 . 2x2 ) preceding the C18 resolving column ( Michrom MAGIC C18AQ , 0 . 2x50 ) . All measurements were made on an Orbitrap Elite mass spectrometer ( Thermo Fisher Scientific ) , and data was analyzed as described previously [32] . Rat anti-mouse IgM-conjugated Dynabeads ( Life Technologies ) were washed according to the manufacturer’s protocol and coated with mAb 15B3 ( Prionics ) at 5 μg per 10 μl beads with gentle mixing at room temperature for 2 h . Coated beads were washed three times to remove unbound antibody and stored at 4°C for no more than one week prior to use . Converted cofactor or protein-only PrPSc PMCA cocktail was washed twice with nOG wash buffer as described above for the preparation of samples for DXMS . After the second wash , samples were collected by centrifugation at 100 , 000 rcf for 1 hr at 4°C and the pellet was gently washed with Prionics homogenization buffer ( Prionics ) . Samples were then centrifuged at 100 , 000 rcf for 10 min at 4°C and the supernatant discarded . The pellet was resuspended in Prionics homogenization buffer by sonication ( 30-s pulse , 70 amplitude ) and vortexing to a final concentration of ~60 ng/μl PrP . For each PrPSc sample , ~250 ng PrP was added to 0 . 5 ml Prionics IP buffer ( Prionics ) and to this was added 10 μl of beads that were either coated with 15B3 or uncoated . Samples were allowed to interact with the beads at room temperature for 4 h with gentle mixing , followed by two washes with Prionics IP buffer and resuspension of the beads in 2x SDS-PAGE loading buffer . Samples were incubated at 95°C for 10 min , briefly centrifuged to concentrate the beads and the supernatant was collected for analysis by Western blot . Cofactor and protein-only PrPSc were generated by sPMCA supplemented with synthetic plasmalogen PE ( Avanti Polar Lipids ) as the sole cofactor , as described previously [64] . Converted PMCA cocktail was digested with PK as described above and quenched by the addition of PMSF ( Sigma Aldrich ) to 2 mM final concentration . Digested samples were washed twice with nOG wash buffer , as described above , and then twice with water to remove residual buffer components and detergent . Samples were then resuspended in water to a concentration of ~140 ng/μl PrP by vortexing and a 15-s sonication pulse . 10 μl of the resulting sample was spotted onto a glass slide and allowed to dry under a stream of nitrogen . Once dry , another 10 μl was spotted on top of the first and again allowed to dry under nitrogen . Spotted samples were scanned using a WITec CRM200 Raman confocal light microscope , equipped with a 100x lens and a 514 nm argon laser with 45 mW output . An f/4 , 300 mm imaging spectrograph was employed with 2 exit ports and a 600 lines/mm grating , with a Peltier-cooled CCD , 1340 x 100 pixel format , and a 16-bit camera controller . The fiber optic connecting the microscope with the spectrograph was 50 μm in diameter . Spectra were acquired using an integration time of 8 s , with two hardware and two software accumulations per shot and a spectral resolution of 4 cm-1 . Presented spectra are averages of 20–30 shots . In each figure , the baseline was adjusted to zero and data points were joined with a smoothed line in Microsoft Excel . Although spectral normalization was not possible , data were collected with the same instrument at the same time from highly concentrated films of protein , and it can be expected that intensity differences between samples originate in structural differences between conformers . All experiments involving mice in this study were conducted in accordance with protocol supa . su . 1 as reviewed and approved by Dartmouth College’s Institutional Animal Care and Use Committee , operating under the regulations/guidelines of the NIH Office of Laboratory Animal Welfare ( assurance number A3259-01 ) .
|
A key prediction of the prion hypothesis is that autocatalytic , misfolded PrPSc molecules should be highly infectious . Various recombinant PrPSc conformers are able to self-propagate in vitro , yet paradoxically only some of these conformers possess significant levels of specific infectivity in bioassays . Here we use two closely-matched autocatalytic recombinant PrP conformers that share the same origin but differ by >105-fold in specific infectivity to study the molecular basis of prion infectivity . We show that infectious and non-infectious autocatalytic recombinant PrP conformers have subtle structural differences , and that GPI-anchored PrP substrate molecules can only adopt the infectious PrPSc conformation . We conclude that post-translational modifications of host PrPC molecules play a critical role in restricting the range of recombinant PrPSc conformers that are biologically infectious .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
A Structural and Functional Comparison Between Infectious and Non-Infectious Autocatalytic Recombinant PrP Conformers
|
Hepadnaviruses , including hepatitis B virus ( HBV ) , a highly relevant human pathogen , are small enveloped DNA viruses that replicate via reverse transcription . All hepadnaviruses display a narrow tissue and host tropism . For HBV , this restricts efficient experimental in vivo infection to chimpanzees . While the cellular factors mediating infection are largely unknown , the large viral envelope protein ( L ) plays a pivotal role for infectivity . Furthermore , certain segments of the PreS domain of L from duck HBV ( DHBV ) enhanced infectivity for cultured duck hepatocytes of pseudotyped heron HBV ( HHBV ) , a virus unable to infect ducks in vivo . This implied a crucial role for the PreS sequence from amino acid 22 to 90 in the duck tropism of DHBV . Reasoning that reciprocal replacements would reduce infectivity for ducks , we generated spreading-competent chimeric DHBVs with L proteins in which segments 22–90 ( Du-He4 ) or its subsegments 22–37 and 37–90 ( Du-He2 , Du-He3 ) are derived from HHBV . Infectivity for duck hepatocytes of Du-He4 and Du-He3 , though not Du-He2 , was indeed clearly reduced compared to wild-type DHBV . Surprisingly , however , in ducks even Du-He4 caused high-titered , persistent , horizontally and vertically transmissable infections , with kinetics of viral spread similar to those of DHBV when inoculated at doses of 108 viral genome equivalents ( vge ) per animal . Low-dose infections down to 300 vge per duck did not reveal a significant reduction in specific infectivity of the chimera . Hence , sequence alterations in PreS that limited infectivity in vitro did not do so in vivo . These data reveal a much more complex correlation between PreS sequence and host specificity than might have been anticipated; more generally , they question the value of cultured hepatocytes for reliably predicting in vivo infectivity of avian and , by inference , mammalian hepadnaviruses , with potential implications for the risk assessment of vaccine and drug resistant HBV variants .
Hepadnaviruses including hepatitis B virus ( HBV ) , the causative agent of B-type hepatitis in humans , are small enveloped hepatotropic DNA viruses that replicate by reverse transcription ( [1]; for reviews see [2] , [3] ) . Related viruses have been found in some mammals ( orthohepadnaviruses ) , e . g . woodchucks ( WHV ) , and in selected bird species ( avihepadnaviruses ) , e . g . ducks ( DHBV ) and herons ( HHBV ) . In general , each of these viruses can infect only a limited range of hosts [4] , [5] . Nonetheless , all hepadnaviruses share a similar genome organization and replication strategy . Upon infection the about 3 kb relaxed circular ( RC ) DNA genome in virions is converted into covalently closed circular ( ccc ) DNA that acts as transcription template . The greater-than-genome length pregenomic ( pg ) RNA serves as mRNA for the capsid protein and the reverse transcriptase ( P protein ) , and via specific P-RNA interactions is encapsidated and reverse transcribed into progeny RC-DNA ( for review , see [6] ) . New nucleocapsids can either recycle the RC-DNA to the nucleus for cccDNA amplification , or be secreted after envelopment by the surface , or envelope , proteins ( for review , see [7] ) that are translated from subgenomic RNAs . N terminal addition to the small surface protein ( S ) , a transmembrane protein , of the PreS1 plus PreS2 domains in orthohepadnaviruses , or of a single about 160 amino acid ( aa ) PreS domain in avihepadnaviruses , creates the respective large envelope proteins ( L ) . PreS2 plus S form the middle ( M ) protein in the mammalian viruses . Notably , the preS/S open reading frames ( ORFs ) overlap entirely with the P ORF ( Figure 1A ) . Hepatotropism is largely attributable to the requirement for hepatocyte-enriched transcription factors [8]; host range appears to be mainly controlled at the early steps of virus attachment and entry as inferred from the ability of several non-infectable cell lines , even of heterologous species origin [9] , to produce infectious virions when transfected with cloned hepadnaviral DNA . Infection , however , usually requires primary hepatocytes . Because none of the natural hosts is an established experimental animal , few different species have been investigated; these include hepatocytes from humans and tupaias [10] , [11] , or woodchucks ( reviewed in [12] ) ; Pekin ducks ( Anas platyrhynchos var . domestica ) provide the only feasible source for hepatocytes of a genuine avihepadnavirus host ( reviewed in [13] ) . Even greater restrictions apply to in vivo experiments . Collectively the few published studies suggest the existence of a species barrier that prevents efficient transmission to other than closely related hosts . HBV can be transmitted to chimpanzees [14] but not [15] , or only extremely inefficiently [16] , to baboons; WHV was not infectious for ground squirrels [17]; and DHBV does not infect chicken [18] . Notably , though , this barrier is not absolute; for instance , Beechey Ground Squirrel HBV ( GSHV ) was infectious for woodchucks and chipmunks [19] , though not mice or rats [20] . In vitro , host tropism appears more relaxed as shown by the relatively efficient infectibility of tupaia hepatocytes [10] , [11] by HBV and woolly monkey HBV ( WMHBV; [21] ) , or of duck hepatocytes by crane hepatitis B virus [22] . While these examples attest to an as yet poorly understood complexity of hepadnaviral host tropism ( reviewed in [5] ) , the inability of HHBV to establish detectable infection in ducks is solidly established [23] , [24] . Though numerous cellular HBV binding proteins have been reported ( reviewed in [4] ) , the receptors for orthohepadnaviruses are not known; the major candidate receptor for DHBV , carboxypeptidase D ( CPD; formerly gp180 [25] ) , is ubiquitously expressed and highly conserved between bird species; hence it can explain neither tissue nor host tropism . Glycine decarboxylase was proposed as a co-receptor [26] yet whether its coexpression with CPD renders non-infectable cells susceptible to DHBV infection was not reported . On the virus side , by contrast , a pivotal role of the PreS domains of the L proteins for infection is firmly established ( Figure 1B ) . Anti-PreS antibodies can block HBV [27] and DHBV infection [28]–[31] , and many mutations within aa 1 to 75 of HBV PreS1 [32] and aa 2 to 115 [33] of DHBV PreS ( D-PreS ) abrogate infectivity , as does prevention of PreS myristoylation . In addition , short C proximal PreS regions are essential for nucleocapsid envelopment and hence for infectious virion formation ( reviewed in [4] , [7] ) . The PreS regions display the highest sequence divergence among hepadnaviruses ( 42% aa identity for DHBV versus HHBV PreS [He-PreS] , compared to 84% for the respective S proteins ) , suggesting that PreS also harbors elements governing host range . In a landmark study , Ishikawa and Ganem [23] laid the foundation for the current model of host-range determinants in hepadnaviruses , using primary duck hepatocytes ( PDH ) and DHBV as homologous , and HHBV as a heterologous virus . HHBV was capable of establishing a low level infection in PDH ( 100 to 1 , 000-fold less efficiently than DHBV ) . Infectivity of an envelope-deficient ( env− ) HHBV genome increased upon pseudotyping with chimeric HHBV envelope proteins containing the entire D-PreS domain or its segments 1–90 and 22–108 , but not 43–161 . It was concluded that the common segment 22–90 , or possibly its subsegment 22–37 ( T . Ishikawa , personal communication ) , governs the species-specificity of avihepadnaviral infection . In apparent accord , D-PreS peptides comprising aa 2–41 can efficiently block PDH infection by DHBV [34] . Although a corresponding He-PreS peptide was also inhibitory , PreS aa 22–37 ( here termed short form ) , or possibly 22 to 90 ( long form; Figure 1B , D ) , are often referred to as the host-determining region ( HDR ) of avihepadnaviruses [4] , [34] , [35] . Similar pseudotype data with L protein chimeras between HBV and WMHBV on human hepatocytes [36] suggested that essentially the same holds for the orthohepadnaviruses , although recent in vitro infection studies [37] , [38] using hepatitis delta virus ( HDV ) , an RNA virus that depends on the envelope of HBV to form infectious particles [39] , are difficult to reconcile with such a simple model ( see Discussion ) . Most importantly , the role of the supposed HDRs in a true in vivo hepadnavirus infection , requiring autonomously replicating viruses that inheritably encode the chimeric envelope proteins , has never been assessed . The aim of this study was to provide such information . We could , indeed , confirm an increased PDH infectivity of HHBV pseudotypes carrying selected D-PreS segments , including aa 22–37 . However , reciprocal replacement of this sequence in an autonomous chimeric DHB virus had no negative impact on infectivity for duck hepatocytes or ducks . Most surprisingly , a DHBV chimera in which the entire proposed HDR was replaced by the corresponding heron virus sequence ( Du-He4 ) , was poorly infectious for duck hepatocytes yet was nearly as , if not equally infectious for ducks as wild-type DHBV . Thus although the heterologous PreS sequence limited in vitro infectivity it did not do so in vivo . These results question the value of isolated hepatocytes to reliably predict in vivo host range of avian , and by inference , mammalian hepadnaviruses , with consequences for the risk assessment of human HBV variants that seem to inevitably emerge during current nucleos ( t ) ide analog based therapeutic regimens of chronic hepatitis B; notably , due to the overlapping ORF arrangement , such mutations in P also affect the envelope genes .
Pseudotypes can not spread in vivo . We therefore aimed at introducing the supposedly relevant PreS segment exchanges into autonomous chimeric viruses . If a specific PreS segment determined the duck tropism of DHBV , its replacement by heron virus sequence should decrease infectivity for ducks; the segments replaced in the respective DHBV chimeras were D-PreS 22–37 ( Du-He2 ) , 22–90 ( Du-He4 ) , and 38–90 ( Du-He3 ) , and in an additional chimera not considered here in detail , 91–161 ( Du-He7 ) . Concerns for the in vivo experiments were the prevalence of congenital DHBV infections in domestic ducks ( 10–20% of the ducks used here ) , and the high in vivo infectivity of DHBV [40] such that even minute contaminations of the inocula might result in wild-type virus infection . For easy distinction , all recombinant viruses , including the DHBV16 ( Genbank accession: K01834 ) derivative DHBVm1 serving as reference wild-type virus , were therefore genetically tagged by unique restriction sites ( Figure S1A ) . PreS segment swapping generates mutant P proteins and could affect genomic cis-elements [6] , [41] . However , replication competence and envelopment were not negatively affected in the three chimeras ( and neither in Du-He7 ) . In transfected LMH cells , all produced similar , if not higher ( DuHe3 , Du-He4 ) , amounts of intracellular RC- and double-stranded linear ( dsL ) progeny DNA ( Figure 2A ) , and of secreted , bona fide enveloped particles . This was demonstrated by the strongly ( at least 20-fold ) increased labeling of the particle-borne virus genome in the presence versus absence of detergent during endogenous polymerase reactions ( Figure 2B ) . These reactions rely on the ability of the genome-linked ( endogenous ) P protein to incorporate exogeneously added dNTPs into the incomplete genome yet only if dNTP access to the nucleocapsid lumen is not blocked by an intact envelope . Titers of transfection-derived virions were quantified as viral genome equivalents ( vge ) ; multiplicities of infection ( MOI ) were operationally defined as vge per inoculated cell . PDH were inoculated at MOI 100 with the recombinant chimeras , and recombinant DHBVm1 and HHBV as controls . Infectivity and the kinetics of infection ( Figure 3 ) were scored by Southern blotting and by quantitative PCR ( qPCR ) [42] of intracellular viral DNAs , and of secreted vge between day 1 and day 15 post inoculation ( p . i . ) ; genotypes were confirmed by restriction analysis ( Figure S1B ) . Du-He2 behaved in all aspects indistinguishably from the reference virus DHBVm1 . Already at day 3 p . i . both gave strong cccDNA and ssDNA signals; while the RC DNA signals could originate , in part , from the inocula they were clearly enhanced compared the other three samples . Signal intensities increased about 10-fold to day 7 , and further to day 15 . Both Du-He3 and Du-He4 produced 10- to 30-fold weaker signals until day 7 p . i . ; thereafter the difference decreased to 4- to 6-fold , probably because DHBVm1 and Du-He2 had already reached a plateau . A similar around 10-fold reduction in PDH infectivity was observed for Du-He7 which had also not displayed any obvious defects in replication or envelopment in transfected LMH cells ( data not shown ) . Signals for wt-HHBV remained two to three orders of magnitude below those from DHBVm1; however , infection was detectable by formation of small amounts of cccDNA and an about 10-fold net increase in intracellular and secreted viral DNAs from day 7 to day 15 p . i . . Thus , as expected if D-PreS 22–90 was important for the host tropism of DHBV , its exchange , or that of D-PreS 38–90 ( Du-He3 ) or 91–161 ( Du-He7 ) , by He-PreS sequence markedly reduced infectivity for PDH whereas D-PreS 22–37 could be replaced without any negative impact; from this one would conclude that D-PreS 22–37 does not contribute to host discrimination . This was surprising given the reported stimulatory effect of D-PreS 22–37 on HHBV pseudotype infectivity for PDH and prompted us to repeat and extend the original pseudotyping experiments [23] . Pseudotypes were generated by complementation of env− HHBV and DHBV genomes with the envelope proteins of wild-type DHBV and HHBV , and with chimeric HHBV PreS/S proteins carrying D-Pres 1–90 and 22–108 [23] , 22–37 and 22–90 , or the PreS domain from crane HBV ( CHBV1; Genbank accession: AJ441111 ) which is reportedly infectious for PDH [22] . Pseudotype yields were all similar , as determined by quantitation of envelope-protected viral DNAs . Autologously pseudotyped DHBV was applied at MOI 1000 , 100 and 10 to calibrate PDH infection efficiency , all others were used at MOI 1000 . Supernatant from cells transfected with the env− DHBV genome plus a vector encoding only DHBV S ( PreS− ) served as background control . Cells were harvested at day 7 p . i . and analyzed for intracellular virus DNA by Southern blotting ( Figure 4 ) . Though the RC-DNA signals were less informative ( see above ) , the levels of the other viral DNA forms , including cccDNA , differed drastically , confirming in part the previously reported results . Autologously pseudotyped env− DHBV gave still a clear cccDNA signal at MOI 10 , slightly exceeding that obtained with HHBV PreS/S at MOI 1000; hence , as reported [23] the heron virus envelope conferred an about 100- to 1 , 000-fold reduced PDH infectivity . Infectivity of HHBV env− was rescued most effectively by a complete DHBV envelope ( D-PreS/S ) yet , even in combination with HHBV S , the entire D-PreS domain and its segments 22–37 , 22–108 , as well as CHBV-PreS led to significant enhancements whereas D-PreS 1–90 and D-PreS 22–90 did not . These data were corroborated by qPCR of the intracellular replicative intermediates ( RIs ) although the accuracy of the low values was probably limited by contributions from inoculum derived RC-DNA . We cannot offer a trivial explanation for the discordant data regarding D-PreS 1–90; the sequence of the expression vector was correct , and enveloped particle formation was as efficient as with the other constructs . Most importantly , however , these data confirmed that D-PreS 22–108 , and in particular D-PreS 22–37 , increased infectivity of the HHBV envelope for PDH nearly as efficiently as the entire D-PreS domain . Thus in this test system introducing the D-PreS 22–37 segment into the HHBV envelope did affect host-specific infectivity whereas its reciprocal removal from the DHBV envelope in Du-He2 did not . To test in vivo infectivity of the chimeric viruses , three 3 day old ducklings each were inoculated with 108 vge of recombinant chimeras Du-He2 , Du-He3 , and Du-He4; 7 . 5×107 vge are estimated to be sufficient to deliver virus to about 10% of liver cells in 1-day-old ducklings [40] . Controls included recombinant wt-HHBV , DHBV16 and DHBVm1 , and serum DHBV3 ( Genbank accession: DQ195079 ) from a congenitally infected duck ( 109 vge ) . Between one and three animals from each group , except those inoculated with HHBV , showed detectable surface protein antigenemia at day 7 p . i . , and remained viremic until the end of the experiment at week 15; intrahepatic virus DNA levels at this time-point were comparable , regardless of the specific PreS sequence ( data not shown ) . Genotypes were confirmed by restriction analysis , excluding wild-type virus as a source of infection in the animals inoculated with the chimeras ( Figure S2A ) . None of the animals showed any overt pathogenicity ( no major weight reduction or retarded growth [43] , no gross abnormalities of the inner organs ) . Two persistently Du-He4 infected females were kept for more than one year , without significant decline in viremia . Hence even the poorly PDH infectious chimeras Du-He3 and Du-He4 were able to establish persistent infections in ducks; notably though , no signs of infection were detectable in 6 animals inoculated under identical conditions with Du-He7 ( not shown ) . To test whether Du-He4 could horizontally and vertically be transmitted , three ducklings ( #4/17 to #4/19 ) were inoculated with Du-He4 positive serum from one of the above described animals ( 108 vge per animal ) ; serum containing DHBVm1 served as control ( animals #4/4 and #4/6 received 108 vge , animal #4/5 105 vge ) . Viremia ( Figure 5A ) was already high at day 6 p . i . in all animals inoculated with 108 vge; viral load in animal #4/5 was initially low but increased to similar levels by week 7 . Du-He4 viremia in animal #4/19 declined , yet the two others maintained very high viral loads ( around 1010 vge/ml ) over 15 weeks . For vertical transmission two persistently Du-He4 infected females were crossed with a DHBV negative drake . Six off-spring embryos were sacrificed 5 to 9 days before hatch ( d 28 ) , and viral DNA in the livers was analyzed for Du-He4 genotype ( Figure S2B ) and quantitated by qPCR . All samples were positive for Du-He4 DNA ( Figure 5B ) . Hence chimera Du-He4 could horizontally and vertically be transmitted in ducks . The disparate in vitro versus in vivo results for Du-He4 prompted us to thoroughly exclude that we had underestimated its in vitro or overestimated its in vivo infectivity . Improper modification of the chicken LMH cell-derived Du-He4 virions did not account for poor PDH infectivity because Du-He4 virions sequentially passaged through two ducks were similarly attenuated ( Figure 6A and 6B ) ; this makes it also unlikely that the fast in vivo spread of Du-He4 ( see below ) resulted from quickly arising adaptive mutations not detectable by restriction genotyping . A significant replication defect of the Du-He4 genome in PDH that went unnoticed in LMH cells was ruled out by pseudotyping the env− DHBV genome , and an analogous env− Du-He4 genome , with either wild-type D-PreS/S , or with Du-He4 PreS/S . The chimeric envelope was up to ten-fold less efficient in rescueing infectivity of both env− genomes whereas replication of the Du-He4 genome was only slightly retarded at day 3 p . i . but reached DHBV-like levels at day 7 p . i . in both settings ( Figure 7 ) . In the previous in vivo experiments , Du-He4 had reached wt-DHBV-like viral loads ( Figure 4A ) but slowed-down kinetics of spread would not have been detected . We therefore monitored the spread of both viruses , in liver and serum , at shortly spaced intervals . Ten ducklings each received 108 vge of serum-derived Du-He4 ( from duck #4/17 ) or DHBVm1 ( from duck #4/6 ) . One animal each was sacrificed at days 1 , 2 , 3 and 5 p . i . , and two animals each at days 7 , 10 and 14 p . i . . Intrahepatic viral DNA was analyzed by Southern blotting ( Figure 8A ) , and the course of viremia by DNA dot blot of serum samples ( Figure S3 ) ; both data sets were quantitated by phosphorimaging ( Figure 8B ) . For DHBVm1 , intrahepatic virus DNA became clearly detectable at day 5 , then rapidly rose to maximal values at day 10 , and slightly declined . Du-He4 produced a very similar profile , except that signals at the early timepoints ( days 2 to 5; see Figure 8A ) were even stronger . Viremia was monitored in individual serum samples from all animals alive at a given time-point ( i . e . ten for day 1 , two for day 14 ) . The mean and maximal values were again higher and displayed a faster rise for the chimera than for the wild-type virus . While the viremia values are statistically significant , especially for the early time points covering samples form several ducks , each liver data is derived from an individual duck and thus should not be overinterpreted to indicate superiority of one virus over the other . However , if the about 10-fold lower PDH infectivity of Du-He4 had translated into a 10-fold lower percentage of initially infected cells , i . e . about 1% instead of 10% [40] , the appearance of detectable levels of the chimeric virus should have occurred with a substantial delay . For wild-type DHBV in young ducklings , the mean doubling time in the percentage of infected hepatocytes has been estimated to be about 16 h [40] , thus three to four times this time ( 2 to 3 d ) would have been required for Du-He4 to catch up with the wild-type virus , provided replication per se proceeded at similar rates . As no such delay was observed , wild-type and chimeric virus appear to spread with comparable kinetics . In these experiments the viral loads showed less variation with Du-He4 than with wild-type DHBV where such variability has been seen also in other studies [44] . Because DHBV is wide-spread in commercial flocks , including the animals in the current study , mother-to-egg transmission of anti-DHBV antibodies , usually directed against the envelope [29] , could reduce or prevent viral spread in some of the ducklings [30] . To test whether Du-He4 represented an a priori immune escape variant we preincubated Du-He4 , or DHBVm1 containing sera with a previously characterized DHBV neutralizing duck antiserum obtained by DNA immunization with a DHBV16 PreS/S expression vector and shown to react predominantly with PreS rather than S [28] , or for control with normal duck serum . Aliquots containing 106 vge Du-He4 were inoculated into two ducklings each . In the test animals ( #7/1 , #7/2 ) , viral loads as determined by qPCR ( Figure S4 ) remained close to or at the detection limit of our non-nested qPCR assay ( about 100 vge per PCR reaction , corresponding to 105 vge/ml per 1 µl serum sample ) between day 4 and day 11 p . i . ; by day 28 , serum from one animal was essentially negative for viral DNA , the other had developed a low titered viremia of 1 . 6×105 vge/ml . In contrast , both control animals ( #7/3 , #7/4 ) developed DHBs antigenemia , easily detectable by day 11 and day 28 p . i . , and viremia which peaked at 2 . 2 and 6 . 5×108 vge/ml on day 11 . The antiserum also strongly reduced infectivity in one ( #7/6 ) , and partially in the second ( #7/5 ) DHBVm1 inoculated duck , whereas the control animals ( #7/7 , 8 ) developed antigenemia and viremia at a similar rate and to similar levels as the Du-He4 controls ( Figure S4 ) . These data suggest that antibodies raised against the wild-type DHBV surface proteins can also neutralize the chimeric virus; in addition , they demonstrated that 106 vge of Du-He4 could establish in vivo infection in ducks . One wild-type DHBV genome has been proposed to be infectious in neonatal ducks [40]; for the human virus , the doses required to infect 50% of the inoculated chimpanzees ( CID50 ) have been reported to range between 3 and 169 vge [45] . It was therefore of interest to see whether replacement of the supposed HDR in Du-He4 , though principally compatible with in vivo infectivity ( see above ) , negatively affected specific infectivity . To this end , 6 ducklings each were inoculated with DHBVm1 ( from duck #2/7 ) and Du-He4 ( from duck #2/20 ) viremic sera , diluted with normal duck serum to contain 107 , 105 . 5 , 104 , and 102 . 5 ( = 300 ) vge per dose . Development of antigenemia and viremia was monitored for up to 42 days p . i . ; at the end of the experiment , animals were sacrificed and the livers were removed for determination of intrahepatic viral DNA . Analysis of the serum samples revealed that at the highest dose , 6 of 6 animals from the DHBVm1 and 5 of 6 animals from the Du-He4 group had developed antigenemia and viremia , easily detectable by day 12 p . i . and persisting to end of the experiment; for the 105 . 5 inocula , 5 of 6 animals in each group were positive for serum markers of infection . Slight differences became apparent at the lowest doses ( Figure 9A ) ; with the 104 vge inocula 3 of 6 Du-He4 inoculated versus 5 of 6 DHBVm1 inoculated birds developed antigenemia and viremia ( titers in all cases around or slightly above 109 vge/ml ) ; with the 300 vge inocula , numbers were none out of 6 versus 2 out of 6 . Formal ID50 values as calculated from the fraction of animals positive for serum PreS antigen at the end of the experiment by the Spearman-Kärber method [46] were 103 . 3 ( about 2 , 000; 95% confidence interval 102 . 2–103 . 8 ) vge for the wild-type virus and 104 . 5 ( about 30 , 000; 95% confidence interval 103 , 6–105 , 4 ) vge for Du-He4; the Reed-Münch method [47] yielded ID50 values of 103 for wild-type DHBV and 104 vge for Du-He4 . Though only a trend given the limited number of animals , this was compatible with a somewhat reduced efficiency of Du-He4 in establishing an overt in vivo infection at low inoculum doses . However , in their livers all animals inoculated with 300 vge of Du-He4 , as well the wild-type DHBV inoculated ducks that had remained negative for serum markers of infection , contained very low but Southern blot detectable amounts of viral DNA ( Figure 9A , bottom panel ) . As determined by qPCR , the livers of the serum antigen positive ducks contained at least 120 , and usually around 200 vge/cell , as did the liver of animal #21/21 from the wild-type virus group that was negative for serum antigen but had a low level viremia ( 7×106 vge/ml versus about 1 . 5×109 vge/ml for the antigenemic ducks ) . Livers from all other animals contained between 0 . 01 and 0 . 20 vge/cell . Though the accuracy of these numbers is limited , even the lowest values were at least five times above background . Most importantly , extrapolated to the entire liver ( liver weights 30–50 g at the time the animals were sacrificed ) and assuming 7×108 cells per gram liver [48] , [49] , even 1 vge per 100 cells translates into a total of 2×108 vge per animal , indicating a 6 log amplification of the input virus . Because according to these data all 6 ducklings receiving 300 vge of Du-He4 were initially infected , as were the four non-antigenemic wild-type DHBV inoculated ducks , a formal ID50 cannot be calculated; however , it must be well below 300 vge . Thus although a low dose infection with Du-He4 may be more effectively contained by the host than infection with wild-type DHBV , heterologous replacement of the PreS region supposedly critical for the duck tropism of DHBV had no major negative impact on specific infectivity in ducks .
An absolute requirement for the infection experiments was that the chimeras be able to produce intact enveloped virions . None of the chimeras reported here displayed any obvious defects , as most compellingly demonstrated by their in vivo infectivity . Nonetheless , chimeras Du-He3 and Du-He4 , but not Du-He2 , were poorly infectious for PDH; these in vitro data , performed on the same batch of PDH for the different viruses and reproduced several times , are statistically highly significant ( P<0 . 001 , Student's t-test ) . Hence replacement of D-PreS 22–90 , 38–90 , and also 91–160 ( Du-He7; not shown ) , by He-PreS segments substantially reduced infectivity for PDH but replacement of D-PreS 22–37 did not . Simply interpreted , DHBV specific sequences downstream of aa 37 were required for efficient infection of duck hepatocytes but the sequence 22–37 was not . Thus in this test system , D-PreS 22–37 had no species-specific impact although the same sequence significantly enhanced PDH infectivity of the HHBV pseudotypes; in other words , reciprocal exchanges of PreS segment 22–37 did not produce reciprocal phenotypes . Other data sets also elude a simple interpretation . D-PreS 22–37 and 22–108 enhanced PDH infectivity of HHBV pseudotypes as reported [23] , yet 22–90 , and 1–90 , did not; pseudotype formation with these latter constructs was as efficient as with the others , and numerous of the pseudotypes prepared and tested in parallel ( Figure 4 ) clearly scored positive . Thus there is no linear relationship between the proportion of a given PreS sequence and infectivity for hepatocytes from the homologous host . This suggests that consecutive PreS segments act as multimodular three-dimensional , not linear , structures that may undergo complex and dynamic intra- and intermolecular interactions . Given the limited [50] or absent knowledge on the structure of PreS and its relevant cellular partners , attempts to define a linear sequence segment as critical for host range determination therefore appear to be doomed . This conclusion is supported by in vitro infectivity data obtained with hepatitis delta virus particles carrying chimeric hepadnavirus envelopes [51] . Replacement of the N terminal HBV PreS1 segment 1–40 by that from WMHBV reduced , indeed , HDV infectivity for human hepatocytes , in accord with a previous HBV pseudotype study [36]; however , infectivity for spider monkey cells was likewise reduced , although in vivo WMHBV can infect the woolly monkey related spider monkeys but not chimps [52] . Even more surprisingly , reciprocal introduction of human virus PreS1 1–40 into a WMHBV envelope did not enhance infectivity for human cells yet even strongly boosted infectivity for spider monkey hepatocytes [37] . These data cannot be reconciled with the simple concept of a defined sequence segment in PreS acting as a host range determinant . The most compelling counterevidence , however , comes from the in vivo infection experiments in this study . In ducks , Du-He4 established high-titered persistent infections; the kinetics of intrahepatic spread after inoculation with 108 vge/duckling were comparable and , in no case , slower than with wild-type DHBV ( Figure 8 ) . At inoculum doses of 10 , 000 or 300 vge/animal , wild-type DHBV seemed to be somewhat more efficient regarding the fractions of birds developping antigenemia and viremia ( Figure 9 ) , with formal ID50 values of around 104 for Du-He4 vs . 103 for wild-type DHBV; however , the small number of animals imposes the same limitation on such an interpretation as the seemingly better performance of the chimera at the 108 vge inocula . Most importantly , all animals inoculated with only 300 vge and negative for serum markers of infection contained intrahepatic viral DNA . Because the absolute copy numbers determined by qPCR were low ( 350 to 2 , 000 copies per PCR reaction ) the estimates of between 0 . 2 and 0 . 01 vge per cell may not be very accurate . Clearly , however , even one vge per 100 cells amounts to a total of more than 108 vge per duck liver [49] , demonstrating a 106-fold , or higher , amplification of the inoculum . Thus 300 vge of Du-He4 , as well as of wild-type DHBV , were sufficient for an at least initial infection of 6 out of 6 ducklings . This prevented calculation of a formal ID50 value; as an approximation to the upper limit of the actual ID50 it may be assumed that the next lower dilution would have infected only a fraction of animals; in our experiments , this would correspond to an input of 10 vge per animal , close to the value reported for wild-type DHBV [40] . Whether or not the chimeric genomes accumulated mutations , and if so which , upon multiple passages in the duck is currently being investigated using serial samples , yet maintainance of the low PDH infectivity phenotype after passage in ducks ( Figure 6 ) as well as preliminary sequencing data make it unlikely that rapidly occurring adaptive mutations [53] caused the high in vivo infectivity . Thus , Du-He4 is a fully functional duck-infectious avihepadnavirus although nearly half its PreS sequence is from a virus that does not infect ducks and although the heterologous sequence clearly reduced infectivity for duck hepatocytes . Thus the predictive significance of in vitro infectivity data for in vivo infectivity is strongly limited . The high in vivo versus low in vitro infectivity of Du-He4 points to major differences between the two settings even though the primary hepatocytes originated from the same species as the experimental animals . In fact , even for wt-DHBV the amounts of virus required for PDH infection appear high given that few DHB virions can establish infection in ducks ( [40] and this study ) , and this difference seems to be further magnified in the Du-He3 and Du-He4 chimeras . Possibly , the expression profiles of cell factors required for , or restricting infection [5] , such as those controlling retroviruses [54] , [55] , differ between hepatocytes in culture versus in the liver , and the chimeras are more sensitive to such alterations . Alternatively , the intact liver may have a higher capacity for as yet undefined processing steps in the envelope that increase infectivity , or its tissue architecture provides for particularly intimate contacts between hepatocytes and incoming virus that outbalance a reduced affinity of the chimeric preS regions for the unknown receptors; it has even been proposed that liver sinusoidal endothelial cells , not hepatocytes , are the initial in vivo target cells [56] , yet the physiological relevance of this model is unclear . For several viruses , spread is particularly efficient via direct cell-to cell contacts , such as filopodial bridges [57] or the recently described nanotubes [58]; such processes may also contribute to the efficiency of hepadnaviral spread in vivo yet not , or less so , in cultured hepatocytes . Another conceivable option for the high in vivo infectivity of Du-He4 was that the chimera had a selective advantage relating to preexisting ( possibly present in some of our animals ) , or rapidly inducible antibodies that neutralize the wild-type virus [31] but not the chimera . Our neutralization experiments argue against but do not fully exclude this possibility . The antiserum used reacted on Western blots virtually exclusively with the L protein but not the S protein of DHBV , as reported [28] , and the chimeric Du-He4 L protein was recognized with similar efficiency . Though in accord with its neutralizing activity against Du-He4 , it is not clear whether the fraction of Western blot reactive antibodies in the antiserum , or possibly others directed against the native L and/or S protein , are the relevant ones for neutralization , and the fraction of these relevant antibodies might differ for wild-type DHBV and Du-He4 . Notably , Du-He3 and Du-He4 infectivity for PDH was not as strongly reduced ( by around 10-fold ) as that of HHBV ( >100-fold ) , which might suggest that there is a threshold in vitro infectivity that is compatible with in vivo infection . Along this line , approximate quantitation of the PDH infectivities of recombinant Ross' Goose ( RGHBV; Genbank accession: AY494848 ) , snow goose ( SGHBV; Genbank accession: AF110997 ) , and stork hepatitis B ( STHBV; Genbank accession: AJ251937 ) viruses showed reductions of 8 to 20-fold for the goose viruses and >100-fold for STHBV ( K . Dallmeier and M . Nassal , unpublished data ) . While , to our knowledge , no in vivo infection data have been published for these viruses , a virus almost identical to RGHBV isolated from Mandarin ducks was indeed infectious for ducks [59] , and STHBV would be predicted not to infect ducks . Thus an about 10-fold reduction in the efficiency of virus uptake , which is probably what is reflected by the data obtained on isolated hepatocytes , may not be rate-limiting for virus spread in vivo . However , not any virus displaying an intermediate ( as opposed to a drastic ) reduction in in vitro infectivity is able to establish in vivo infection . Of 6 ducklings inoculated with 108 vge of chimera Du-He7 none developed signs of infection although the chimera produced similar amounts of enveloped particles in transfected LMH cells and had a similar in vitro infectivity as Du-He3 and Du-He4 . The same was observed with a DHBV chimera in which the core gene was replaced by that from HHBV ( Dallmeier and Nassal , unpublished ) . Similarly , a natural avihepadnavirus isolate from an Ashy-headed sheldgoose ( ASHBV; Genbank accession: NC_005890 ) with reduced but clearly detectable infectivity for PDH [59] failed to infect 6 out of 6 ducklings . Hence there seems to be no simple correlation between in vitro and in vivo infectivity . Given these various uncertainties , testing in vivo infectivity appears currently as the only reliable way of addressing hepadnaviral host range . This conclusion may be relevant for the risk assessment of vaccine escape and drug resistant variants of human HBV . De novo infection with such mutants could seriously impair efficacy of vaccination and restrict the options for chronic hepatitis B treatment [60] . Some resistance-conferring mutations seem to lower replication capacity [61] , yet their effect on infectivity is not known . Notably , for HBV the discrepancy between highly efficient in vivo infection [45] and the requirement for MOIs of 10 or higher , and often for polyethylenglykol as an additional facilitator [51] , [62] , [63] , in the current cell culture infection systems is even more pronounced than for DHBV . Hence cell culture data may not be dependably extrapolated to the behaviour of such viruses in vivo . Even if our in vivo titration data were interpreted as evidence for a somewhat reduced specific infectivity of Du-He4 , this was manifest only at inoculum doses of 104 vge or less; given that HBV titers in humans frequently exceed 109 vge per ml , such a dose would be contained within 10 nl of blood , only a fraction of what is commonly transferred in needlestick injuries with even the finest ( 25G ) needles [64] . Hence fundamental questions in hepadnavirus infection biology will need to be reassessed , not only regarding the identity of the still obscure cellular factors involved but also with respect to the seemingly well understood role of the viral envelope proteins in mediating infectivity and tissue and host tropism .
Virus expression vectors were based on plasmid pCD16 , carrying a CMV promoter controlled 1 . 1× DHBV16 genome [65]; the tagged derivative DHBVm1 contained three nt substitutions that created new restriction sites for BsmBI and MroI ( Figure S1 ) . Chimeric DHBV genomes encoded the following aa replacements: Du-He2 , D-PreS 22–37 by He-PreS 22–40; Du-He3 , D-PreS 38–90 by He-PreS 38–92; Du-He4: D-PreS 22–90 by He-PreS 22–92 ( Figure 1C ) . Vector pCHHBV4 was based on HHBV4 ( Genbank accession: NC_001486 ) [24] . Envelope-deficient ( env− ) DHBV and HHBV genomes corresponded to those described earlier [23] , [33] , [66] . PreS/S expression vectors were all controlled by the CMV promoter; the CHBV PreS construct contained a segment from CHBV1 [22] , kindly provided by H . Sirma and H . Will . Intracellular replicative intermediates ( RIs ) from transfected LMH cells were analyzed by Southern blotting of DNAs from cytoplasmic extracts; secreted virions were enriched by D-PreS-specific immunoprecipitation , then subjected to endogenous polymerase assay conditions [67] , [68] with or without detergent . Alternatively , viral DNA in enveloped particles was enriched by pronase plus nuclease treatment [33] and used for quantitative PCR ( qPCR ) determinations . Recombinant viruses were obtained analogously; pseudotypes were generated by cotransfection of vectors encoding an env− viral genome and the desired PreS/S proteins . Viral titers , as viral genome equivalents per ml ( vge/ml ) , were determined by DNA dot blot or by qPCR . Primary duck hepatocytes [69] were usually incubated overnight at the desired MOI with recombinant virus , or with viremic duck serum . For analysis of intracellular viral DNAs including cccDNA , total DNA was prepared by SDS lysis [66] . Two to three day old Pekin ducklings were injected into the foot vein with the indicated amounts of recombinant virus or viremic serum . Viremia was determined as described above for cell culture supernatants . To monitor the kinetics of viral spread in the liver , one or two animals each were sacrificed at each time-point , and liver DNA was analyzed by virus specific Southern blot . To estimate specific infectivities , six ducklings each were inoculated with viremic sera diluted to contain 107 , 105 . 5 , 104 , and 102 . 5 vge per dose of either the reference virus DHBVm1 or Du-He4 . Serum samples were collected for up to 42 days , and PreS antigemia was monitored by Western blotting; viral loads in selected PreS antigen positive animals were determined by qPCR . At the end of the experiment , the animals were killed and their livers were removed to monitor intrahepatic viral DNA by Southern blotting and by qPCR . The fractions of animals positive for serum PreS antigen were used to calculate formal ID50 values using the Spearman-Kärber [46] and the Reed-Muench method [47] . All animal experiments were approved by the local authorities ( Regierungspräsidium Freiburg , project G02/36 ) and performed in compliance with German animal welfare legislation at a registered facility of the University Hospital Freiburg under veterinary supervision .
|
Hepatitis B virus ( HBV ) associated liver disease is a leading cause of death worldwide . Host range restrictions limit experimental HBV infections largely to chimpanzees or isolated human hepatocytes . A narrow host range is shared by the animal hepadnaviruses , e . g . from ducks ( DHBV ) and herons ( HHBV ) ; HHBV does not infect ducks though it can establish a low-level infection in cultured duck hepatocytes . Host tropism is thought to be mediated by the PreS domain of the large viral envelope protein , because certain duck virus PreS segments introduced into the envelope of spreading-incompetent HHBV pseudotypes enhanced infectivity for duck hepatocytes . Expecting that reciprocal exchanges in DHBV would negatively impact duck tropism , we generated chimeric DHBVs in which the PreS regions in question are derived from HHBV and which are autonomously spreading-competent; this allowed us to directly compare their infectivity for duck hepatocytes and ducks . Surprisingly , even the chimera with the largest portion of HHBV sequence was as infectious for ducks as authentic DHBV; in vitro infectivity , however , was substantially reduced . These unexpected differences question the value of cultured hepatocytes to reliably predict in vivo infectivity of avihepadnaviruses and , by inference , also that of vaccine escape and therapy resistant HBV variants .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/mechanisms",
"of",
"resistance",
"and",
"susceptibility,",
"including",
"host",
"genetics",
"virology/animal",
"models",
"of",
"infection",
"virology/host",
"invasion",
"and",
"cell",
"entry"
] |
2008
|
Heterologous Replacement of the Supposed Host Determining Region of Avihepadnaviruses: High In Vivo Infectivity Despite Low Infectivity for Hepatocytes
|
Alternative pre-mRNA splicing plays fundamental roles in neurons by generating functional diversity in proteins associated with the communication and connectivity of the synapse . The CI cassette of the NMDA R1 receptor is one of a variety of exons that show an increase in exon skipping in response to cell excitation , but the molecular nature of this splicing responsiveness is not yet understood . Here we investigate the molecular basis for the induced changes in splicing of the CI cassette exon in primary rat cortical cultures in response to KCl-induced depolarization using an expression assay with a tight neuron-specific readout . In this system , exon silencing in response to neuronal excitation was mediated by multiple UAGG-type silencing motifs , and transfer of the motifs to a constitutive exon conferred a similar responsiveness by gain of function . Biochemical analysis of protein binding to UAGG motifs in extracts prepared from treated and mock-treated cortical cultures showed an increase in nuclear hnRNP A1-RNA binding activity in parallel with excitation . Evidence for the role of the NMDA receptor and calcium signaling in the induced splicing response was shown by the use of specific antagonists , as well as cell-permeable inhibitors of signaling pathways . Finally , a wider role for exon-skipping responsiveness is shown to involve additional exons with UAGG-related silencing motifs , and transcripts involved in synaptic functions . These results suggest that , at the post-transcriptional level , excitable exons such as the CI cassette may be involved in strategies by which neurons mount adaptive responses to hyperstimulation .
Alternative pre-mRNA splicing expands protein functional diversity by directing precise nucleotide sequence changes within mRNA coding regions . Splicing regulation often involves adjusting the relative levels of exon inclusion and skipping patterns as a function of cell type or stage of development . In the nervous system , such changes affect protein domains of ion channels , neurotransmitter receptors , transporters , cell adhesion molecules , and other components involved in brain physiology and development [1 , 2] . There is growing evidence that various biological stimuli , such as cell excitation , stress , and cell cycle activation , can induce rapid changes in alternative splicing patterns [3 , 4] . These phenomena suggest that splicing decisions may be altered by communication between signal transduction pathways and splicing machineries , but such molecular links and mechanisms are largely unknown . The focus of the present study is to gain insight into these mechanisms using primary neurons as the model system . Splicing decisions take place in the context of the spliceosome , which is the dynamic ribonucleoprotein machinery required for catalysis of the RNA rearrangements associated with intron removal and exon joining [5–7] . Spliceosomes assemble on pre-mRNA templates by the systematic binding of the small nuclear ribonucleoprotein particles , U1 , U2 , and U4/U5/U6 , which leads to splice site recognition and exon definition . Thus , splicing decisions can be profoundly influenced by the strength of the individual 5′ and 3′ splice sites and by auxiliary RNA sequences that tune splice site strength via enhancement or silencing mechanisms . RNA binding proteins from the serine/arginine-rich ( SR ) and heterogeneous nuclear ribonucleoprotein ( hnRNP ) families play key roles in recognizing auxiliary RNA sequences from sites within the exon ( exonic splicing enhancers or silencers; ESEs or ESSs , respectively ) or intron ( intronic enhancers or silencers; ISEs or ISSs , respectively ) . Despite numerous RNA motifs that have been functionally characterized as splicing enhancers or silencers , the mechanisms by which these motifs function in combination to adjust splicing patterns are not yet well understood [8 , 9] . The broad significance of this problem is highlighted by the observation that nearly 75% of human pre-mRNAs with multiple exons undergo alternative splicing [10] . In addition , numerous point mutations in splice sites or splicing control elements have been linked to genetic disease [11] . Insights into the molecular basis for induced effects on alternative splicing have come from a variety of biological systems . Cell excitation by KCl-induced membrane depolarization in the pituitary cell line , GH3 , has been shown to induce skipping of several cassette exons , including the STREX exon of the BK slo channel , as well as the N1 and C1 exons of the glutamate NMDA receptor NR1 subunit ( GRIN1 gene ) [12] . Note that the CI cassette exon is called exon 21 in other studies . Calcium/calmodulin kinase ( CaMK ) IV was implicated in these effects based on the loss of excitation-induced exon skipping by a specific inhibitor of this enzyme , whereas the reverse effect was observed by expression of the catalytic subunit of the enzyme . In this study , a 54-nucleotide region of the 3′ splice site of the STREX exon was identified and found to be sufficient to mediate similar effects when attached to a heterologous exon . In another study , application of the drug pilocarpine induced skipping of the EN exon of clathrin light chain B , the N1 exon of c-src , and the CI exon of GRIN1 transcripts in the rat hippocampus and/or cortex of living animals [13] . In the T cell–derived cell line JSL1 , phorbol ester treatment has been shown to induce cell excitation together with increased skipping of exon 4 of CD45 pre-mRNA . These effects are dependent upon a 60-nucleotide splicing silencer within exon 4 , and hnRNP L has been implicated as a factor involved in mediating these effects [14 , 15] . In another study , phorbol ester has been shown to increase CD44 v5 exon inclusion in a manner that was dependent upon Sam68 and the MAPK/ERK pathway [16] . In HeLa cells , the cellular response to heat shock was found to generate potent and widespread splicing silencing , and SRp38 was identified as the protein factor responsible for these effects [17] . A general silencing effect on pre-mRNA splicing by SRp38 was also observed in cells undergoing mitosis [18] . Recent studies have implicated hnRNP A1 as a factor that mediates induced changes in alternative splicing in response to osmotic stress . NIH 3T3 cells that have undergone osmotic stress have a relative increase in cytoplasmic A1 compared to nuclear A1 , and this effect is associated with the phosphorylation of carboxy terminal residues that extend into the M9 region [19 , 20] . HnRNP A1 has been shown to regulate a variety of alternative splicing events through the recognition of RNA sequences related to the consensus binding motif , UAGGGA/U [11 , 21 , 22] . Two RNA binding domains and a glycine-rich region are involved in sequence-specific recognition , and a sequence near the carboxyl terminus , M9 , is required for nuclear import . HnRNP A1 has been shown to function as a substrate for protein kinase C ( PKC ) , protein kinase A ( PKA ) , and casein kinase [23] . HnRNP A1′s ability to silence splicing of CD44 v5 exon was reduced by the overexpression of a constitutively active form of the mitogen-activated protein/ERK kinase kinase 1 , and by the small GTP-binding protein Cdc42 in NIH 3T3 and erythroleukemia cell lines [24] . Together , these results suggest that the functional properties of hnRNP A1 could be altered in complex ways through signaling pathways in response to various biological stimuli . The CI cassette exon of the GRIN1 gene was chosen for this study based on its known hnRNP A1–mediated exon silencing mechanism , which involves two exonic UAGGs and an intronic GGGG motif [25] . Here we develop a system to study the response of the CI cassette to cell excitation in neurons of primary cultures , and we utilize this system to investigate the role of the UAGG-related exon silencing code in the induced exon-skipping response . We extend this with complementary biochemical approaches to test for binding- and expression-level effects on hnRNP A1 protein in nuclear extracts prepared from resting and excited cultures . We also take advantage of the primary neuronal cultures to explore the role of endogenous ion channels in the induced exon-skipping phenomenon using antagonists of the NMDA receptor and other physiologically relevant calcium channels . An expanded analysis of exon-skipping responsiveness of endogenous transcripts involved in synaptic and non-synaptic functions is also presented .
Primary neurons , due to their responsiveness and plasticity , should represent a useful model system to study activity-induced changes in alternative splicing , but to date , such systems have not been well developed . In this study , we have developed low-density cortical cultures from the embryonic rat forebrain as an experimental system to address the molecular mechanisms of induced changes in alternative splicing using cell-specific and biochemical approaches . Upon dissociation and plating in culture , the postmitotic neurons extend processes , establish synaptic connections , and become electrically active , whereas glial cells in these cultures play supportive roles . Initially , changes in endogenous splicing patterns were monitored in the cortical cultures both as a function of KCl-induced depolarization , and cell differentiation using the CI cassette exon as a readout . The cortical cultures at 6 , 8 , 12 , and 14 d in vitro ( DIV ) after plating were treated for 24 h with media containing 25 or 50 mM KCl , since these conditions are known to induce membrane depolarization in these cultures . Changes in the percent exon inclusion values were calculated as the difference between the value in the 50 mM KCl sample and the mock-treated control ( ΔEI values ) . Each stage of differentiation of the cultures showed similar trends in which CI cassette exon inclusion decreased ( ΔEI , −32% to −36%; Figure 1 , lanes 1–12 ) as a function of the KCl treatment . Based on the consistent response of each exon in this time frame , we used the cultures to ask whether KCl treatment produces transient or stable changes in the splicing patterns of the endogenous RNA transcripts . If transient changes in transcriptional or post-transcriptional events affect splicing factors involved in these mechanisms , we would expect to see a reversal to the basal splicing pattern with time after removal of the stimulus . Reversibility was tested in the 12-d cultures , again using the CI cassette splicing events as a readout . After treatment with KCl , the cultures were changed to fresh medium without KCl , and cells were harvested 0 , 6 , 12 , and 24 h later . KCl-induced changes in the splicing of the CI cassette exon progressively lessened with time to approximately basal levels after KCl washout ( ΔEI , −8%; Figure 1 , lanes 13–24 ) . For comparison , we assayed for the effects on alternative exon 9 ( E9 ) of the GABAA receptor γ2 ( GABARG2 gene ) in the same cultures , since E9 is known to undergo silencing by the polypyrimidine tract binding protein , but not hnRNP A1 ( Figure S1A ) . Interestingly , E9 inclusion was responsive to the KCl treatment , but this effect was maximized at longer differentiation times of the cultures , and , in contrast to the CI cassette , E9 inclusion increased ( ΔEI , 23% , lanes 1–12 ) . These effects were also largely reversible after KCl washout ( lanes 13–24 ) . Thus , the KCl-induced effects on CI cassette and E9 inclusion are largely reversible , and suggest a role for mRNA homoeostasis in allowing for the readjustment of splicing patterns to basal levels . To experimentally identify nucleotide sequence requirements for activity-induced alternative splicing in neuronal and glial cell types in the cortical cultures , we turned to cell-specific promoters that were previously characterized in transgenic mice . For this purpose , we assessed the ability of the alpha CaMKII promoter to drive neuron-specific expression in rat cortical cultures , since this promoter was shown previously to drive forebrain-specific expression in excitatory neurons of transgenic mice [26] . Fluorescent reporters were constructed in which the expression of enhanced yellow fluorescent protein ( EYFP ) was driven by various portions of the CaMKII promoter ( Figure 2 , constructs 1 and 2 ) . Construct CaMKII_279 EYFP contained an 8 . 5-kilobase ( kb ) promoter region known to restrict expression of Cre to the forebrain of transgenic mice . For cloning purposes , a shorter promoter region was also tested , which contained the start site–proximal 2 . 2-kb promoter fragment ( CaMKII_22 EYFP ) . To determine expression patterns in the cortical cultures , confocal microscopy was used to measure the extent of overlap between EYFP expression and antibody staining with TRITC-labeled NeuN or glial fibrillary associated protein ( GFAP ) , which served as molecular markers for neurons and glia , respectively ( Figure 2B ) . For comparison , we also constructed a fluorescent reporter in which DsRed was fused to a 2-kb region of the Gfa2 promoter , Gfa2 DsRed ( Figure 2 , construct 3 ) [27] . The Gfa2 promoter was shown previously to drive expression predominantly in glial cells of transgenic mice [28] . As controls , the EYFP and DsRed coding sequences were also fused to the human cytomegalovirus immediate early ( CMV ) promoter ( Figure 2 , constructs 4 and 5 ) . The strong neuron specificity of expression of the CaMKII promoters in these cultures is shown as the percent overlap expression of each construct with NeuN or GFAP antibody staining ( Figure 2A , top right ) . For each sample , a minimum of 200 cells with EYFP fluorescence were examined for TRITC-labeled NeuN staining . Whereas , the largest ( 8 . 5 kb ) region of the promoter ( CaMKII_279 ) showed 96% overlap with NeuN and 3% overlap with GFAP , similar results were observed with the 2 . 2-kb region ( CaMKII_22; 95% overlap with NeuN and ∼5% overlap with GFAP ) . In contrast , the Gfa2 promoter was preferentially expressed in glia in these cultures ( 94% ) , and some overlap with neurons was observed ( 22% ) in agreement with previous analysis in transgenic mice [28] . A summary of these results is also shown graphically ( Figure 2C ) . As a reference , EYFP expressed from the CMV promoter ( CMV_EYFP ) showed 64% overlap with NeuN and 21% overlap with GFAP , whereas the DsRed expressed from the same promoter ( CMV_DsRed ) showed 54% overlap with NeuN and 26% overlap with GFAP . Due to its mixed expression profile , the CMV promoter was not used for further analysis . Representative examples of the overlap expression of the two CaMKII EYFP reporters with NeuN is shown in Figure 2B . Overlap expression is indicated by yellow fluorescence in the nuclei of samples stained with TRITC-labeled anti-NeuN ( CaMKII EYFP reporters , anti-NeuN panels ) , and the lack of overlap expression in samples containing the anti-GFAP antibody is clearly shown ( anti-GFAP panels ) . The pattern of expression for samples containing the Gfa2 DsRed reporter characteristically showed overlap of DsRed fluorescence with FITC-labeled anti-GFAP , but not with FITC-labeled anti-NeuN ( Gfa2 DsRed panels ) . To confirm the neuron- versus glial-specific expression patterns of the promoters , we co-transfected the CaMKII_22_EYFP and Gfa2-DsRed plasmids into the same cortical cultures and determined overlap expression . No greater than 22% overlap would be expected based on the least specific of the two promoters , Gfa2 . The observed overlap ranged from 14% to 19% ( raw values ) , in contrast to 82% to 84% from the generally expressed CMV promoters . After normalizing these values to 100% transfection efficiency for the CMV samples , the co-expression of CaMKII and Gfa2 promoters ranged from 17% to 23% ( corrected values ) . These results are in good agreement with the promoter selectivity shown above . We next asked how these promoters affect splicing patterns in established neuronal ( PC12 and ST15A ) and muscle myoblast ( C2C12 ) cell lines , in comparison to the primary cortical cultures . Similar CI cassette exon inclusion levels were observed from the two promoter types in the neuronal and non-neuronal cell types ( ΔEI , <3 . 6% , Table 1 ) . Thus , the increase in CI cassette exon inclusion arising from the promoter fusions in primary cortical cultures must reflect the properties of the neurons and glial cells in the cultures , and cannot be explained by the promoters alone . Next we took advantage of the CaMKII and Gfa2 splicing reporters to characterize the KCl-induced effects on CI cassette exon silencing as a function of cell type in the primary cortical cultures . Wild-type CI cassette splicing reporters driven by the cell-specific promoters CaMKII_22 CI wt0 and Gfa2 C1 wt0 were transiently expressed in cortical cultures for 18 h , followed by treatment with KCl for 6 and 24 h . When transcripts were expressed from the CaMKII_22 promoter , CI cassette exon inclusion decreased in response to the KCl treatment , and this effect was consistent in neurons at all time points examined ( Figure 3A , lanes 7–12 ) . Similar effects were observed for the endogenous CI cassette exon when mock-transfected cultures were treated in parallel ( lanes 1–6 ) . Optimal effects were reached after 24 h for both endogenous and CaMKII_22-expressed transcripts . In contrast , when transcripts were expressed from the Gfa2 promoter , CI inclusion showed little or no change at the 6- and 24-h time points ( lanes 13–18 ) . Based on the cell specificity of the CaMKII promoter shown above , we conclude that neurons play the predominant role in the KCl-induced changes in alternative splicing in these cultures . Furthermore , the trend and magnitude of the effects for the exogenous CI cassette exon inclusion are in good agreement with those of the endogenous transcripts . As a corollary to this experiment , we measured the number of CaMKII_EYFP-expressing neurons that fail to exclude trypan blue . In mock- and depolarization-treated cultures , we observed that the viability of the neurons was high in both the mock-treated ( 94% viable ) and depolarization-treated ( 93% viable ) samples ( Figure 3B ) . Thus , we conclude that the RT-PCR results shown above ( Figure 3A , lanes 7–12 ) largely reflect splicing pattern changes in viable neurons . To further validate these results , we transferred the E9 splicing reporter into the CaMKII and Gfa2 promoter constructs and tested their response to KCl treatment in the cortical cultures ( Figure S1B ) . When expression was driven in neurons , E9 inclusion increased similarly to that of the endogenous transcripts ( ΔEI , 17% and 15% , respectively ) , but this was not the case when expression was driven in glial cells ( ΔEI , 3% ) . Thus , for a second test exon , the promoter system recapitulated the splicing response in neurons as observed for the endogenous transcripts . Based on the well-behaved exon-skipping response of the CI cassette exon to KCl-induced depolarization in this system , we focused on this exon to identify nucleotide sequences involved in the response . A three-component code of two exonic UAGGs and an intronic GGGG motif , which was recently shown to mediate CI cassette exon silencing [29] , served as the starting point for these experiments . Splicing reporters with point mutations in combinations of the three silencing motifs were subcloned under the control of the CaMKII_22 promoter , and assayed in the cortical cultures ( Figure 4A ) . In the absence of KCl treatment , the CI wt0 reporter with intact UAGG and GGGG motifs showed strong silencing activity ( exon skipping ) in the primary neurons in agreement with our previous analysis in cultured cell lines . Compared to the wild-type substrate , point mutations in a single silencer increased exon inclusion ( Figure 4A , lanes 1 , 4 , 7 , and 13 ) , and multiple point mutations generated complete or nearly complete exon inclusion ( lanes 10 and 16 ) . In results new to this study , the UAGG and GGGG silencer motifs were found to be important for the depolarization-induced effects on exon skipping in the primary neurons . A complete disruption of KCl-induced exon skipping was associated with point mutations in two or more of the silencing motifs ( substrates E17 and T8; lanes 10–12 and 16–18 ) . In contrast , an increase in exon skipping occurred in parallel with the depolarization treatment for the wild-type substrate ( ΔEI , −17%; lanes 1–3 ) . Point mutations in single motifs showed a similar trend in which the KCl-induced effects on exon skipping were disrupted at various levels ( substrates E8 , E9 , and D0; lanes 4–9 and 13–15 ) . We next tested the role of exonic enhancer motifs in the same fashion , since these motifs function generally to antagonize exon silencing . The sequence , type , and position of the exonic enhancer motifs and their inactivating mutations are shown in Figure 4B . Of the six mutants , E2 , E3 , E4 , and E5 showed reduced basal levels of CI exon inclusion in neurons , and the response of these exons to KCl-induced depolarization was either similar to ( E6 and 5 m1 , lanes 34–39 ) or reduced ( E2 , E3 , E4 , and E5 , lanes 22–33 ) compared to the wild-type CI cassette ( lanes 19–21 ) . These results suggest that sequences outside of the UAGG motifs in the exon may also play a role in the induced splicing silencing response . To validate these results , we asked whether a multicomponent UAGG silencing motif code is sufficient to confer sensitivity to KCl-induced depolarization; we introduced UAGG motifs into exon 5 of the gene encoding the human Dip13 beta adapter protein ( DIP13B_HUMAN ) . This exon was chosen based on the use of algorithms that predicted strong 5′ and 3′ splice sites , a moderate number of exonic enhancer motifs ( n = 10 ) , and a lack of known exonic silencing motifs ( MaxEntScan , ACEScan , and ESEFinder ) . Another attractive feature was the clustered arrangement of predicted enhancer motifs that allowed for the insertion of UAGG motifs at multiple discrete positions . The region containing exon 5 and its flanking splice sites was cloned into a chimeric splicing reporter by replacing the middle exon of the previously described SIRT1a plasmid [29] , and the promoter was replaced with CaMKII_22 to direct expression in neurons ( Figure 5A , DIP13_E5 ) . When expressed in neurons , the DIP13_E5 splicing reporter was nearly insensitive to the excitation treatment ( ΔEI , 0%; Figure 5B , lanes 1–3 ) . In order to test the role of the silencing motif pattern in the response to excitation , we next introduced three exonic UAGG motifs and a 5′ splice site GGGG motif ( DIP_3aG ) . Introduction of the silencing motif pattern indeed generated an exon-skipping pattern in resting neurons ( 61% exon inclusion ) , and in the presence of KCl treatment to induce excitation , exon inclusion progressively decreased to 45% ( ΔEI , −16%; lanes 4–6 ) . In another variant , DIP_E2 , point mutations were introduced into the exon to destroy overlapping SC35 and ASF-SF2 motifs near the 3′ end of the exon ( Figure 5A ) . For DIP_E2 , exon inclusion decreased in resting neurons , and the response to excitation was similar to that observed for DIP_3aG ( Figure 5B , lanes 7–9 ) . Thus , we conclude that a multicomponent UAGG silencing motif code is sufficient to generate induced exon skipping in response to KCl-induced depolarization . We reasoned that factors known to be involved in splicing silencing via UAGG silencing motifs in resting cells would be likely candidates to mediate the induced splicing silencing response in excited neurons . Alternatively , excitation might weaken the role of factors involved in antagonizing this silencing mechanism . We initially focused on hnRNP A1 to probe for changes in regulatory factors , because our previous work demonstrated direct binding of this factor to the UAGG motifs in HeLa nuclear extracts , and because its silencing effect in vivo was dependent upon the intact motif pattern . To attempt to visualize whether changes in hnRNP A1 accompany KCl-induced depolarization , RNA-protein binding assays were used as sensitive readouts . In these experiments , 5-d cortical cultures were treated with KCl in the medium to induce depolarization , and cells were harvested for preparation of small-scale nuclear extracts ( see Materials and Methods ) . The time of KCl treatment was shortened from 24 h to 12 h based on the expectation that any excitation-induced alterations to splicing factors should precede the splicing pattern shifts themselves . Ultraviolet ( UV ) crosslinking was used to monitor direct protein binding to 5′[32P]-labeled RNA oligonucleotides under splicing conditions , and the crosslinked proteins were resolved by SDS-PAGE . A 22-mer containing three UAGG motifs showed increased crosslinking of a 34-kDa protein , suggestive of hnRNP A1 , in nuclear extracts prepared from KCl-induced versus mock-treated samples ( Figure 6A , lanes 1 and 2 ) . Increased crosslinking of the 34-kDa protein in the KCl-induced extracts was observed in eight different nuclear extract preparations , and the fold increase was in the range of 1 . 5- to 2 . 5-fold . To determine whether hnRNP A1 was responsible for the observed increase in crosslinking , a size-matched control RNA with point mutations in the UAGG motifs ( A1 mutant ) was tested in parallel reactions . Crosslinking of the 34-kDa protein to the A1 mutant oligo was abolished , demonstrating that intact UAGGs are essential for binding . To confirm the identity of the 34-kDa protein , three UV crosslinking reactions equivalent to that shown in lane 2 were combined and immunoprecipitated with monoclonal antibody 9H10 , which is specific for hnRNP A1 . These results showed that , relative to the control antibody , the 34-kDa crosslinked protein was partitioned into the pellet ( and depleted from the supernatant ) in the presence of 9H10 , supporting its identification as A1 ( Figure S2 ) . For comparison , parallel crosslinking reactions with distinct RNA oligos specific for human Tra2 and ASF/SF2 were also tested , but these proteins showed no detectable change in binding in extracts prepared from the KCl-induced versus mock-treated cultures . Enhanced hnRNP A1 binding was also observed for the full-length CI cassette exon in UV crosslinking reactions prepared with nuclear extracts from KCl-induced cultures ( Figure 6A . lanes 9 and 10 ) . Note that the full-length CI cassette exon tested here contains three UAGGs distributed throughout the 111-nucleotide exon , which is a less-concentrated motif arrangement relative to the A1 oligo . In contrast to hnRNP A1 , higher molecular weight proteins in the 57- to 100-kDa range in these samples serve as reference comparisons , since these displayed similar crosslinking levels in the two nuclear extracts ( lanes 9 and 10 , Ref bands ) . The identity of hnRNP A1 was verified by competition experiments with unlabeled A1 oligo , which reduced crosslinking to the 34-kDa protein in a concentration-dependent manner ( unpublished data ) . To verify the change in hnRNP A1 binding observed in the UV crosslinking assay , affinity selection was used as a complementary method . For this experiment , the full-length CI cassette exon with three UAGGs was subcloned upstream of the M3 hairpin to provide an affinity tag that was specific for the MS2-MBP fusion protein [30] . The CI cassette exon was subcloned without the M3 hairpin as a control . To assay for hnRNP A1 , one half of the eluted samples were separated by SDS-PAGE and immunoblotted with the 9H10 antibody . Parallel blots of the second half of the samples were developed with an antibody specific for ASF/SF2 . Binding of hnRNP A1 was dependent upon the presence of the M3 hairpin ( Figure 6B , lanes 13–16 ) , and an increase ( 1 . 5-fold ) in A1 binding to the M3_E18 substrate was observed in the KCl-induced versus mock-treated extracts ( lanes 13 and 14 ) . This difference in A1 was similar in the input samples prior to RNA binding ( lanes 11 and 12 ) . The increase in the level of A1 protein was similar to the input samples prior to RNA binding . In contrast , ASF/SF2 showed no change in binding to the M3_E18 substrate in the two samples , nor was there an observable difference in ASF/SF2 in the input samples ( lanes 17–22 ) . To confirm the difference in nuclear levels of hnRNP A1 , quantitative Western blotting was used to measure A1 levels in nuclear extracts from resting and excited cultures relative to known levels of recombinant A1 ( Figure 6C ) . These results verify that an increase in nuclear A1 protein levels in the cultures is associated with the depolarization treatment . We would expect cell excitation initiating at the cell membrane to communicate changes to splicing factors in the nucleus via calcium-mediated signal transduction pathways . In the absence of antagonists , the NMDA receptor channel opens in response to membrane depolarization and serves as the major conduit for calcium entry into neurons . Because NMDA receptors are functionally intact in neurons in these cultures , we wished to take advantage of this property of the culture system to ask whether NMDA receptors play a role in the induced effects on splicing observed here . To address this question , we expressed the CI wt0 splicing reporter in 5-d cortical cultures under the control of the CaMKII_22 promoter , and the cultures were pre-incubated with antagonists specific for the NMDA receptor before KCl-induced depolarization . MK801 and AP5 antagonists were used for these experiments because of their known specificity and effectiveness in inhibiting the NMDA receptor calcium channel in cortical cultures [31 , 32] . AP5 binds competitively to the extracellular glutamate ( NMDA ) site of the NR2 subunit , whereas MK801 binds to the channel itself , blocking ion flow . The exon-skipping response of the CI cassette exon was strong in the control ( mock treated ) samples ( Figure 7A , lanes 1–3 ) , but in the presence of either antagonist , this response was attenuated in a dose-dependent manner ( lanes 4–15 ) . Relative to the control sample ( ΔEI , −25% ) , exon skipping decreased in the presence of MK801 ( ΔEI , −10% ) and in the presence of AP5 ( ΔEI , −4% ) . In contrast , when cultures were pre-incubated with bicuculline , which is an antagonist of the GABAA receptor , only slight effects were observed on induced exon skipping ( ΔEI , −22%; lanes 16–21 ) relative to the control ( ΔEI , −25%; lanes 1–3 ) . Thus , to a first approximation , the effects of MK801 and AP5 implicate a role for the NMDA receptor in mediating the exon-skipping response of the CI cassette exon in this system . To extend these results , we used cell-permeable inhibitors to ask whether calcium-mediated signal transduction pathways downstream of NMDA receptors are involved in the induced splicing silencing response of the CI cassette exon . Relative to the mock-treated control , the exon-skipping response was strongly inhibited in the presence of 15 μM KN93 , which is an active site-based inhibitor of CaMK I , II , and IV ( Figure 7B , lanes 22–24 and 40–42 ) , and this effect was lessened when the inhibitor concentration was reduced to 3 μM ( lanes 37–39 ) . The control compound , KN92 , had little or no effect in these experiments ( P . An and P . J . Grabowski , unpublished data ) . We also found that the exon-skipping response was inhibited by H89 , which has been widely used as an inhibitor of PKA ( lanes 31–36 ) . In an attempt to confirm this effect , the inhibitor KT5720 , which is reported to have a higher specificity for PKA , was also tested . Interestingly , KT5720 had little or no effect at 2 . 5 and 10 μM concentrations ( lanes 25–30 ) , indicating that the inhibitory effect of H89 on splicing silencing observed here may involve a distinct pathway ( or pathways ) . To determine if additional calcium channels could play a role in the splicing response of the CI cassette exon , we tested the effects of specific antagonists of the AMPA/kainate receptor and voltage-gated calcium channels ( L- and N-type ) ( Figure S3 ) . Relative to control samples , the exon-skipping response was reduced , but not eliminated , in the presence of nimodipine ( antagonist of L-type calcium channels ) and conotoxin ( N-type calcium channels ) , whereas CNQX ( AMPA/kainate receptors ) showed little or no effect . Taken together , these results are consistent with roles for multiple calcium channels in the induced splicing response of the CI cassette exon in neurons . Next , we expanded the analysis of endogenous transcripts in the cortical cultures to test whether additional alternative cassette exons were responsive to depolarization , and if so , to determine whether exonic UAGG silencing motifs were required . We were also prompted to examine transcripts that encode protein components with synaptic functions , since the NMDA NR1 receptor and other calcium channels were implicated in the splicing response of the CI cassette exon from the experiments described above . Of 14 new exons tested , seven showed a significant exon-skipping response , five showed little or no response , and two showed an increase in exon inclusion ( Figure 8 ) . Strong exon-skipping responders were found to contain hnRNP A1–type silencing motifs in or near the responsive exon ( Figure 9 ) . Exon 3 of hnRNP H3 and exon 2 of the RNPS1 transcripts ( ΔEI , −32% and −25% , respectively ) contain either one exonic UAGG and a 5′ splice site GGGG motif ( H3 ) , or two exonic UAGGs ( RNPS1 ) . Exon 35 of PLCβ4 also showed a strong response ( ΔEI , −26% ) , and this exon contains two TAGA motifs , which reflects a four of six match to hnRNP A1 motifs reported for SMN2 exon 7 and human CD44 v5 exon [33] . These motifs cannot be the sole determinants of the response , however . Exon 4 of hnRNP H1 , which is identical in size ( 139 nucleotides ) and highly homologous to exon 3 of hnRNP H3 , showed little or no response ( ΔEI , −3% ) in the same samples . Interestingly , there is one exonic UAGG and a GGGG tetramer near the 5′ splice site of this exon , but it lacks a third silencing motif ( exonic GGGG ) that is found in exon 3 of hnRNP H3 . Moreover , constitutive exon 8 of the MEN1 transcript remains entirely unresponsive even though this exon contains two exonic UAGGs and a 5′ splice site proximal GGGG motif . Of the exons that showed little or no response , exon EN of clathrin light chain B ( CLCB ) was the most interesting . In a previous report , this exon showed increased exon skipping in the cortex and hippocampus of rats treated with pilocarpine [13] . In cortical cultures under the conditions tested here , however , exon EN showed a stable splicing pattern even though it is a well-skipped exon ( exon inclusion , 30% ) . Other alternative exons with a predominant skipping pattern that were unresponsive in these cultures include exon 21 of MAP4k4 , exon 8 of hnRNP A1 , exon N1 of GRIN1 , exon 7 of Chl , and exon 7 of Agrin . These exons , including EN of CLCB , lack hnRNP A1 motifs . Thus , the exon-skipping responsiveness of certain exons to depolarization treatment cannot be explained solely by a weakening of the general splicing machinery that causes all skipped exons to undergo stronger exon-skipping responses . Finally , two exons showed moderate increases in exon inclusion during the depolarization treatment . These included exon 9 of the GABAA receptor γ2 transcript , and the STREX exon of the c-slo transcript ( ΔEI , 18% , and 12% , respectively ) . These results suggest that some of the biochemical changes resulting from the depolarization treatment in neuronal cells can , in principle , increase exon inclusion .
At the level of pre-mRNA splicing , the CI cassette of the NMDA R1 receptor is one of several responsive exons that undergo an increase in exon skipping in response to neuronal excitation . Despite the importance of NMDA receptors in neuronal excitability at the level of ion channel function , the mechanisms by which stimuli are communicated to the nuclear splicing machinery to affect changes in alternative splicing are poorly understood . Moreover , methodologies to address these questions in primary neurons , in which NMDA receptors are functionally intact , have not been well developed . Here we describe a promoter-based expression assay that reports splicing changes in response to cell excitation with tight specificity for the neurons of cortical cultures derived from embryonic rat brain . Due to its neuron-specific expression in the forebrain regions of transgenic mice , the alpha CaMKII promoter was tested for its ability to provide a neuron-specific readout in a primary culture setting . In cortical cultures , full-length as well as truncated forms of this promoter displayed a strong preference for expression in neurons versus glial cells as demonstrated by co-localization of promoter-directed EYFP expression together with antibody staining for cell-specific markers . Moreover , these effects were dependent upon the primary culture context , since neuron-specific splicing patterns were eliminated when the same promoter-EYFP cassettes were expressed in neuronal versus non-neuronal cell lines . Using splicing reporters driven by the CaMKII promoter , we show that neuronal cells play the predominant role in the acute splicing response to excitation , and these splicing pattern changes reflect the trend and magnitude of those observed for the corresponding endogenous transcripts . In neurons of these primary cultures , the exon-skipping response of the CI cassette exon was mediated by combinations of UAGG-type silencing motifs . The general importance of a strong silencing motif pattern was confirmed by a gain-of-responsiveness due to the transfer of a similar UAGG-type silencing motif pattern into a constitutive exon . In this heterologous context , the transfer of multiple exonic UAGG motifs and a 5′ splice site proximal GGGG motif generated a basal-level exon-skipping pattern in the absence of depolarization , and exon skipping was further increased , over and above this basal level , as a function of depolarization . Furthermore , by expanding this analysis to fourteen alternatively spliced exons , several exons with strong exon-skipping responses were found to contain multiple hnRNP A1 silencing motifs consistent with a plausible role for hnRNP A1 in these effects . These results extend our previous work , which identified a multicomponent UAGG and GGGG motif code for basal-level splicing silencing of the CI cassette exon in established cell lines [29] . Additional studies have documented a similar exon-skipping response of the CI cassette exon to cell excitation in rat brain [13] and in GH3 pituitary cells , but in these previous studies nucleotide sequences required for the effects were not defined . The first RNA element shown to confer an exon-skipping response to KCl-mediated depolarization was defined for the STREX exon of the BK potassium channel . This element , termed CaRRE ( CaMKIV-responsive RNA element ) , was defined as a 54-nucleotide pyrimidine-rich region in the 3′ splice site of the STREX exon , and a pyrimidine-rich exonic RNA element , termed D56 , was also found to contribute to this splicing response in GH3 cells [12] . The CaRRE sequence was later refined by mutagenesis in primary mouse cerebellar cultures to comprise the sequence ( 5′ ) CACATNRTTAT ( 3′ ) [34] . We note that neither the CaRRE , nor the D56 element , are present in the 3′ splice site upstream of the CI cassette . Moreover , the UAGG-type silencing motifs defined here for the responsiveness of the CI cassette are absent from the STREX exon . These observations suggest that the exon-skipping responsiveness of these two exons is likely to involve distinct silencing mechanisms . The accompanying paper by Lee et al . [35] reports the identification of two types of CaRRE elements required for inducible exon-skipping in P19 cells , one of which is adjacent to a UAGG splicing silencer in the CI cassette . Whether the sequence requirements for inducible exon-skipping are more complex than these studies indicate or whether the differences in cell types or assays can account for seemingly different requirements is unknown . In efforts to extend the results of the RNA motif analysis , we used biochemical approaches to attempt to visualize any changes in the proteins that bind directly to the identified UAGG motifs . In UV crosslinking experiments , the binding of hnRNP A1 to UAGG motifs was found to increase in nuclear extracts from the excited versus resting cells ( 2-fold increase ) , and this was verified by affinity selection and immunoblotting . In contrast , no change in the binding of splicing factors ASF/SF2 and TRA2 to their respective RNA substrates was observed in parallel reactions using these nuclear extracts . In addition , the cell-permeable PKA inhibitor H89 , which was shown to block the excitation effects at the level of splicing , was also found to eliminate the increase in hnRNP A1 crosslinking in nuclear extracts that were excited in the presence of this compound . Taken together with the UAGG motif analysis , these results are in agreement with the prediction that signal transduction pathways communicate with splicing regulatory factors to cause induced effects on splicing , although at this point , many questions remain about how this communication is relayed to the nucleus , and exactly how the hnRNP A1 polypeptide is affected by the induction . Previous studies have documented an increase in the cytoplasmic distribution of A1 together with specific changes in the phosphorylation pattern upon osmotic stress [19 , 20] . Despite the apparent differences in the nature of the changes in hnRNP A1 during osmotic stress versus neuronal excitation , these previous studies and the results shown here , reinforce the idea that A1 is adept at responding to environmental signals . NMDA receptors are multisubunit protein complexes concentrated in the postsynaptic membrane where they regulate calcium influx into neurons in response to the binding of glutamate and glycine . Calcium influx through NMDA receptors is believed to trigger acute biochemical changes leading to long-term changes in synaptic strength , or plasticity [36] . In this study , we have taken advantage of the cortical culture system to explore the potential role of NMDA receptors in the CI cassette exon-skipping response to neuronal excitation . If the excitation regimen used here to induce the CI cassette exon-skipping response involves calcium influx through NMDA receptors , we would expect antagonists that specifically block the NMDA calcium channel to weaken the excitation effects on splicing . Consistent with this idea , we observed a substantial , dose-dependent block in the response of this exon-skipping event when the cultures were treated with the antagonists AP5 or MK801 . In contrast , when bicuculline was applied as an antagonist of the GABAA receptor , the exon-skipping response was only slightly weakened . The complete inhibition of the CI cassette exon-skipping response by KN93 is consistent with a role for calcium-mediated signaling in these effects . The effects shown here for KN93 in cortical neurons are in good agreement with previous studies from the Black laboratory , which have demonstrated a role , specifically , for CaMKIV in the exon-skipping response to excitation in GH3 cells and in cerebellum tissue from mouse CaMKIV knockout lines [12 , 34] . Because treatment with AP5 or MK801 largely , but not completely , blocks the CI cassette exon-skipping response to excitation , it is a viable possibility that calcium influx into neurons via AMPA receptors and/or L-type calcium channels might also be involved . Modular changes in a variety of proteins involved in synaptic function have been documented at the level of alternative splicing . A large-scale analysis of splicing pattern changes in the neocortex of Nova2 knockout mice has revealed that approximately 34 exons with splicing defects correspond to transcripts that encode synapse-related proteins [37 , 38] . Of these , the CI cassette exon was found to undergo increased exon skipping ( ∼45% increase ) in the Nova2 knockout mice , suggesting a role for Nova2 as a positive regulator of this exon in the mouse forebrain . In addition , NAPOR/CUGBP2 , hnRNP H , and to a lesser extent ASF/SF2 and SC35 , have been shown to function as positive regulators of the CI cassette exon at the level of splicing [29 , 39] . Along with the splicing silencing features of this control mechanism , a significant amount of combinatorial control of the CI cassette exon is indicated . Although it is not known how the nuclear levels of these additional factors are affected by neuronal excitation , an assumption of the present study is that selective alteration ( s ) to the balance of enhancement versus silencing underlies the net changes observed in the splicing patterns . To a first approximation , our results indicate that NMDA receptors are involved in the CI cassette exon-skipping response to KCl-induced depolarization in these cultures . This raises questions about the potential biological role of this exon-skipping response . The CI cassette exon itself has been shown to be important for localization of the NMDA R1 subunit of the receptor at the plasma membrane , and the role of this exon in intracellular signaling has been documented [40 , 41] . Previous studies have shown that NMDA receptor recruitment to synaptic sites can be substantially affected by neuronal activity . Rao and Craig found increased accumulation of NMDA receptors at synaptic sites in hippocampal cultures when neuronal excitation was chronically blocked by treatment with AP5 , although effects at the level of splicing were not detected [42] . In another study , the Ehlers laboratory showed that trafficking of NMDA receptors to synapses during activity blockade in cortical cultures was associated with spliced variants of the NMDA R1 receptor that contain the C2′ exon , but not the alternative exon , C2 [43] . The synaptic homeostasis model holds that , in order to stabilize excitability , neurons tend to downwardly adjust their synaptic strengths under conditions of chronic excitation , or upwardly adjust them under conditions of activity blockade [44] . This model , together with the results shown here , suggests that alternative splicing of the CI cassette exon might be involved in strategies by which neurons mount protective responses to an overload or lack of environmental stimuli . In agreement with the results shown here , a chronic KCl-induced depolarization of cortical cultures was shown to accompany a decrease in the NR1–1 ( +CI cassette ) , and an increase in the NR1–2 ( −CI cassette ) , mRNA isoform of the NMDA R1 receptor [45] . We have no direct evidence that a 2-fold increase in exon skipping of the CI cassette exon would cause a meaningful change in the number of NMDA receptors at the synapse . Nonetheless , it is apparent from other studies that subtle changes at the level of splicing can bring about important changes at the level of protein expression . In mice , a splicing defect in the sodium channel gene , Scn8a , was shown to produce viable animals with only 10% of correctly spliced mRNA in one genetic background , whereas a reduction to 5% of the correctly spliced mRNA in a different genetic background was lethal [46] . In this case , the cause of lethality was shown to be due to a mutation in the modifier gene , Scnm1 . Furthermore , rescue from lethality was achieved by transgenic expression of a wild-type form of Scnm1 , which brought about a 5% increase in the correctly spliced Scn8a mRNA . In another example , a bifunctional antisense oligonucleotide was introduced into fibroblasts derived from spinal muscular atrophy patients in an attempt to reprogram the splicing of exon 7 of SMN2 pre-mRNA . The SMN ( survival of motor neurons ) protein is a key component of nuclear gems , which are required for the biogenesis of small nuclear ribonucleoproteins . In the presence of the bifunctional oligonucleotide , exon 7 inclusion increased from 57% to 84% , and the percentage of gem-positive nuclei increased accordingly from 2%–3% to 13% [47] . Thus , subtle changes in splicing can be biologically important . In this study , E9 of the GABAA receptor γ2 subunit transcript showed an increase in exon inclusion in response to neuronal excitation , clearly opposite to that of the CI cassette . Based on the distinctive features of these two splicing control mechanisms at the biochemical level ( E9 involves silencing polypyrimidine tract binding protein ) , the opposite responses to excitation may be due to modifications of splicing factors that are unique for each exon . Alternatively , hnRNP A1 may be involved indirectly in the regulation of E9 inclusion . At the level of cellular function , GABAA receptors are the principal mediators of inhibitory neurotransmission , and the γ2 subunit plays important roles in clustering of GABAA receptors and the scaffold protein gephyrin at postsynaptic sites [48 , 49] . Although the E9 region was originally reported to confer ethanol sensitivity to the GABAA receptor [50 , 51] , no discrete function for this region of the protein has been revealed in transgenic mice expressing the γ2S ( −E9 ) , but not γ2L ( +E9 ) subunit isoform [52] . Thus , the possibility that the changes in splicing we observe here for GABAA receptor γ2 could be related to the adaptive response of neurons to hyperstimulation is purely speculative . Future work will be needed to describe the full spectrum of exons that respond to neuronal excitation genome-wide , and the splicing factors and signaling pathways involved . Although this silencing motif code is relatively rare , well over 100 human exons contain two or more UAGGs that could potentially confer responsiveness in neurons . This also raises the question of whether the failure of cells to reverse acute splicing responses to environmental stimuli or stress may play roles in the misregulation of splicing that contributes to disease . It will also be of interest to explore the relationships between induced splicing effects and the function and distribution of relevant neurotransmitter receptors at the synapse .
Fluorescent reporters: CaMKII_EYFP reporters were constructed by inserting the open reading frame of EYFP and poly ( A ) site downstream of the 8 . 5 kb SalI-NotI fragment of the mouse CaMKII promoter [26] in a pBlueScript ( pBS ) plasmid . Promoter deletions were obtained by cleavage with FspI as shown in Figure 2 . CaMKII-Cre promoter constructs were generated from corresponding Cre fusions , which were the generous gift of Joe Tsien and Zhenzhong Cui . Construction of Gfa2-DsRed involved inserting a 2 . 2-kb EcoRI fragment with the Gfa2 ( glial fibrillary acidic protein ) promoter upstream of the coding sequence of DsRed2 ( red fluorescent protein ) and downstream SV40 poly ( A ) site . The latter fragment was amplified by PCR from pDsRed2 ( BD Biosciences , San Diego , California , United States ) . Splicing reporters: Wild-type ( wt0 ) and mutant CI cassette splicing reporters originally described in [29] were subcloned into promoter-specific plasmids CaMKII_22 or Gfa2 . DIP13 splicing reporters were generated by insertion of the human DIP13 beta exon 5 and flanking introns into the NdeI and XbaI sites of the SIRT1a plasmid , followed by transfer of the HindIII-NotI fragment containing the complete splicing reporter sequence downstream of the CaMKII promoter . The exon 5 insert was synthesized in two parts , as two double-stranded deoxyoligonucleotides , and these were ligated together via complementary EcoRI cohesive ends . Site-directed mutations were generated with the QuikChange Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , California , United States ) Sequences of all splicing reporters were verified by DNA sequencing . Constructs pBS_E18 , and pBS-M3_E18 were generated by PCR from the corresponding full-length splicing reporters and cloned into the HindIII site of pBluescript vectors . All inserts were verified by DNA sequencing . Approximately one dozen rat embryonic day 17 cerebral cortex tissues ( Zivic Miller , Pittsburgh , Pennsylvania , United States ) were treated with trypsin ( 0 . 25% trypsin , w/v , in 0 . 1% glucose , 2 mM L-glutamine and 20 mM HEPES in MEM ) at 35 °C for 20 min . Cells were dissociated by passing the treated samples five to six times through an 18-gauge needle . The cell suspension was washed twice with ice-cold growth medium ( 0 . 5 mM L-glutamine , 2% B27 supplement in Neurabasal Medium; Invitrogen , Carlsbad , California , United States ) , and plated in poly-D-lysine–coated 6-well plates ( BD Biosciences ) at a density of 4–6 × 105 cells/well . For antibody staining , cells were plated on poly-D-lysine–coated coverslips . L-glutamic acid ( 25 μM final concentration ) was added to the growth medium for the first 4 d of culture . Cultures were maintained at 37 °C with 5% carbon dioxide . To induce depolarization , KCl was added directly to the growth medium at the indicated concentrations . Inhibitor compounds were obtained from Sigma ( bicuculline; St . Louis , Missouri , United States ) , or Calbiochem ( H89 , KN93 , MK801 , AP5 , and KT5720; San Diego , California , United States ) . For immunostaining , cortical cultures on coverslips were fixed with 3 . 7% formaldehyde in PBS for 7 min at room temperature , and permeabilized with 0 . 1% TritonX-100 in phosphate buffered saline ( PBS ) for 1 min at −20 °C followed by incubation for 5 min at 4 °C . Coverslips were washed thoroughly with PBS , and blocked for 2 h at room temperature in Blocking Buffer ( 1% BSA , 0 . 1% gelatin in PBS ) containing 5% normal goat serum . The neuron-specific mouse anti-NeuN ( 1:100 dilution ) or glial-specific rabbit anti-GFAP ( 1:1 , 000 dilution ) antibodies were applied to coverslips and incubated overnight at 4 °C . After three washes in incubation buffer at room temperature , TRITC- or FITC-labeled goat anti-mouse or goat anti-rabbit ( Chemicon , Temecula , California , United States ) secondary antibodies ( 1:1 , 000 dilution ) were applied . After washing in Blocking Buffer ( three times for 5 min each ) at room temperature , coverslips were dried and mounted on slides in 10% MOWIOL 4–88 ( EMD Biosciences , San Diego , California , United States ) , 25% glycerol , 2 . 5% DABCO ( Sigma ) , and 0 . 1 M Tris-HCl ( pH 8 . 5 ) . The number of cells with EYFP and TRITC fluorescence , or the number of cells with DsRed and FITC fluorescence , were scored by confocal microscopy ( Radiance 2000; Bio-Rad , Hercules , California , United States ) with FITC/EYFP ( 460–500 nm ) or TRITC/DsRed ( 530–550 nm ) excitation filters . Plasmid DNA ( 1 . 25 μg ) was transfected into cortical cultures using 3 μl of Lipofectamine 2000 ( Invitrogen ) per well of a 6-well plate . Plasmid DNA and Lipofectamine were each diluted to a final volume of 100 μl with OPTI-MEM , then combined and incubated for 20 min at room temperature . Cultures were prepared for transfection by washing once with OPTI-MEM , followed by addition of 2-ml fresh OPTI-MEM and transfection mixture to each well . Medium was replaced with fresh growth medium after 5 h . For splicing pattern measurements , cells were harvested 24–48 h post-transfection using TRIZOL ( Invitrogen ) . Splicing patterns were measured in triplicate by RT-PCR as described [39] . Reverse transcription ( RT ) reactions ( 20-μl total volume ) contained M-MLV reverse transcriptase ( Invitrogen ) , 2-μg RNA sample , and 0 . 5-μg random hexanucleotide primers ( Promega ) . PCR reactions ( 10-μl volumes ) contained 0 . 2 μM specific primers , 2 units of Taq DNA polymerase ( Promega , Madison , Wisconsin , United States ) , 1/20th of the volume of the RT reaction , 0 . 2 mM dNTPs , and 1-μCi of [α-32P-dCTP] . Amplification was for 25 cycles . Primers were designed to amplify exon-included and -skipped mRNAs in each sample . Primers NR1 3021 ( 5′ ) ATGCCCGTAGGAAGCAGATGC ( 3′ ) and NR1 3255 ( 5′ ) CGTCGCGGCAGCACTGTGTC ( 3′ ) were used amplify endogenous C1 cassette mRNAs . Primers rp1 ( 5′ ) GTATGGTACCCTGCACTATTTTGTG ( 3′ ) and rp2 ( 5′ ) TTGGATCGTTGCTGATCTGGGACG ( 3′ ) were used to amplify the GABAA receptor γ2 mRNAs . CaMKII- and Gfa2-specific mRNAs were amplified with promoter-specific upstream primers: CaMKII , ( 5′ ) GCACGGGCAGGCGAGTGG ( 3′ ) ; Gfa2 ( 5′ ) TTGGAGAGGAGACGCATCACCTCC ( 3′ ) together with the gene-specific downstream primer . PCR products were resolved on 6% polyacrylamide/7 M urea gels . Gel images were captured with a BAS-2500 Phosphorimager ( Fuji Medical Systems , Roselle , Illinois , United States ) , and quantitation determined with Science 2003 ImageGauge 4 . 0 software . Scaled-down nuclear extracts were prepared essentially as described for rat cortical tissue [53] . Cortical cultures ( five 10-cm dishes equivalent to 2 × 107 cells total ) were harvested using plastic cell lifters ( Corning , Corning , New York , United States ) in 1× PBS ( 1 ml/dish ) . After centrifugation , cell pellets were resuspended in 4 ml of ice-cold Buffer I ( 10 mM Tris-HCl [pH 8 . 0] , 0 . 32 M sucrose , 3 mM CaCl2 , 2 mM Mg-acetate , 0 . 1 mM EDTA , 0 . 5% NP40 , 1 mM DTT , and 1× protease inhibitor cocktail ) . Cell suspensions were transferred to pre-chilled 15-ml Wheaton glass homogenizers , and cells were broken on ice with pestle A ( tight fitting ) three to five times every 10 min for a total of 30 min . Cell lysates were mixed with 4 ml of Buffer II ( 10 mM Tris-HCl [pH 8 . 0] , 2 M sucrose , 5 mM Mg-acetate , 0 . 1 mM EDTA , 1 mM DTT , and 1× protease inhibitor cocktail ) , and layered on top of 3 . 5 ml of Buffer II in 12-ml ultracentrifuge tubes . Samples were centrifuged in a SW41 rotor at 30 , 000 g for 45 min at 4 °C . Nuclear pellets were rinsed twice with 0 . 5 ml of Buffer C ( 20 mM Hepes [pH 7 . 6] , 20 mM KCl , 1 . 5 mM MgCl2 , 25% v/v glycerol , 0 . 5 mM DTT , 0 . 2 mM EDTA ) , resuspended in 50-μl Buffer C plus 0 . 25 M KCl , and transferred to 1 . 5-ml Eppendorf tubes . The concentration of nuclei in each sample was determined by trypan blue staining and adjusted with buffer to equal concentrations . Nuclear suspensions were incubated for 30 min on ice with occasional mixing , then centrifuged at 14 , 000 rpm for 10 min at 4 °C . Nuclear extracts ( 50 μl volumes ) were collected and used for UV crosslinking experiments . Protein concentrations were typically in the range of 0 . 5 to 0 . 75 mg/ml as determined by Bradford assay . Short RNA substrates ( 22-mers ) were synthesized by Dharmacon and 5′ end labeled with γ-32P-ATP and T4 polynucleotide kinase . Longer RNAs were internally labeled with α32P-UTP during in vitro transcription using T7 RNA polymerase ( Stratagene ) . RNA-protein complexes were assembled for 5 min at 30 °C in 12 . 5-μl reactions by combining labeled RNA substrates ( 100 , 000 disintegrations/min [dpm] ) and nuclear extract ( 3 . 5 μl ) under splicing conditions ( 20 mM Hepes [pH 7 . 5] , 1 . 5 mM ATP , 5 mM creatine phosphate , 2 mM MgCl2 , and 4 . 5 μl of 4 . 5 λ Buffer ( 25% glycerol v/v , 0 . 02 M Hepes [pH 7 . 5] ) . Samples were irradiated on ice with 1 . 8 joules at 365-nm wavelength . Samples were positioned at a distance of 8 cm from the UV light source . After adding SDS-PAGE loading buffer ( volume ) , samples were boiled 5 min at 95 °C and resolved by electrophoresis on SDS-PAGE ( 10% polyacrylamide separating gels ) . Gels were fixed in 45% methanol , 9% acetic acid for 2 h , and dried . Radioactive images were captured as described above . For affinity selection experiments , the CI cassette exon ( variant E18 ) was cloned upstream of the M3 hairpin and synthesized by in vitro transcription by T3 RNA polymerase . Transcripts were purified on Sephadex G50–150 spin columns and ethanol precipitated . MS2-MBP was purified as described [30] .
|
The modular features of a protein's architecture are regulated after transcription by the process of alternative pre-mRNA splicing . Conditions that excite or stress neurons can induce changes in some splicing patterns , suggesting that cellular pathways can take advantage of the flexibility of splicing to tune their protein activities for adaptation or survival . Although the phenomenon of the inducible splicing switch ( or inducible exon ) is well documented , the molecular underpinnings of these curious changes have remained mysterious . We describe methods to study how the glutamate NMDA receptor , which is a fundamental component of interneuronal signaling and plasticity , undergoes an inducible switch in its splicing pattern in primary neurons . This splicing switch promotes the skipping of an exon that encodes the CI cassette protein module , which is thought to communicate signals from the membrane to the cell nucleus during neuronal activity . We show that this induced splicing event is regulated in neurons by a three-part ( UAGG-type ) sequence code for exon silencing , and demonstrate a wider role for exon-skipping responsiveness in transcripts with known synaptic functions that also harbor a similar sequence code .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"in",
"vitro",
"computational",
"biology",
"neuroscience",
"molecular",
"biology"
] |
2007
|
Exon Silencing by UAGG Motifs in Response to Neuronal Excitation
|
Cysteine-rich intestinal protein 1 ( CRIP1 ) has been identified as a novel marker for early detection of cancers . Here we report on the use of phage display in combination with molecular modeling to identify a high-affinity ligand for CRIP1 . Panning experiments using a circularized C7C phage library yielded several consensus sequences with modest binding affinities to purified CRIP1 . Two sequence motifs , A1 and B5 , having the highest affinities for CRIP1 , were chosen for further study . With peptide structure information and the NMR structure of CRIP1 , the higher-affinity A1 peptide was computationally redesigned , yielding a novel peptide , A1M , whose affinity was predicted to be much improved . Synthesis of the peptide and saturation and competitive binding studies demonstrated approximately a 10–28-fold improvement in the affinity of A1M compared to that of either A1 or B5 peptide . These techniques have broad application to the design of novel ligand peptides .
Cysteine-rich intestinal protein 1 ( CRIP1 ) belongs to the LIM/double zinc finger protein family , which includes cysteine- and glycine-rich protein-1 , rhombotin-1 , rhombotin-2 , and rhombotin-3 . Human CRIP1 , primarily a cytosolic protein , was cloned in 1997 [1] using RT-PCR of human small intestine RNA and oligonucleotides whose sequence was derived from the human heart homolog of this protein , CRHP [2] . Recently CRIP1 has been identified as a very exciting biomarker for human breast cancers [3] , 4 , cervical cancers [5] , [6] , pancreatic cancers [7] , [8] and potentially other cancers [4] , [9] . In experiments comparing CRIP1 expression in human breast cancer to matched normal breast tissue the mRNA for this target was overexpressed 8–10-fold in approximately 90% of both invasive and ductal carcinoma in situ [3] . Furthermore , in situ hybridization studies demonstrated close association of the expression with the ductal carcinoma cells [3] . CRIP1 overexpression has also been demonstrated to be the most highly differentially expressed gene in invasive cervical carcinomas; 100-fold up-regulation relative to normal cervical keratinocytes measured in 34 cervical tissues from different clinically defined stages [5] , [6] . CRIP1 was also found to have high levels of expression in pancreatic adenocarcinoma , lung cancers and colorectal cancers [7]–[9] . These data strongly support the development of imaging probes targeting CRIP1 to improve cancer detection . Phage display technology is a robust methodology for identifying peptides that bind relatively tightly to target proteins . This is especially true if the targeted protein's function is to bind peptides in vivo . In these applications , the first generation peptides have a generally lower Kd ( 10–100 µM ) for their target and typically need to be structurally altered to improve binding before the peptides exhibit robust binding suitable to image the target protein . If structural data for the targeted protein exists , it should be feasible to utilize the data to help redesign in silico the Phage display-identified peptides thereby increasing their binding affinity . This approach is much more cost efficient than exhaustive screening of structured phage libraries or expansion of screening assays to include other types of phage display libraries . Despite the potential utility of CRIP1 [10] as an imaging target , significant efforts to develop CRIP1-specific ligands have not been attempted . Here we utilized phage display techniques [11]–[20] to identify peptide ligands with micromolar binding affinity for purified human CRIP1 and exploited rational protein redesign [21]–[27] to increase the peptide's binding affinity . This approach has yielded a peptide that has approximately 10–28-fold improved binding affinity as measured by in vitro saturation and competitive binding assays . This study is a significant advance to the ultimate goal of synthesizing imaging probes that report CRIP1 expression levels in vivo .
CRIP1 was initially cloned into a mammalian expression vector and subsequently into pHAT10 for expression in bacteria . The pHAT10/CRIP1 vector encodes a naturally occurring polyhistidine epitope tag with the sequence of nonadjacent histidines that enable purification of expressed proteins under native conditions at neutral pH 7 . 0 ( details of construct can be found in Figure S1 and Figure S2 ) . Bacterial expression was chosen since it is a robust expression system and presumably CRIP1 does not require post-translational modifications for function . Cultures derived from these bacteria were induced to express CRIP1 using IPTG . We then isolated purified CRIP1 ( see Methods ) . SDS-PAGE analysis of the cell lysate and fractions containing eluted CRIP1 show a single band for chimeric CRIP1 running approximately at the calculated molecular weight for the chimeric protein , 12 . 8 KDa ( Figure S2 and Table S1 ) . The yield of CRIP1 protein was approximately 10 mg of recombinant protein per liter of culture . In order to generate CRIP1 protein that was as similar as possible to endogenous CRIP1 , enterokinase cleavage was performed on purified CRIP1 . Uncleaved contaminating HIS-tagged CRIP1 as well as HIS-tagged peptides were removed by re-running the digest over the CellThru resin and retaining the flow thru . This manipulation of the chimeric protein resulted in a polypeptide almost completely devoid of other “non-CRIP1” amino acids and was used as the bait for phage display studies . After four rounds of positive selection against enterokinase-truncated CRIP1 , 29 phage DNA inserts were sequenced using a 96 gIII primer ( 5′-HOCCC TCA TAG TTA GCG TAA CG-3′ ) . Sequencing verified that 18 of the 29 phagotopes were from the cysteine-constrained phage library , Table 1 . Many of the peptide sequences contained similar motifs and six sequences occurred in more than one phagotope . The peptides A1 and C5 were identified four times , C1 three times , and A9 , B1 , and B5 twice . However , even accounting for conserved amino acid substitutions , no clear motif could be identified . To select clones for further analysis , we used ELISA to determine the relative binding affinities of selected individual phage clones to purified CRIP1 ( see Methods and Figure S3 ) . For these studies the purified chimeric CRIP1 was not reacted with enterokinase . Clone A1 and Clone B5 were measured to possess higher relative affinity . Although it occurred with the same frequency as clone A1 , clone C5 exhibited lower binding affinity . With these results , inserts from the two clones with the highest affinities ( A1 and B5 ) were further investigated as potential ligands to CRIP1 . The computational optimization of the binding affinity of the peptide initially identified from phage display involved three stages . We first constructed a structural model of the cyclic peptide A1 , and then we identified putative binding sites on CRIP1 by docking . Lastly , we searched for new peptide sequences that optimize the stability of the peptide-CRIP1 complex . Shown in Figure 1C is the molecular model of the cyclic peptide A1 ( see Methods ) . To remove the bias on the docking that may be introduced by using only one backbone peptide conformation , we first generated several peptide backbone conformations from snapshots of equilibrium molecular dynamics simulations . Each peptide was docked to the 48 conformations of CRIP1 derived from NMR [10] . By clustering the location of the peptides on the CRIP1 surface , we were able to identify and rank the putative binding sites ( Figure 2 ) . Interestingly , the peptides preferably bind to one face of CRIP1 ( Figure 2 ) . This side of CRIP1 contains two grooves , one formed by helix H3 and S6–S7 loop and another by S2–S3 loop and the N-terminal loop . The binding site of the successfully redesigned A1M is formed by Glu46 , His45 , Phe60 , Tyr56 , and Lys48 ( Figure 3 ) . In the second stage of the redesign , we searched for peptide sequences that optimized the binding free energies of the peptide-CRIP1 complexes using heuristic algorithms and a physical force-field ( see Methods ) . The methodology employs rapid side-chain packing and backbone relaxation to calculate the free energy change due to a mutation . For a given CRIP1-peptide complex , we determined a set of mutations in the bound peptide that resulted in the lowest free energy change , and thus , the highest predicted increase in binding affinity . All CRIP1-peptide complexes were subjected to redesign . All redesigned peptides were then grouped according to their starting peptide backbone conformation ( 1-ns , 9-ns , or 10-ns ) , and according to their putative binding site . The redesigned sequence CLDGGGKGC , which we denote here as A1M ( “modified A1” ) , corresponds to a peptide with the lowest binding free energy ΔΔG among the redesigned sequences in the highest-ranked binding mode . In Table 2 we list representative peptide sequences with high binding affinity but located in other putative binding sites and featuring backbone conformations other than the 1-ns . To identify the dominant motifs in the redesigned peptides , we show in Figure S5 the dominant sequence motifs in the top three candidates binding sites for each peptide model . There is a prevalence of Gly , presumably due to the strongly curved backbone that prefers more flexible Gly over any other residue when the peptide is in the context of the protein but not when the peptide is isolated ( Figure S6 ) . The redesigned sequences also exhibit a preference for charged residues ( mostly Asp , Glu , and Lys ) in at least two positions ( Figure S5 ) . These charged residues , we believe , are what attributes the redesigned peptides their specificity to CRIP1 . In particular , the designed sequence A1M ( Figure S5 ) , which is a member of the largest cluster in the CRIP1 and 1-ns peptide complexes , exhibits a preference for either Lys or Glu in the 2nd position , Asp in the 5th , Lys in the 7th , and Gly in the rest . A closer inspection of the specific energy contributions to the ΔΔG of A1M ( Table S2 ) , we found that the largest contributions to ΔΔG arises from more favorable van der Waals interaction between the peptide and CRIP1 , which we believe is reflected in the preference for Gly in some sites of the binding peptide . The CRIP1-peptide complex also exhibits more favorable solvation energy after the redesign . This observation is also reflected structurally in Figure 3 . In particular , the peptide side chains in A1 ( such as 4D , 5N , 6H , and 8S ) that point toward the CRIP1 surface are replaced by Gly , while those pointing to solution ( 3K and 7R ) retain their polar nature . We computationally redesigned A1 resulting in a peptide with a new sequence ( denoted as A1M ) predicted to bind to CRIP1 with higher affinity . To test this prediction , A1 , B5 and A1M peptides were all synthesized and labeled with FITC for binding studies . Since the peptides encoded by the C7C phage library are at the N-terminus of the minor phage coat protein pIII followed by a short phage encoded spacer Gly-Gly-Gly-Ser , we included this 4-mer in the synthesized peptide . An additional C-terminal Lys was also included in order to enable fluorescent labeling of the peptide . Thus , the different selected mimotopes were produced as synthetic peptides with Gly-Gly-Gly-Ser-Lys and then labeled by adding a fluorescent molecule to the C-terminal lysine . We synthesized the cyclic form of the peptides , A1 , B5 and A1M and determined their ability to bind CRIP1 using saturation binding experiments . The value for the apparent equilibrium dissociation constant ( Kd apparent ) of the FITC-A1M peptide determined by saturation binding was 2 . 6 µM , Figure 4A . This was substantially lower than that obtained for either the parent A1 peptide ( Kd apparent = 34 . 4 µM ) or the estimate for the Kd apparent of the B5 peptide ( Kd apparent = 62 . 5 µM ) derived using similar assays , data not shown . To directly compare the affinity of the A1 and A1M peptides for CRIP1 protein , we performed a competitive binding assay and determined the IC50 for each of the peptides using FITC-A1M as the ligand . These studies demonstrated that the binding affinity of the A1M peptide to CRIP1 was approximately 27 . 5 times better than that of the original A1 peptide , Figure 4B ( A1 peptide IC50 = 8 . 8 µM , A1M peptide IC50 = 0 . 32 µM ) . Since each peptide was effective at displacing FITC-A1M and reached the same minimal binding these data also suggest that ligand binding to CRIP1 occurs at a single site . Further analysis of the binding data with multiple binding site models clearly showed that the best fit of the data was obtained with a one binding site model . Both experimental results are further supported by the predicted binding sites for each peptide to CRIP1 as depicted in Figure 3 . Interestingly , when the Ki for the A1M peptide is calculated ( Ki = 0 . 067 µM ) , it is not the same as the apparent Kd for FITC-A1M ( Kd = 2 . 6 µM ) determined by saturation binding experiments . Based on this observation , the FITC label likely reduces the affinity of the peptide for CRIP1 , which is not uncommon with labeled peptides . However , this observation does not alter the interpretations of the data comparing the affinity of the unlabeled peptides A1 and A1M . From the apparent equilibrium dissociation constants , we calculate the experimental free energy change to be ΔΔG = RT ln Kd , A1M − RT ln Kd , A1 = −1 . 6 kcal mol−1 , which is smaller than the estimated computational free energy change ΔΔG = −83 kcal mol−1 ( Table 2 ) . This difference between the experimental and computational free energy changes is primarily contributed by the van der Waals repulsion term ( Table S2 ) , suggesting initial clashes in the docking of the A1 peptide to the CRIP1 structure . However , since the docking protocol ( ZDOCK ) is consistently implemented , we still expect strong correlation between the computational and experimental free energy changes , that is , those redesigned peptides with lower computational ΔΔG is also expected to have low experimental ΔΔG , although the absolute values may not be directly comparable . In a separate study benchmarking the Medusa force field [28] , [29] , experimental and computational ΔΔG values exhibited a correlation of 0 . 75 ( P = 10−108 ) .
CRIP1 is an extremely compelling marker to exploit for enhanced detection of breast and other cancers . However , its cytosolic expression makes it hard to measure by conventional means , e . g . , antibodies . The cell membrane increases the pharmacological barriers that must be overcome to bind and consequently image the expression of this protein in cancer cells . Thus , we developed methodologies to generate high affinity peptides to purified cytosolic proteins with the ultimate aim of designing these peptides to cross membranes and serve as imaging ligands . To rapidly identify peptides that will bind to CRIP1 , we utilized phage display technology and purified CRIP1 protein . This technology identifies relatively low affinity ( 10–100 µM ) ligands to target proteins . To increase the affinity of the peptides identified using phage display , we developed a protocol for rational peptide redesign that utilizes computational techniques . This protocol successfully increased peptide affinity by approximately 10–28-fold . Computational design methods have been employed to modulate protein-protein interactions . Major challenges in protein design include ( 1 ) identification of ligand-peptide binding site and ( 2 ) optimization of affinity of the peptides that bind to that particular protein [30] . In practice , sequence and conformational space need to be adequately sampled [30] . There is also the need for accurate energy functions that identifies protein sequences corresponding to the global free energy minimum of a given protein conformation [30] . Several studies have been reported to identify protein interaction specificity [31]–[35] . For example , Shifman and Mayo computationally redesigned the promiscuous binding site of calmodulin to increase its specificity to one of its ligand peptides [36] . The authors performed iterative optimization of the rotamers . In another study , Reina et al . computationally engineered a small protein-protein interaction motif of the PDZ domain to bind novel target sequences [37] . The study demonstrated that by combining different backbone templates with computer-aided protein design , PDZ domains could be engineered to specifically recognize a large number of proteins [37] . Another example of successful redesign was the engineering of coiled-coil interfaces that direct the formation of either homodimers or heterodimers [38] . The design protocol involved both positive design , stabilization of desired interaction , and negative design , the destabilization of undesired interactions [38] . The problem of redesigning ligand peptides initially identified from phage display is challenging because the structure of the peptides are not known and the peptides do not have a known binding site in CRIP1 . While there have been successes in the redesign of protein-protein interfaces and location of binding site through computational docking , there is yet no study where the system being designed face these two major challenges simultaneously . We computationally modeled the cyclic peptide and performed molecular dynamics to find the equilibrium conformation of the peptide . To diversify the backbone conformation of the peptide included in the redesign , we selected 3 peptides from the equilibrium molecular dynamics and docked them to 48 CRIP1 conformations from NMR . Interestingly , this procedure of diversifying protein and ligand peptide conformation is sufficient to identify putative binding sites on the protein . We believe that the cyclic structure of the peptide was an important factor to the success of the procedure , because the error from enthalpy-entropy compensation is reduced when docking a cyclic peptide compared to docking a linear peptide . Another important factor that contributed to the success of the peptide design is the conformational sampling introduced in the design steps to maximize the coverage of sequence-structure space available to the CRIP1-peptide complex . First , we performed multiple docking simulations that allowed us to identify various poses for binding . Second , we allowed backbone of the peptide to be flexible during sequence design procedure , thereby significantly diversifying the designed sequences [28] , [29] . Hence , the combination of the restricted conformational space available to a peptide due to circularization and our flexible-backbone sampling technique [28] , [29] allowed us to sufficiently sample the conformational space of the peptide during design , thereby contributing to a successful peptide binder to CRIP1 . Our approach can be further extended to other systems of interest . In this study , we combine empirical and computational approaches to develop a novel paradigm to improve ligand affinity when limited structural information is available . CRIP1 , a potentially powerful biomarker for several cancers , was purified and used in an empirical phage display assay to identify short amino acid peptides with modest affinity for the protein . The resulting peptides were then structurally modeled , based on the structures of other known but unrelated peptides of similar size . Using the limited NMR structure available for CRIP1 the modeled peptides were then computationally docked to CRIP1 resulting in identification of several potential structural motifs responsible for the binding interaction . The modeled interactions were then optimized and peptides were redesigned based on these data . Interestingly , even after 4 rounds of phage display isolation , no consensus sequence for CRIP1 binding peptides emerged . These data might possibly suggest that a strong binding “natural” peptide did not exist on the CRIP1 protein . Remarkably , however , computational manipulation of the amino acids contained within the peptide , based on energy minimization , significantly increased the affinity of the peptide . This suggests that: ( 1 ) conditions for phage-CRIP1 binding were not optimal for peptide identification; ( 2 ) the phage library did not contain all possible combinations of amino acids; and/or ( 3 ) the library was not exhaustively screened . In any of these cases , however , the use of computational redesign combined with empirically derived initial binding data significantly improved the quality of final peptide ligands . As our database of redesigned peptides and resulting Kd's accumulates , the approaches described here potentially can be generalized and could be implemented for peptide ligand generation routinely . The resulting peptide from these studies , A1M , will be further developed as an imaging probe .
The coding region of the CRIP1 cDNA was removed from CRIP1 in pcDNA3 . 1+ using BamH1 and Xba1 restriction enzymes and subsequently subcloned into the BamHI and EcoRI sites of the vector pHAT10 ( BD Clontech ) which contains an N-terminal histidine affinity tag . The construct was confirmed by sequencing . Bacterial cells expressing the pHAT10-CRIP1 were cultured in LB media containing 50 µg/ml ampicillin until reaching OD of 0 . 6 at which time they were induced to express the protein by adding IPTG to a final concentration of 0 . 5 mM IPTG . The bacteria were then harvested and resuspended in Equilibration/Wash Buffer ( 50 mM sodium phosphate pH 7 . 0 , 300 mM NaCl ) containing 0 . 75 mg/ml lysozyme and 0 . 0174 mg/ml PMSF and sonicated with three 10 s pulses ( medium power , Sonic Dismembrator Model 100 , Fisher Scientific ) , with a pause for 30 s on ice between sonication cycles . Following sonication , the lysates were cleared by centrifugation , and incubated with TALON CellThru Resin ( BD Biosciences , Palo Alto , CA ) in Extraction/Wash Buffer . The tagged protein was eluted from the washed column with 0 . 15 M imidazole in Extraction/Wash Buffer . The purity of CRIP1 in fractions was confirmed by SDS-PAGE [39] . The concentration of CRIP1 in fractions was determined by Bradford Assay using IgG as a standard [40] . CRIP1 was digested with enterokinase ( Roche Diagnostics , Inc . ) to remove the His tag and then was used as bait for 4 rounds of panning with the Ph . D . -C7C Phage Display Peptide Library ( New England Biolabs ) . The nucleotide sequence of the gene III insert was determined by sequencing the phage , and the amino acid sequence of the insert was deduced from the nucleotide sequence , shown in Table 1 . Computational optimization of the peptide binding affinities consists of three major steps: ( 1 ) structural modeling of cyclic peptides initially identified from phage display experiments , ( 2 ) finding putative binding sites of the peptides on CRIP1 , and ( 3 ) searching for sequences that optimize the stability of the peptide-CRIP1 complex . The binding affinity of the peptides for CRIP1 protein was determined by saturation binding experiments [54] , [55] . Ninety-six well plates were coated with 150 µl of PBS buffer containing 100 µg/ml of CRIP1 and incubated overnight at 4°C . The wells were then washed three times with 50 mM Tris , 150 mM NaCl , pH 7 . 5 ( TBS ) containing 0 . 1% Tween-20 ( TBST ) , and then each well filled completely with blocking buffer ( TBS containing 0 . 5% BSA ) , incubated at least 1 hour at 4°C , and then rapidly washed 3 times with TBST . Following washing 100 µl of binding buffer containing different concentrations of FITC-labeled peptides ( ranging from 50 nM to 100 µM ) were added to the CRIP1 containing wells and incubated for 1 hour at 37°C with rocking . After incubation , the plates were washed three times with binding buffer . The fluorescence intensity in each well was determined on Infinite M200 Tecan Instrument ( Tecan , NC ) ( Excitation wavelength: 494 nm , Emission wavelength: 530 nm ) . The apparent equilibrium dissociation constant , Kd , apparent , was calculated by non-linear regression using GraphPad Prism ( GraphPad Prism 4 . 0 Software , San Diego , CA ) . Each data point is the average of three determinations . Nonspecific binding was defined in the presence of 1 mM unlabeled peptide . All binding experiments ( saturation binding and competitive binding experiments ) were conducted under equilibrium binding conditions and under conditions where total ligand added was essentially equivalent to the amount of free ligand after the binding reaction occurred . The binding affinity of the A1 and A1M peptides for CRIP1 protein was directly compared by a competitive binding experiment [55] , [56] . Labeled A1M peptide ( FITC-A1M ) was competed with increasing concentrations of either unlabeled A1M peptides or A1 peptide and the IC50 for each peptide calculated . Ninety-six well plates were coated with 150 µl of PBS buffer containing 100 µg/ml of CRIP1 and incubated overnight at 4°C . The wells were then washed 3 times with 50 mM Tris , 150 mM NaCl , pH 7 . 5 ( TBS ) containing 0 . 1% Tween-20 ( TBST ) , and then each well filled completely with blocking buffer ( TBS containing 0 . 5% BSA ) , incubated at least 1 hour at 4°C , and then rapidly washed 3 times with TBST . Following washing 150 µl of binding buffer containing FITC-A1M peptides of 10 µM and appropriate dilutions of unlabeled A1 and A1M peptides ( ranging from 0 to 300 µM ) were added to the CRIP1 containing wells and incubated for 1 hour at 37°C with rocking . After incubation , the plates were washed three times with binding buffer . The fluorescence intensity in each well was determined on Infinite M200 Tecan Instrument ( Tecan , NC ) ( Excitation wavelength: 494 nm , Emission wavelength: 530 nm ) . Ki was calculated by non-linear regression with one binding site using GraphPad Prism ( GraphPad Prism 4 . 0 Software , San Diego , CA ) . Each data point is the average of three determinations , shown in Figure 4B . Data was analyzed using several different binding models and was found to only fit a one binding site model . When no competitor was added the data point was graphed as 0 . 1 nM to satisfy software requirements . This has no effect on calculations of the IC50's .
|
Breast cancer is one of the most frequently diagnosed malignancies in American females and is the second leading cause of cancer deaths in women . Several improvements in diagnostic protocols have enhanced our ability for earlier detection of breast cancer , resulting in improvement of therapeutic outcome and an increased survival rate for breast cancer patients . However , current early screening techniques are neither comprehensive nor infallible . Imaging techniques that improve breast cancer detection , localization , and evaluation of therapy are essential in combating the disease . Cysteine-rich intestinal protein 1 ( CRIP1 ) has been identified as a novel marker for early detection of breast cancers . Here , we report the use of phage display and computational molecular modeling to identify a high-affinity ligand for CRIP1 . Phage display panning experiments initially identified consensus peptide sequences with modest binding affinity to purified CRIP1 . Using ab initio modeling of binding peptide structures , computational docking , and recently developed free energy estimation protocols , we redesigned the peptides to increase their affinity for CRIP1 . Synthesis of the redesigned peptide and binding studies demonstrated approximately a 10–28-fold improvement in the binding affinity . The combination of computational and experimental techniques in this study demonstrates a potentially powerful tool in modulating protein–protein interactions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"oncology",
"biochemistry/experimental",
"biophysical",
"methods",
"biophysics/protein",
"folding",
"biochemistry/protein",
"folding",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/protein",
"structure",
"prediction",
"biochemistry/bioinformatics",
"biotechnology/bioengineering",
"chemical",
"biology/macromolecular",
"chemistry",
"chemical",
"biology/directed",
"molecular",
"evolution"
] |
2008
|
Identification and Rational Redesign of Peptide Ligands to CRIP1, A Novel Biomarker for Cancers
|
The outcome of viral infections is dependent on the function of CD8+ T cells which are tightly regulated by costimulatory molecules . The NK cell receptor 2B4 ( CD244 ) is a transmembrane protein belonging to the Ig superfamily which can also be expressed by CD8+ T cells . The aim of this study was to analyze the role of 2B4 as an additional costimulatory receptor regulating CD8+ T cell function and in particular to investigate its implication for exhaustion of hepatitis C virus ( HCV ) -specific CD8+ T cells during persistent infection . We demonstrate that ( i ) 2B4 is expressed on virus-specific CD8+ T cells during acute and chronic hepatitis C , ( ii ) that 2B4 cross-linking can lead to both inhibition and activation of HCV-specific CD8+ T cell function , depending on expression levels of 2B4 and the intracellular adaptor molecule SAP and ( iii ) that 2B4 stimulation may counteract enhanced proliferation of HCV-specific CD8+ T cells induced by PD1 blockade . We suggest that 2B4 is another important molecule within the network of costimulatory/inhibitory receptors regulating CD8+ T cell function in acute and chronic hepatitis C and that 2B4 expression levels could also be a marker of CD8+ T cell dysfunction . Understanding in more detail how 2B4 exerts its differential effects could have implications for the development of novel immunotherapies of HCV infection aiming to achieve immune control .
The outcome of viral infections is dependent on the function of CD8+ T cells . The activity of CD8+ T cells is tightly regulated by costimulatory molecules which are expressed on the cell surface . Upon interaction with their respective counterparts , various intracellular signalling pathways can be modified leading to altered effector functions [1] . Infection with the hepatitis C virus ( HCV ) results in persistent infection in the majority of cases [2] . The mechanisms leading to chronicity are yet poorly understood . Besides viral escape mutations HCV-specific CD8+ T cells are functionally impaired and lack key effector functions such as cytokine production , proliferation and cytotoxicity [3] , [4] . Virus-specific CD8+ T cell responses generated during the early onset of HCV infection are strong and multispecific , however , in settings of persistent virus infections virus-specific T cells gradually become exhausted [5] , [6] . The mechanisms leading to exhaustion of T cells are only partially understood , beside changes in the cytokine milieu and the lack of CD4+ T cell help [7] , [8] , altered expression levels of coinhibitory molecules may also be of importance . In mouse models of persistent viral infections exhaustion of virus-specific CD8+ T cells was shown to be linked to the expression of the coinhibitory molecule PD1 [9] , [10] . Subsequently , also in human chronic viral infections impaired CD8+ T cell functions have been reported to be associated with PD1 expression [5] , [11] , [12] . However , the susceptibility to blockade of PD1 signaling varied between individuals and PD1 blockade alone was not able to restore function of intrahepatic HCV-specific CD8+ T cells [13] . Similarly , it was shown that HCV-specific CD8+ T cells in acute hepatitis C can be functional despite continued PD1 expression [14] . These findings implicate that multiple factors might be involved in the control of CD8+ T cell function and establishment of T cell exhaustion . Consequently , studies performed in mouse models with persistent viral infections demonstrated that functionally exhausted cells showed expression of multiple costimulatory molecules [15] . Besides PD1 one of the costimulatory molecules identified being upregulated in exhausted virus-specific CD8+ T cells is the NK cell receptor 2B4 ( CD244 ) . This molecule expressed on the cell surface belongs to the family of SLAM-related receptors and contains two cytoplasmatic ITSM ( Immunoreceptor Tyrosine-based Switch Motif ) [16] , which get phosphorylated upon ligation with the high-affinity counterpart CD48 [17] . Initially , 2B4 was described as a costimulatory receptor enhancing NK and CD8+ T cell functions [18] , [19] , but a recent study showed that 2B4 can elicit both activating as well as inhibitory signals on NK cells [20] . Importantly , the consequence of 2B4 ligation is dependent on the cell surface expression intensity of 2B4 and the relative availability of intracellular adaptor molecules . Interestingly , 2B4 signalling can occur via different adaptor molecules , while the recruitment of SAP ( SLAM-associated protein ) is supposed to cause an activation of the cell , involvement of the adaptor molecule EAT-2 ( EWS-Fli1-activated transcript 2 ) seems to result in inhibitory signaling [21] . 2B4 has recently been described to be expressed also on HBV- and HCV-specific [22] CD8+ T cells , however , functional consequences of 2B4 stimulation have not been investigated yet . Due to the costimulatory potential and dual function of 2B4 , we aimed to investigate the role of 2B4 for the control of HCV-specific CD8+ T cell function .
As 2B4 can be expressed not only by NK cells but also by CD8+ T cells we first aimed to investigate if 2B4 is also detectable on virus-specific CD8+ T cells in latent infections ( CMV , EBV ) and after resolved infections ( Influenza A ) . As shown in Figure 1a , we observed a distinct pattern of 2B4 expression on these virus-specific CD8+ T cells in healthy individuals . While CMV- and EBV-specific CD8+ T cells displayed a high frequency of 2B4 expression ( CMV: mean 96% ±5 . 6%; EBV: mean 79% ±18 . 7% , respectively ) , only a proportion of Flu-specific CD8+ T cells were positive ( mean 29% ±21 . 9%; see Figure 1a ) . 2B4 expression levels were also lower on Flu-specific than on CMV- or EBV-specific CD8+ T cells ( average 2B4 MFI 25 ±16 . 6 vs . 163 ±14 . 9 and 88 ±38 . 0 , respectively; data not shown ) . We next asked for the expression of 2B4 on virus-specific CD8+ T cells in both patients with acute viral hepatitis and in patients with chronic hepatitis C . HCV and HBV-specific CD8+ T cells during acute symptomatic infection showed a high frequency of 2B4 expression ( mean 85% ±10 . 3% and 74% ±10 . 7; average MFI 97 ±38 . 9 and 83 ±36 . 9 , respectively; see Figure 1b ) . On HCV-specific CD8+ T cells from patients with persistent HCV infection the frequency of 2B4 expression was slightly lower than in acute patients , here about three quarter of virus-specific CD8+ T cells expressed 2B4 with a considerable inter-individual variability ( mean 70% ± 26% , average MFI 108 ±60 . 1 ) . The level of 2B4 expression on HCV-specific CD8+ T cells in patients with chronic hepatitis C seemed to be lower in subjects showing viral sequence variants in the respective epitopes . However , in those individuals where the viral sequence was matching to the peptide sequences used not only high 2B4 MFIs could be observed on virus-specific CD8+ T cells , but also low expression levels of 2B4 could be found ( see online Supplementary Table S1 ) . In order to assess the frequency of 2B4 expression on bulk CD8+ T cells we screened PBMCs ex vivo for 2B4 . We therefore used PBMCs obtained from patients with chronic hepatitis C infection , patients with acute symptomatic hepatitis C and hepatitis B virus infection and analyzed 2B4 expression by flow cytometry in comparison to samples from healthy individuals . Generally , the frequency of 2B4 expression on bulk CD8+ T cells showed a large inter-individual variability in all cohorts analyzed ( Figure 1c ) . In all patient cohorts a higher frequency of 2B4 expression on CD8+ T cells as compared to healthy controls could be found ( acHC: mean 58% ±19 . 5%; acHB: mean 53% ±18 . 4%; chrHC: mean 45% ±17 . 1% and healthy: mean 39% ±16%; respectively; see Figure 1c ) . No correlations of 2B4 expression frequency on total CD8+ T cells with viral load , AST or ALT levels or other clinical marker of liver disease could be seen ( data not shown ) . In addition , 2B4 expression was studied on liver-infiltrating CD8+ T cells from healthy liver tissues which stained highly 2B4-positive in 85% to 98% of cases ( mean 79% ±17 . 5% , see Figure 1c ) with higher relative levels of 2B4 expression ( average MFI 214±66 . 7 , data not shown ) as compared to peripheral lymphocytes ( Figure 1c ) . To investigate upregulation of 2B4 expression on tetramer+ T cells as compared to the respective bulk CD8+ T cells , we calculated a ratio of 2B4 expression levels by dividing the 2B4 MFI on tetramer+ by the 2B4 MFI on bulk CD8+ cells from the respective individual . A ratio >1 indicates higher expression and a ratio <1 indicates lower 2B4 expression on tetramer+ CD8+ T cells as compared to bulk CD8+ T cells of each respective individual . Of note , in healthy individuals the level of 2B4 expression on CMV- and EBV-specific CD8+ T cells showed a selective upregulation as compared bulk CD8+ T cells ( mean ratio MFI CMV+: 1 . 83±1 . 1 and mean ratio MFI EBV+: 2 . 4±1 . 4 , respectively , see Figure 1d ) . This was not the case , however , for Flu-specific CD8+ T cells which in the majority of cases showed lower levels of 2B4 expression as compared to the respective bulk CD8+ T cells ( mean ratio MFI Flu+: 0 . 7±0 . 5 ) . Importantly , the level of 2B4 expression was selectively increased on HCV-specific CD8+ T cells as compared to the respective bulk CD8+ T cells ( mean ratio MFI chrHC 2 . 1±2 . 4; see Figure 1d ) in chronic hepatitis C . In contrast , virus-specific CD8+ T cells from patients with acute HCV or HBV infection showed almost equal 2B4 expression intensities as compared to the respective bulk CD8+ T cells ( mean ratio MFI acHC 1 . 1±0 . 3 and mean ratio MFI acHB 1 . 2±0 . 5 , respectively ) . Interestingly , the rank order for 2B4 expression between different groups was different between MFI ratios and frequency of 2B4+ cells . For further analysis , grouping in 2B4 high versus 2B4 low expression was performed based on the expression level of 2B4 on tetramer-positive cells ( 2B4 MFI ) . We next investigated 2B4 expression on CMV- , EBV- and Flu-specific CD8+ T cells in patients with chronic hepatitis C . A higher frequency of 2B4 expression on Flu-specific cells was detected as compared to healthy individuals ( Figure 1e , left panel ) while no difference was seen for CMV- or EBV-specific CD8+ T cells . The level of 2B4 expression ( 2B4 MFI ) , however , was lower on Flu-specific T cells as compared to CMV- and EBV-specific cells ( Figure 2e , right panel ) , thus showing the same pattern as seen in healthy individuals . To evaluate whether 2B4 expression is linked to a certain phenotype we analyzed the memory status of 2B4+ CD8+ T cells ( Figure 2a ) by costaining for common differentiation and costimulatory markers . As demonstrated in Figure 2b , 2B4+ CD8+ T cells show a trend towards an elevated coexpression of CD45RA ( mean 57% ±23% ) and reduced coexpression of CCR7 ( mean 25 . 5% ±26% ) . Thus , 2B4+ CD8+ T cells preferentially show an effector or effector memory phenotype . Similar patterns were seen using CD127 and CD62L ( CD62L+ CD127-: mean 25 . 7% ±20 . 8%; CD62L+ CD127+: mean 13 . 4% ±11 . 4%; CD62L- CD127+: mean 18 . 5% ±13 . 9%; CD62L- CD127-: mean 42 . 3% ±22 . 3% ) . However , as expression levels of these markers show a high inter-individual variability there was no clear link to any memory population subtype . Costaining of 2B4+ CD8+ T cells with PD1 and CD57 showed a clear correlation of expression of these costimulatory molecules with all PD1+ CD8+ T cells being also positive for 2B4 ( Figure 2c ) . Of note , this was not the case vice versa as not all 2B4+ CD8+ T cells also showed expression of PD1 . Similar patterns of 2B4-coexpression could be observed with other costimulatory molecules such as CD16 or CD161 , where expression of these markers was also always associated with 2B4 expression . To investigate whether 2B4 has potential costimulatory effects on bulk and virus-specific CD8+ T cells different effector functions of CD8+ T cells were analyzed after 2B4 stimulation . Therefore , the anti-2B4 antibody clone C1 . 7 was used which is known to activate 2B4 by cross-linking [18] . Of note , anti-2B4 alone without further stimulation did not cause any alteration of T cell proliferation ( data not shown ) supporting the concept that 2B4 acts as a costimulatory molecule modifying T cell responses to antigenic stimulation . We generated in vitro cultures using PBMCs and stimulated with anti-CD3/CD28 or virus-derived peptides and with or without addition of anti-2B4 antibody in the cell culture . In line with the report by Chlewicki et al . [20] , where the outcome of 2B4 ligation was described to be dependent on the surface expression intensity of 2B4 , we observed a difference in the proliferation of cells with high or low 2B4 expression levels directly ex vivo . While 2B4 cross-linking enhanced proliferation of CD3/CD28-stimulated 2B4-low cells in a 7 day CFSE assay , no such effect could be observed for cells with high 2B4 expression ( Figure 3a ) . Similarly , other effector functions like degranulation as a marker for cytotoxicity and IFNγ production of bulk CD8+ T cells increased after anti-CD3/CD28 stimulation and additional 2B4 cross-linking only in 2B4-low expressing cells ( Figure 3b ) . Again , this was not the case for samples with high 2B4 expression . However , not all samples with low 2B4 expression levels increased in their effector functions after 2B4 cross-linking . The functional importance of 2B4 expression for CD8+ T cells further became evident when stimulating sorted 2B4+ and 2B4- CD8+ T cells . In this case , only the 2B4+ T cells responded to TCR-stimulation with an increased degranulation ( see online supplementary Figure S1 ) . The findings on altered effector functions of anti-CD3/28-stimulated bulk CD8+ T cells were confirmed for antigen-specific CD8+ T cells . High 2B4-expressing CMV- and EBV-specific CD8+ T cells showed no increase and in some cases even a decrease of expansion of tetramer positive cells after peptide-specific stimulation and additional 2B4 cross-linking ( CMV mean SI = 0 . 93 +/− 0 . 2 and EBV mean SI = 0 . 8 +/− 0 . 3 , respectively; see Figure 3c , left side ) . In contrast , 2B4-low Flu-specific CD8+ T cells showed an elevated peptide-induced proliferation upon simultaneous 2B4 cross-linking as compared to peptide stimulation alone ( mean SI = 1 . 66 +/− 1 . 48 , Figure 3c , right side ) . These observations could be confirmed by using the CFSE assay as readout showing an increased proliferation of Flu-specific but not CMV- or EBV-specific CD8+ T cells . These experiments confirmed an increased proliferation of antigen-specific T cells and not only a relative enrichment in cultures ( Figure 3d ) . Similarly , degranulation and IFNγ production of Flu-specific T cells could be enhanced through 2B4 cross-linking , while CMV- or EBV-specific T cells did not respond or showed decreased of effector functions upon 2B4 stimulation ( Figure 3e ) . Of note , cross-linking of 2B4 using a monoclonal antibody had no impact on the survival and viability of cells . No increase in Annexin-V positive cells was observed when treating cells with anti-2B4 and with or without additional anti-CD3/28 stimulation of the cells ( see online supplementary Figure S2 ) . Also , after in vitro culture no differences in cell viability between the different cell culture conditions could be seen as analyzed by flow cytometry ( according to “live gate” , data not shown ) . Persistent HCV infection is characterized by the functional exhaustion of the HCV-specific CD8+ T cells . In settings of persistent infection PD1 was shown to be upregulated contributing to the dysfunctionality of these cells . As we could show that 2B4 expression is selectively upregulated on HCV-specific CD8+ T cells , we next wanted to investigate the impact of 2B4 on the function of HCV-specific CD8+ T cells in persistent HCV infection . We therefore studied peptide-specific proliferation of HCV-specific CD8+ T cells in a 10 days in vitro culture experiment and determined the expansion of tetramer-positive cells . Overall , 86 patients with chronic hepatitis C were screened for HLA-A2 and 39 cell lines were established . 24 cell lines showed detectable tetramer-positive cells and enriched HCV-specific CD8+ T cells could be detected in 19 ( 79% ) cell lines in at least one of the culture conditions . 10 cell lines responded to peptide stimulation alone ( see Table 1 ) . Overall , the effects of cross-linking 2B4 varied between individuals . Additional stimulation of 2B4 resulted in an enrichment of HCV-specific CD8+ T cells in 5 individuals ( 26% ) ( stimulation index referring to peptide stimulation alone ) . Interestingly , all of these five samples responding to 2B4 stimulation displayed low 2B4 expression levels on the respective virus-specific cells ex vivo ( see table 1 , p = 0 . 052 comparing 2B4-low versus 2B4-high tetramer-positive samples ) . Examples of different cell lines are shown in Figure 4 . In addition there was a trend of reduced responsiveness to peptide stimulation alone for 2B4-high samples ( 2/10 versus 8/15 responding cell lines , p = 0 . 13 ) . A detailed listing of all cell lines analyzed including the respective ex vivo frequencies of HCV-specific T cells is given in table 1 . We also analyzed the effect of blocking 2B4 instead of cross-linking on the expansion of HCV-specific CD8+ T cells from chronic hepatitis C patients using a different anti-2B4 antibody . In this setting , we were also able to achieve an increased proliferation of HCV-specific T cells after 10 days of culture in some individuals . Of note , in those cases where the expansion of HCV-specific T cells increased upon 2B4 blockade , no effect or even a negative effect was induced through 2B4 cross-linking ( see online supplementary Figure S3 ) . Similarly , when 2B4 cross-linking resulted in an enhanced proliferation of HCV-specific cells , no effect could be seen with blocking 2B4 . We next asked whether the variability in the effect of 2B4 stimulation might also be influenced by the signalling pathways elicited . 2B4 ligation can lead to the recruitment of different intracellular adaptor proteins to the cytoplasmic domain of 2B4 . As the binding of the adaptor molecule SAP leads to a positive signalling and activation of the cell and as the outcome of 2B4 ligation seems to be dependent on the availability of intracellular SAP molecules as described by Chlewicki et al . [20] , we analyzed the expression of SAP in CD8+ T cells in order to elucidate whether the role of 2B4 during CD8+ T cell function and functional exhaustion might be based on differences in SAP expression . For this we used PBMCs from healthy individuals and patients with chronic hepatitis C as well as isolated intrahepatic lymphocytes and stained for intracellular SAP ( Figure 5a ) . No striking differences between SAP expression in peripheral CD8+ T cells from healthy individuals and patients with chronic hepatitis C could be observed . However , SAP contents in T cells isolated form liver tissue tended to be lower as compared to peripheral lymphocytes ( Figure 5b ) . Moreover and importantly , SAP expression differed between 2B4hi and 2B4lo CD8+ T cells as SAP levels were significantly lower in 2B4hi cells in both healthy individuals ( MFI 1361±821 vs . 1566±994; p = 0 . 02 ) , chronic hepatitis C patients ( MFI 1023±734 vs . 1170±698; p = 0 . 005 ) and intrahepatic T cells ( MFI 807±255 vs . 1045±222; p = 0 . 03; see Figure 5b and 5c ) . Thus , these findings might explain why HCV-specific CD8+ T cells with high 2B4 expression levels showed a reduction of proliferation upon 2B4 cross-linking , as the reduced intracellular SAP availability results in inhibitory signalling . It has been shown before that blockade of PD1 in vitro can enhance HCV-specific CD8+ T cell proliferation and thereby restore functionality of exhausted cells during persistent HCV infection [23] , [24] . Similarly , we also observed an increase of HCV-tetramer positive CD8+ T cells by addition of anti-PDL-1 in our cell culture system with 6 out of 19 cell lines ( 31% ) showing a significant increase of tetramer-positive cells as compared to peptide stimulation alone ( see table 1 ) . However , as also seen in other reports the susceptibility to PD1 blockade showed a strong inter-individual variability as no positive effect was observed in several individuals analyzed ( Figure 6a and 6b ) . This finding again underlines that PD1 is not alone responsible for the functional exhaustion of virus-specific CD8+ T cells during persistent infections . Recently , it was shown that multiple costimulatory receptors are involved in the regulation of virus-specific CD8+ T cell function during persistent infection in mice [25] . We therefore investigated the effects of simultaneous PD1 blockade and 2B4 cross-linking on the proliferation of HCV-specific CD8+ T cells . Interestingly , we were able to observe an opposing responsiveness to PD1 blockade and 2B4 stimulation . Those samples positively responding to 2B4 cross-linking did not show an increased proliferation induced by PD1 blockade . Similarly , 2B4 cross-linking alone was ineffective in those cases where proliferation increased upon PD1 blockade ( data not shown ) . Additionally and importantly , 2B4 stimulation was able to counteract enhanced proliferation of HCV-specific CD8+ T cells induced by PD1 blockade . The observed increase of HCV-specific proliferation was abrogated if 2B4 was cross-linked simultaneously ( Figure 6a and 6b ) . Only in a few samples ( 3 out of 19 ) the double combination of 2B4 cross-linking and PD1 blockade resulted in enhanced proliferation of HCV-specific CD8+ T cells . In these cases however , neither 2B4 cross-linking nor PD1 blockade alone showed any effect .
In this paper we demonstrate that ( i ) 2B4 is expressed on virus-specific CD8+ T cells during acute and chronic hepatitis C , ( ii ) that 2B4 cross-linking can lead to both inhibition and activation of HCV-specific CD8+ T cell function , depending on expression levels of 2B4 and the intracellular adaptor molecule SAP and ( iii ) that 2B4 stimulation may counteract enhanced proliferation of HCV-specific CD8+ T cells induced by PD1 blockade . 2B4 has first been described on human NK cells [26] and previous studies showed that 2B4 can also be expressed on human CD8+ T cells [27] . Recently , 2B4 expression has been demonstrated also in patients with chronic hepatitis C [22] . We here describe a rather high inter-individual variability of 2B4 expression on CD8+ T cells ranging from less than 10% of cells expressing 2B4 to more than four fifths being 2B4 positive . The majority of 2B4+ CD8+ T cells were differentiated effector memory cells being CD45RA positive and CCR7 negative , although 2B4 expression was not exclusively linked to a specific memory subtype . Nevertheless , further phenotyping showed that the expression of several other costimulatory molecules including PD1 was almost always accompanied by simultaneous 2B4 expression . During persistent virus infections CD8+ T cells can be functionally impaired and may express high levels of PD1 which control CD8+ T cell functions [9] , [23] , [28] . Recent data generated in mouse models of chronic viral infections suggested that not only single but multiple coinhibitory molecules are involved in the control of exhausted virus-specific CD8+ T cells [25] . Gene array analysis revealed that , along with others , also 2B4 was significantly upregulated in exhausted virus-specific CD8+ T cells [15] . In line with these findings , we also could demonstrate that HCV-specific CD8+ T cells show an elevated expression of 2B4 in chronic hepatitis C as compared to the respective individual's bulk CD8+ T cells . Further , we could show that expression levels of 2B4 are higher on HCV-specific CD8+ T cells as compared to the respective individual's bulk CD8+ T cells in chronic hepatitis C patients suggesting that 2B4 might be involved in regulating T cell effector functions in chronic infections . In addition , the increased frequency of 2B4+ Flu-specific CD8+ T cells in chronic hepatitis C suggest that cytokines in the context of chronic hepatitis C may result in an unspecific upregulation of 2B4 on Flu-specific CD8+ T cells . Another explanation could be a cross-reactive stimulation of Flu-specific T cells in HCV infected individuals [29] . Moreover , viral escape leading to sub-optimal T cell receptor stimulation may in contrast be associated with a lower 2B4 expression as 2B4 levels were usually low in those subjects showing viral sequence variations in the respective epitopes . Similar findings have previously been shown for other costimulatory molecules [22] , [30] . Cross-linking 2B4 using the C1 . 7 antibody has been shown to stimulate cytotoxicity and cytokine production by NK cells [31] . In contrast , CD8+ T cell function was not altered if 2B4 alone was engaged without simultaneous TCR stimulation [32] , [33] which is in line with our own findings ( data not shown ) . Still , a previous report suggested that 2B4 costimulation may enhance in vitro expansion of tumour-specific CD8+ T cells [33] . We here show that 2B4 cross-linking may indeed alter effector functions of bulk and antigen-specific CD8+ T cells like proliferation , degranulation and IFNγ production . Further and importantly , 2B4 stimulation was able to enhance peptide-induced proliferation of HCV-specific CD8+ T cells from patients with chronic hepatitis C . Preliminary data from patients with acute hepatitis C confirmed the findings of the stimulatory capacity of 2B4 cross-linking ( data not shown ) . However , this activating effect was only observed in some but not all patients . We noted that responsiveness to 2B4 stimulation was linked with the ex vivo 2B4 expression levels , which held true for HCV-specific CD8+ T cells as well as for CMV- , EBV- and Influenza-specific CD8+ T cells . Virus-specific CD8+ T cells with high 2B4 expression levels appeared to be insensitive towards 2B4 stimulation or even showed a decrease in proliferation upon 2B4 cross-linking , while cells with lower 2B4 expression still responded to 2B4 cross-linking . Future studies also need to address in more detail the effects of 2B4 blockade versus 2B4 cross-linking . 2B4 blockade may enhance effector functions of HBV-specific CD8+ T cells in some patients with acute hepatitis B [34] . We identified a positive effect of 2B4 blockade preferentially in those samples with higher 2B4 expression , while 2B4 cross-linking was effective in 2B4-low expressing cells only . The cause for these opposing functions of 2B4 could be explained with the diverse signal transduction pathways elicited upon 2B4 cross-linking which may lead to a dual function of 2B4 . Different adaptor molecules including SAP can be recruited to the intracellular tail of 2B4 [33] , [35] . Further studies showed that the expression density of 2B4 influences the outcome of 2B4 signalling . While cell activation seemed to correlate to low 2B4 levels , high 2B4 expression and insufficient availability of SAP molecules lead to an inhibition of cells [20] . These data would explain our findings where 2B4 responsiveness was depended on the level of 2B4 expression . Indeed , we could show that SAP expression was lower in 2B4-high versus 2B4-low expressing CD8+ T cells . The low SAP content in 2B4-high cells may induce inhibitory downstream signalling . In this setting , further stimulation would not result in functional enhancement or might even induce reduction of effector functions . This would also explain the important finding that 2B4 cross-linking counteracted stimulatory effects induced by PD1 blockade . Of note , stimulatory effects by 2B4 cross-linking versus PD1 blockade seemed to be rather exclusive as either one of the mechanisms was only active alone and synergistic effects were rarely observed . Since blocking PD1 is currently explored already in clinical trials our findings may partially question this therapeutic concept as PD1 positive cells are always 2B4 positive and as 2B4 ligands are ubiquitously expressed . Another 2B4 adaptor molecule is EAT-2 , which is supposed to confer inhibitory downstream signalling [36] . Future studies should therefore also consider EAT-2 expression in relation to SAP expression . However , one has to consider that not only the presence of the signal transduction molecules , but also the phosphorylation status of the intracellular domain of 2B4 is of importance [37] , potentially explaining the high inter-individual variability observed in our study . Obviously future studies should address several additional issues . We here provide only first data supporting the hypothesis that 2B4 plays a role in the regulation of CD8+ T cell functions . However , the sample sizes are too low to draw definite conclusions and thus confirmatory studies are needed . Only two co-activating or co-inhibitory receptors potentially regulating CD8+ T cells were investigated and thus additional costimulatory molecules such as CTLA-4 , TIM-3 or BLIMP-1 [38] , [39] need to be explored in this context . Moreover , we do not provide data on intrahepatic HCV-specific T cell responses . However , almost all IHLs show very high 2B4 expression levels . Thus , we would expect that IHLs should show similar response patterns as peripheral blood 2B4-high HCV-specific CD8+ T cells . In summary we suggest that 2B4 could be both a marker of CD8+ T cell dysfunction and a potential target for immunointerventions . These findings might be of importance for future development of novel therapies for chronic HCV aiming to achieve immune control .
Heparinized peripheral whole blood was collected from acute hepatitis B virus ( HBV ) and hepatitis C virus ( HCV ) infected patients ( n = 6 and n = 13 , respectively ) as well as from persistently HCV infected patients ( n = 86 ) . Healthy volunteers or samples retrieved from the internal blood donation centre ( n = 49 ) were used as controls . Liver tissue was derived from tumour patients who underwent partial liver resection for evaluation of potential liver metastases or from PSC patients . Written informed consent was obtained from all patients . Ethics approval for this study was obtained by the local ethics committee of Hannover Medical School . All patients were seen in the outpatient clinic of the Department for Gastroenterology , Hepatology and Endocrinology at Hannover Medical School , Germany . Written informed consent was obtained from all patients involved in this study . Isolation of peripheral blood mononuclear cells ( PBMC ) was performed using standard Ficoll Density Centrifugation method . Intrahepatic lymphocytes ( IHL ) were isolated from liver tissue samples by mechanical disruption through a 70 µm nylon mesh and separated by density centrifugation after washing . Mouse anti-human fluorochrome-conjugated monoclonal antibodies were purchased as follows: anti-2B4 clone C1 . 7 ( Beckman Coulter , Fullerton , CA , USA ) , anti-CCR7 ( R&D Systems , Minneapolis , MN , USA ) , anti-PD1 ( BioLegend Inc . , San Diego , CA , USA ) , rat anti-SAP and secondary anti-rat IgG ( Cell Signaling Technology , Danvers , MA , USA ) . All other antibodies and mouse IgG isotype controls were obtained from BD Pharmingen ( Becton Dickinson , Heidelberg , Germany ) . For in vitro blocking and cross-linking experiments the following monoclonal antibodies were used at a concentration of 5 µg/ml each: purified anti-2B4 ( clone C1 . 7 , Beckman Coulter , Fullerton , CA , USA ) , functional grade purified anti-2B4 ( clone eBioPP35 ) , functional grade purified anti-PDL-1 ( clone MIH1 ) and functional grade purified mouse IgG1 isotype control ( eBioscience , San Diego , CA , USA ) . PE-labelled HLA-A*0201 restricted iTag MHC Class I Tetrameric Complexes ( tetramers ) specific for CMV-pp65 495–504 ( NLVPMVATV ) , EBV-BMLF1 259–267 ( GLCTLVAML ) , influenza A Matrix ( influenza-A ( IV ) ) Protein 58–66 ( GILGFVFTL ) , HCV-NS3 1073-1082 ( NS3–1073 ) derived peptide ( CINGVCWTV ) - HCV-NS3 1406–1415 ( KLVALGINAV ) , HCV-core 35–44 ( YLLPRRGPRL ) , HCV-core 132–140 ( ADLMGYIPLV ) , HCV-NS4B 1789–1796 ( SLMAFTAAV ) , HCV-NS5A 2252–2260 ( ILDSFDPLV ) , HCV-NS5B 2594–2602 ( ALYDVVTKL ) and HCV-E2 614v621 ( RLWHYPCTV ) were purchased from Beckman Coulter Inc . ( Fullerton , CA , USA ) . Tetramer staining was considered positive if a distinct population of positive cells could be discriminated . Moreover , at least 0 . 02% of CD8+ T cells were required to be considered as positive . Antigenic HLA-A*0201 restricted peptides were purchased from ProImmune Ltd . ( Oxford , UK ) . Peptides were dissolved in sterile endotoxin-free DMSO ( Sigma-Aldrich , Munich , Germany ) as stock solution . Purity of all peptides was >98% . Final DMSO concentration during T cell culture never exceeded 0 . 1% . Amino acid sequences of the specific peptides are identical to those of the respective MHC Class I Tetrameric Complexes used . Expression analysis of 2B4 on lymphocytes was performed directly ex vivo after PBMC isolation and detection of antigen-specific CD8+ T cells was performed as described elsewhere [40] . Appropriate unstained and FMO ( fluorescence minus one ) controls were performed for adjustment of gating . Samples were analysed on a flow cytometer ( FACSCalibur or FACSCantoII , Becton Dickinson , Heidelberg , Germany ) within 30 minutes . Analysis of FACS data was performed using FlowJo Software ( TreeStar Inc . , San Diego , CA , USA ) . Frozen PBMCs were resuspended in RPMI-1640 ( Invitrogen , Karlsruhe , Germany ) supplied with 10% human AB-Serum ( Cambrex , East Rutherford , NJ , USA ) , non-essential amino acids and sodium private ( Invitrogen , Karlsruhe , Germany ) , 2 µM HEPES ( Invitrogen , Karlsruhe , Germany ) and Penicillin/Streptomycin ( 100U/ml Penicillin and 100 µg/ml Streptomycin; PAA , Pasching , Austria ) . 3×105 PBMCs per well and condition were stimulated in 96-well U-bottom plates ( Sarstedt GmbH , Nümbrecht , Germany ) at 37°C and 5% CO2 . Experiments were set up in multiple replicates if possible . Medium alone or peptide alone with IgG1 isotype control antibodies served as a negative control . Respective antigenic peptides were added at optimal concentrations as mentioned above . The frequency of antigen-specific CD8+ T cells tetramer staining was analyzed after in vitro expansion of cells after seven days in healthy individuals and after ten days for chronic HCV patients . Human recombinant IL-2 ( Invitrogen , Karlsruhe , Germany ) was added at day 3 or 5 in concentrations of 5U/ml , respectively . For healthy individuals changes in proliferation of virus-specific CD8+ T cells was analyzed by calculating the stimulation index ( SI ) of samples with peptide+2B4 cross-linking in relation to peptide stimulation alone . Grouping of cell samples into 2B4-low and 2B4-high was done according to the ex vivo expression levels ( MFI ) of 2B4 on the respective tetramer-positive CD8+ T cell . Cut-offs were set according to the average 2B4 MFI calculated for all tetramer-positive CD8+ T cells analyzed , respectively . Proliferation of bulk and antigen-specific CD8+ T cells was analyzed by CFSE staining exactly as described previously [40] . Cells were stained with 4 µM CFSE prior to culture . After 7 days of in vitro culture the percentage of dividing CFSE-low cells was analyzed by flow cytometry . Degranulation ( CD107a/b expression ) as a surrogate marker for cytotoxicity was analyzed after in vitro stimulation of PBMC by flow cytometry as previously described [40] . In addition , 2B4+/CD8+ and 2B4-/CD8+ T cells were sorted by flow cytometry ( BD FACSAria , Becton Dickinson , Heidelberg , Germany ) , incubated with anti-CD3/28 beads and stained for CD107a/b . IFNγ production of CD8+ T cells was investigated by intracellular cytokine staining after 6h in vitro stimulation with peptides or anti-CD3/28 beads and analyzed by flow cytometry [40] . Determination of the viability of CD8+ T cells was performed after 3 days in vitro culture by staining for Annexin-V using the Annexin-V Staining Kit ( Becton Dickinson , Heidelberg , Germany ) according to manufacturer's protocol . PBMC from healthy individuals were stimulated by anti-CD3/28 and additional 2B4 cross-linking . Intracellular staining of the 2B4 signalling adaptor molecule SLAM-associated protein ( SAP ) was performed in peripheral or intrahepatic lymphocytes using the BD CytoPerm/Wash Buffer Kit ( BD Pharmingen ) . After surface staining cells were fixed and following permeabilization stained using anti-SAP antibody , detection was performed using a secondary antibody . Cells were then analyzed on a flow cytometer . For descriptive means statistics are expressed as mean values ± standard deviations . Statistical analysis of stimulation experiments were performed using considered two-tailed unpaired Student's T tests . Increase of effector functions were considered significant if the calculated SI referring to the medium or peptide only sample were ≤2 . 0 . Man-Whitney U-Tests were used for analyzing differences in 2B4 expressions . For calculating differences in responsiveness of chronic hepatitis C patients to peptide stimulation or additional 2B4 cross-linking or PD1 blockade a simple Chi-Square Test was used . P values of <0 . 05 were considered as significant . Accession Numbers and IDs of human proteins referred to in this manuscript were received from UniProt ( http://www . ebi . ac . uk/uniprot/ )
|
Infection with the hepatitis C Virus ( HCV ) is a world-wide health burden , the infection becomes persistent in the majority of cases . In chronic patients HCV-specific immune responses are weak , HCV-specific CD8+ T cells were shown to be functionally exhausted and to be negatively controlled by costimulatory molecules like PD-1 . Here , we show that the costimulatory molecule 2B4 ( CD244 ) is also involved in the regulation of HCV-specific CD8+ T cell responses and that 2B4 expression is selectively upregulated on virus-specific CD8+ T cells in persistent infections . Proliferation of HCV-specific CD8+ T cells from chronic patients increased by 2B4 cross-linking only if the ex vivo 2B4 expression was low , while we could observe no effect on samples with high 2B4 expression levels . Of note , expression of the intracellular 2B4 adaptor molecule SAP , which leads to an activation of the cell , decreased with higher 2B4 expression levels . Finally , we were able to show that 2B4 cross-linking can counter-act enhanced proliferation of HCV-specific CD8+ T cells seen upon PD-1 blockade . Thus , our study provides new insights into the regulation of CD8+ T cell responses demonstrating an implication of the costimulatory molecule 2B4 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"immune",
"cells",
"immune",
"activation",
"immunity",
"to",
"infections",
"immunology",
"liver",
"diseases",
"adaptive",
"immunity",
"infectious",
"hepatitis",
"hepatitis",
"gastroenterology",
"and",
"hepatology",
"immunoregulation",
"hepatitis",
"c",
"infectious",
"diseases",
"t",
"cells",
"biology",
"immune",
"response",
"clinical",
"immunology",
"immunity",
"viral",
"diseases"
] |
2011
|
Dual Function of the NK Cell Receptor 2B4 (CD244) in the Regulation
of HCV-Specific CD8+ T Cells
|
Chk2 is an effector kinase important for the activation of cell cycle checkpoints , p53 , and apoptosis in response to DNA damage . Mus81 is required for the restart of stalled replication forks and for genomic integrity . Mus81Δex3-4/Δex3-4 mice have increased cancer susceptibility that is exacerbated by p53 inactivation . In this study , we demonstrate that Chk2 inactivation impairs the development of Mus81Δex3-4/Δex3-4 lymphoid cells in a cell-autonomous manner . Importantly , in contrast to its predicted tumor suppressor function , loss of Chk2 promotes mitotic catastrophe and cell death , and it results in suppressed oncogenic transformation and tumor development in Mus81Δex3-4/Δex3-4 background . Thus , our data indicate that an important role for Chk2 is maintaining lymphocyte development and that dual inactivation of Chk2 and Mus81 remarkably inhibits cancer .
DNA damage response is a result of the coordinated actions of DNA damage signaling and repair pathways , cell cycle checkpoints , and apoptosis [1] . Highlighting the importance of the damage signaling and repair mechanisms , mutations of genes such as ATM , BRCA1 and NBS1 , involved in these mechanisms , are associated with increased DNA damage sensitivity , genomic instability , cancer predisposition , immunodeficiency , and developmental defects [2] . Mammalian Mus81 with its partners Eme1 or Eme2 form a heterodimeric structure-specific endonuclease that preferentially cleaves 3′ Flaps and replication fork intermediates [3] . This endonuclease has been shown to facilitate restart of stalled DNA replication forks by generating DNA double-strand breaks ( DSBs ) [4] . Mus81 also interacts with other DNA damage repair proteins including Rad54 , Blm , as well as SLX4 [5]–[7] . Interestingly , Cds1 , the yeast homolog of the mammalian Serine/Threonine kinase Chk2 , was reported to phosphorylate and release mus81 from chromatin , presumably to prevent it from cleaving stalled replication forks ( RFs ) [8] . Three strains of Mus81 mutant mice have been reported . In addition to Mus81 inactivation , Mus81Δex1-10 mice have been reported to also display decreased expression of Fibulin-4 gene . Several of these homozygous mutant mice developed cardiovascular complications and died before reaching weaning age [9] . The phenotypes of these mice have been attributed to the decreased expression of Fibulin-4 [9] . Mus81Δex9-12 mice have been also reported [10] . These mice displayed increased sensitivity to interstrand crosslinking ( ICL ) agents including MMC . Genomic instability was reported to be increased in homozygous Mus81Δex9-12 MEFs expressing the human papillomavirus type 16 E6 that promotes degradation of p53 . While these mutant mice were viable , they showed no increased of tumorigenesis when monitored for a period of 15 months [10] . The Mus81Δex3-4 mutant mice and cells that we have generated were also highly sensitive to MMC [11] . Mice homozygous for the Mus81Δex3-4 mutation showed no expression of Mus81 protein , and displayed elevated levels of spontaneous genomic instability and cancer predisposition [11] . While the cause for the lack of tumorigenesis in Mus81Δex9-12 mutant mice is still not clear , inactivation of p53 in Mus81Δex3-4/Δex3-4 mice rescued their MMC hypersensitivity and exacerbated their genomic instability and tumorigenesis [12] . Inactivation of MUS81 in human cells also resulted in hypersensitivity to ICL agents and elevated levels of genomic instability[13] . Importantly , MUS81 expression was found significantly decreased in human hepatocellular carcinomas , and this reduced expression correlates with a poor prognosis for patients with this cancer [14] . Moreover , a variant MUS81 allele ( rs545500 ) was recently associated with increased risk for breast cancer [15] . CHK2 plays important roles in the DNA damage response , the signaling of the ATM-CHK2-P53 pathway and in cell cycle checkpoints including G2/M checkpoint [16] , [17] . CHK2 phosphorylates a number of substrates including p53 , CDC25A , CDC25C , BRCA1 , E2F1 , and MDC1 . A role for CHK2 in cancer is supported by its rare germline or somatic mutations in certain human familial cancers and in a number of tumors and by its central role in oncogene-induced senescence [18] , [19] . Interestingly , mounting evidence also supports the benefit of CHK2 inhibition in promoting tumor killing in response to genotoxic drugs [16] . Given the importance of Chk2 and Mus81 in DNA damage signaling and repair respectively , we have examined the effect of their dual inactivation on lymphoid cell differentiation , DNA damage response and cancer .
In contrast to the female specific embryonic lethality of Mus81Δex3-4/Δex3-4 mice in p53 deficient background [12] , Mus81Δex3-4/Δex3-4Chk2−/− mice were viable and born at the expected Mendelian ratio ( Table S1 ) . We next examined the fertility of Mus81Δex3-4/Δex3-4Chk2−/− mice and have also further assessed the viability of double mutant females . Interbreeding of Mus81Δex3-4/Δex3-4Chk2−/− mice as well as their breeding ( males and females ) to mice from different genotypes resulted in normal size litters compared to control mice ( P<0 . 05; Table S1 ) . In addition , interbreeding of double mutant mice or heterozygous compound mice resulted in the expected ratio of males and females ( P<0 . 05; Table S2 ) . Double mutant males and females were indistinguishable from their wildtype ( WT ) and single mutant littermates . Examination of the weight of 6 to 8 week old and 4 to 8 month old double mutant males and females indicated no significant differences compared to WT mice and single mutant controls ( Figure S1A ) . Collectively , these data indicate that Mus81Δex3-4/Δex3-4Chk2−/− males and females were viable and fertile , and their gross morphology was indistinguishable from their WT and single mutant littermates . DNA damage signaling and repair are essential for the development and function of the immune system and their failure impairs hematopoietic cell differentiation [20] . We therefore examined the effect of inactivation of Chk2 on the development and homeostasis of the lymphocytes of Mus81Δex3-4/Δex3-4 mice . FACS analysis of splenocytes from 6 to 8 week old Mus81Δex3-4/Δex3-4Chk2−/− mice showed significant impairment of the ratio of T-cells ( Thy1 . 2+ ) to B-cells ( B220+ ) ( Figure 1A and Figure S2A ) . The absolute number of splenocytes was significantly decreased in double mutant mice ( 40 . 9×106±2 . 1 ) compared to Mus81Δex3-4/Δex3-4 ( 68 . 4×106±3 . 6; P = 0 . 0002 ) , Chk2−/− ( 73 . 9×106±3 . 7; P<0 . 0001 ) and WT ( 74 . 9×106±2 . 7; P<0 . 0001 ) littermates ( Figure 1B ) . The absolute number of B-cells was also significantly reduced in spleen of double mutant mice ( 12 . 4×106±0 . 6 ) compared to Mus81Δex3-4/Δex3-4 ( 32 . 8×106±1 . 7; P<0 . 0001 ) , Chk2−/− ( 46×106±2 . 3; P<0 . 0001 ) and WT ( 44 . 5±1 . 6; P<0 . 0001 ) mice ( Figure 1C ) . The absolute number of T-cells in the spleen of double mutant mice ( 15 . 9×106±0 . 8 ) was also significantly decreased compared to Mus81Δex3-4/Δex3-4 ( 21×106±1 . 1; P = 0 . 007 ) , Chk2−/− ( 19 . 4×106±0 . 3; P = 0 . 02 ) and WT ( 19 . 7×106±0 . 7; P = 0 . 01 ) mice ( Figure 1C ) . Impaired T-cell lineage homeostasis in the absence of Chk2 and Mus81 was also reflected by the significantly reduced ratio of helper ( CD4+ ) to cytotoxic ( CD8+ ) T-cells in the spleen of double mutant mice ( 1 . 1±0 . 1 ) compared to Mus81Δex3-4/Δex3-4 ( 1 . 6±0 . 1; P = 0 . 028 ) , Chk2−/− ( 1 . 9±0 . 3; P = 0 . 03 ) and WT ( 2±0 . 2; P = 0 . 008 ) mice ( Figure 1A and 1D ) . To examine the impaired homeostasis of peripheral lymphocytes and the imbalance of the ratio CD4+ to CD8+ peripheral T-cells in double mutant mice , we examined the level of spontaneous and activation induced death of these cells . Spontaneous cell death was found significantly increased in both CD4+ and CD8+ naïve splenocytes from double mutant mice compared to controls ( Figure S3A and S3B ) . In addition , LPS activation of double mutant B-cells also resulted in elevated levels of cell death compared to B-cells from single mutants or WT mice ( Figure S7C ) . While the number of T-cells and B-cells were reduced in spleen of double mutant mice , the number of macrophages in these spleens remained similar to single mutants and WT controls . Taken together these data demonstrate a specific role for Chk2 in maintaining homeostasis of peripheral T- and B-cells deficient for Mus81 . We next assessed the effect of dual inactivation of Chk2 and Mus81 on thymocyte development . Similar to peripheral T-cells , FACS analysis indicated a significantly decreased ratio of CD4+ to CD8+ thymocytes in Mus81Δex3-4/Δex3-4Chk2−/− mice compared to controls ( Figure 1E and 1F ) . Moreover , total number of thymocytes was significantly reduced in double mutant mice ( 86 . 3×106±5 . 5 ) compared to Mus81Δex3-4/Δex3-4 ( 121 . 1×106±4 . 1; P = 0 . 002 ) , Chk2−/− ( 109 . 3×106±3 . 9; P = 0 . 015 ) and WT ( 130 . 9×106±8 . 7; P = 0 . 005 ) mice ( Figure 1G ) . Examination of double negative ( CD4−CD8− ) thymocytes and their subpopulations DNI ( CD44+CD25− ) , DNII ( CD44+CD25+ ) , DNIII ( CD44−CD25+ ) and DNIV ( CD44−CD25− ) from double mutant mice , single mutants and controls indicated no significant differences in cell numbers ( P>0 . 05; Figure S2C ) . However , the numbers of CD4+CD8+ and CD4+ thymocytes from Mus81Δex3-4/Δex3-4Chk2−/− mice were significantly reduced compared to control littermates ( Figure S2D and S2E ) . We next examined the level of expression of TCRβ expression in thymocytes from the four genotypes using FACS analysis and anti-pan TCRβ chain constant region , anti-TCRVβ4 , anti-TCRVβ5 and anti-TCRVβ17a . Consistent with the normal number of CD4−CD8− thymocyte population in Mus81Δex3-4/Δex3-4Chk2−/− mice , double mutant thymocytes displayed no significant difference in the level of TCRβ expression or in the percentages of thymocytes expressing these TCRVβ compared to controls ( Figure S4A and S4B ) . Consistent with these data , no difference was observed in TCRβ expression between Mus81Δex3-4/Δex3-4Chk2−/− and control splenocytes ( Figure S4C ) . These data indicate no significant changes in TCRVβ repertoire in Mus81Δex3-4/Δex3-4Chk2−/− mice compared to controls . Therefore , we conclude that inactivation of Chk2 and Mus81 did not affect TCR V ( D ) J recombination . Our data also indicate that while double negative thymocytes were not affected in Mus81Δex3-4/Δex3-4Chk2−/− mice , the numbers of double positive and CD4+ thymocytes were significantly reduced . Therefore , dual inactivation of Chk2 and Mus81 impairs thymocyte development post DN stage and this defect likely contributes to the homeostatic imbalance of peripheral T-cells in Mus81Δex3-4/Δex3-4Chk2−/− mice . To determine whether the observed reduced number of B-cells in spleen of Mus81Δex3-4/Δex3-4Chk2−/− mice could result from their impaired differentiation , we examined bone marrow ( BM ) cell populations from double mutants and their littermate controls . Total number of BM cells was significantly reduced in double mutant mice ( 8 . 7×106±1 . 6 ) compared to Mus81Δex3-4/Δex3-4 ( 14 . 4×106±1; P = 0 . 012 ) , Chk2−/− ( 14 . 1×106±0 . 7; P = 0 . 005 ) and WT ( 15 . 8×106±1 . 3; P = 0 . 012 ) littermates ( Figure 2A ) . Consistent with the decreased number of peripheral B-cells in Mus81Δex3-4/Δex3-4Chk2−/− mice , FACS analysis using antibodies against CD43 , B220 , IgM and IgD indicated a significantly reduced representation of mature recirculating ( B220highCD43−IgM+ , IgD+ ) B-cells in the BM of double mutant mice compared to controls ( P<0 . 001; Figure 2B and 2C and Figure S5 ) . Similarly , a significantly reduced representation of the pro-B ( CD43+B220+IgM− ) and pre-B ( CD43−B220+IgM− ) cells was also observed in Mus81Δex3-4/Δex3-4Chk2−/− mice ( Figure 2B and Figure S5 ) . Examination of absolute numbers of the pro-B , pre-B and mature B-cell populations indicated their significant decrease in double mutant mice ( pro-B , P = 0 . 014; pre-B , P = 0 . 003; mature B-cells , P = 0 . 0001 ) compared to control littermates ( Figure 2C ) . To further evaluate the cause for the depletion of BM cells in the absence of Mus81 and Chk2 we examined the cell death level of B220+ BM cells from double mutant mice and control littermates using Propidium Iodide ( PI ) staining and FACS analysis . Remarkably , the level of spontaneous cell death of B220+ BM cells was significantly higher in Mus81Δex3-4/Δex3-4Chk2−/− mice compared to single mutants ( P<0 . 05 ) , and WT ( P<0 . 01 ) controls ( Figure 2D ) . These data indicate that in contrast to the inactivation of either Mus81 or Chk2 , their dual inactivation significantly reduces the pool of B-cell precursors . Our data also identify increased cell death as likely to contribute to the defective differentiation of B-cell lineage in Mus81Δex3-4/Δex3-4Chk2−/− mice . The earlier differentiation defect of the B-cell lineage compared to T-cell lineage of Mus81Δex3-4/Δex3-4Chk2−/− mice is likely to contribute to the more pronounced imbalance of the ratio B- to T-cells in the periphery of these mice . In order to address whether the phenotypes observed in Mus81Δex3-4/Δex3-4Chk2−/− mice were cell autonomous or nonautonomous , we have transplanted BM cells from the 4 genotypes into Rag1−/− mice , and have examined the reconstituted mice for the number and differentiation status of BM cells , thymocytes and splenocytes ( Figure S6 ) . Rag1−/− mice arrest their B-cell development at the Pro-B stage [21] . Examination of the number of BM cells in the Rag1−/− mice reconstituted with Mus81Δex3-4/Δex3-4Chk2−/− BM cells indicated no significant differences at the Pre-B-stage ( Figure S6A ) . However , similar to Mus81Δex3-4/Δex3-4Chk2−/− mice , reconstituted Rag1−/− mice with Mus81Δex3-4/Δex3-4Chk2−/− BM cells also displayed significantly reduced number of recirculating B-cells compared to reconstituted controls ( P<0 . 006; Figure S6B ) . Rag1−/− mice reconstituted with Mus81Δex3-4/Δex3-4Chk2−/− BM cells , similar to double mutant mice , displayed reduced number of thymocytes ( P<0 . 03; Figure S6C ) . In addition , spleen from these mice also displayed reduced number of total splenocytes , B-cells , T-cells , CD4+ T-cells and CD8+ T-cells ( P<0 . 05; Figure S6D ) . These data indicate that the phenotypes observed with BM , thymus and spleen of Mus81Δex3-4/Δex3-4Chk2−/− mice were largely cell autonomous . Hypersensitivity to ICL agents including MMC was reported for Mus81Δex1-10 [4] , Mus81Δex9-12 [10] as well as with Mus81Δex3-4 mutations [11] . The hypersensitivity of Mus81Δex3-4/Δex3-4 cells was rescued in p53 null background [12] . Examination of the proliferation of activated T-cells ( anti-CD3 + IL2 ) using the Carboxyl fluoroscein succinimidyl ester ( CFSE ) assay indicated similar level of cell divisions in T-cells from Mus81Δex3-4/Δex3-4Chk2−/− mice and control mice ( Figure 3A ) . However , 72 hours ( hr ) post-MMC treatment ( 0 . 1 µg/ml ) , double mutant cells exhibited a pronounced proliferative defect that was similar to the level observed for Mus81Δex3-4/Δex3-4 cells ( Figure 3A ) . Moreover , loss of Chk2 also failed to rescue MMC induced G2 arrest and apoptosis of Mus81Δex3-4/Δex3-4 T-cells ( Figure 3B and Figure S7A ) . We also examined the effect of Mus81 inactivation on the radioresistance of Chk2−/− activated T-cells . A nearly equivalent resistance was observed in both Mus81Δex3-4/Δex3-4Chk2−/− and Chk2−/− cells 12 hr post ionizing radiation ( IR; 4Gy ) ( Figure S7B ) , supporting that Mus81 inactivation has no effect on the radioresistant phenotype of Chk2−/− cells . Therefore , in contrast to p53 inactivation [12] , inactivation of Chk2 failed to rescue MMC hypersensitivity of Mus81Δex3-4/Δex3-4 T-cells . To examine whether Chk2 plays a role in the MMC hypersensitivity of Mus81Δex3-4/Δex3-4 BM cells [12] , we performed colony forming assay , in the absence or presence of MMC ( 40 ng/ml ) , using double mutant , Mus81Δex3-4/Δex3-4 , Chk2−/− and WT BM cells . Twelve days post plating , MMC treated BM cells from Chk2−/− and WT mice formed a similar number of colonies ( Figure 3C and 3D ) . However , similarly to Mus81Δex3-4/Δex3-4 BM cells , double mutant BM cells displayed reduced colony-forming capacity post-MMC treatment ( Figure 3C and 3D ) . Therefore , inactivation of Chk2 in Mus81Δex3-4/Δex3-4 BM cells does not rescue their MMC hypersensitivity . Given the high in vivo MMC sensitivity of Mus81Δex3-4/Δex3-4 mice [11] and Mus81Δex9-12 mice [10] , we assessed the effect of Chk2 inactivation on the MMC hypersensitivity of Mus81 mutant mice . Cohorts of mice from the four genotypes received intraperitoneal ( i . p ) injection of MMC ( 12 . 5 mg/kg ) and were monitored for survival ( Figure 3E ) . In contrast to the results of the in vitro BM colony forming assay suggestive of equivalent MMC sensitivity of double mutant and Mus81Δex3-4/Δex3-4 BM cells , double mutant mice displayed significantly higher sensitivity to MMC ( mean survival = 6 days ) than Mus81Δex3-4/Δex3-4 mice ( mean survival = 9 days ) . As BM failure might contribute to the elevated MMC sensitivity of Mus81Δex3-4/Δex3-4Chk2−/− mice , we examined the in vivo effects of MMC on BM cell populations from Mus81Δex3-4/Δex3-4Chk2−/− mice , single mutants and WT littermates . Mice were either left untreated or subjected to i . p injection of MMC ( 12 . 5 mg/kg ) and the level of death of B220+ BM cells was examined three days later using PI staining . Increased cell death was observed in B220+ BM cells from both untreated and MMC treated double mutant mice compared to control littermates ( Figure 3F ) . These data indicate that in contrast to p53 inactivation [12] , loss of Chk2 synergizes MMC hypersensitivity of Mus81Δex3-4/Δex3-4 mice and BM cells . Mitotic catastrophe is an abnormal mitosis that triggers the death of cells with damaged DNA as they enter mitosis [22] . Mitotic catastrophe is also triggered by agents that affect the stability of microtubules as well as by mitotic failure that results from impaired cell cycle checkpoints . Chk2 , which is important for G2/M checkpoint , has been reported to negatively regulate mitotic catastrophe of DNA damaged cells [23] . As Mus81Δex3-4/Δex3-4Chk2−/− cells displayed increased levels of spontaneous cell death and these mutant cells and mice also displayed elevated MMC-induced cell death , we have examined whether cell death of Mus81Δex3-4/Δex3-4Chk2−/− cells is triggered by mitotic catastrophe . Cells undergoing mitotic catastrophe typically display micronuclei , increased frequency of giant cells , multilobed nuclei , and nuclear bridging [22] . Untreated Mus81Δex3-4/Δex3-4Chk2−/− primary MEFs stained with DAPI displayed increased frequency of abnormal giant cells with multilobed nuclei compared to controls MEFs ( P<0 . 025; Figure 4A and 4E ) . Lagging chromosomes were also observed in untreated Mus81Δex3-4/Δex3-4Chk2−/− MEFs ( Figure 4B ) . The frequency of untreated double mutant MEFs displaying micronuclei was also elevated compared to controls ( P<0 . 018; Figure 4F ) . In response to MMC , Mus81Δex3-4/Δex3-4Chk2−/− MEFs displayed increased frequency of giant cells with multilobed nuclei ( P<0 . 01; Figure 4G ) as well as cells with nuclear bridging ( P<0 . 001; Figure 4D and 4H ) . These data indicate increased mitotic catastrophe of Mus81Δex3-4/Δex3-4Chk2−/− cells . Loss or reduction of the expression of survivin [24] , an inhibitor of apoptosis protein ( IAP ) , has been shown to lead to cell death by mitotic catastrophe [25] . We have therefore performed Western blot analyses to examine the levels of survivin in untreated or 18 hr and 24 hr post-MMC treatment of LPS activated B-cells from Mus81Δex3-4/Δex3-4Chk2−/− mice , single mutants and WT controls . We observed a lower level of survivin in MMC treated Mus81Δex3-4/Δex3-4Chk2−/− B-cells compared to single mutants and WT controls ( Figure S8 ) . We propose that decreased survivin level in Mus81Δex3-4/Δex3-4Chk2−/− cells could contribute to their mitotic catastrophe . Increased mitotic catastrophe would trigger cell death of Mus81Δex3-4/Δex3-4Chk2−/− cells and therefore is likely to contribute to the impaired homeostasis of BM cells , thymocytes and splenocytes in double mutant mice . p53 plays a central role in DNA damage responses and its phosphorylation by Chk2 on Serine 20 is important for its stability and activation [26] . Therefore , we examined the level of p53 expression and activation in double mutants and control B-cells from the four genotypes . While LPS activated double mutant B-cells displayed increased cell death levels ( Figure 5A ) , the basal level of p53 in these activated B-cells remained similarly low compared to single mutants and WT B-cells ( Figure 5B ) . MMC treatment of WT and Mus81Δex3-4/Δex3-4 activated B-cells increased p53 expression levels ( Figure 5B ) . However , double mutant and Chk2−/− cells failed to increase their level of p53 in response to MMC treatment ( Figure 5B ) . In accordance with the low level of p53 in untreated and MMC treated double mutant cells , the level of Serine 15-p53 ( a substrate for ATM ) , and the levels of Bax and p21 ( p53 downstream targets ) were low to undetectable in double mutant cells under untreated conditions and were not induced in response to MMC ( Figure 5C ) . In contrast , the expression levels of Serine 15-p53 , Bax and p21 in Mus81Δex3-4/Δex3-4 and WT cells were significantly increased in response to MMC ( Figure 5C ) . These data indicate that despite the lack of p53 activation in double mutant B-cells , these cells displayed elevated level of spontaneous and MMC-induced death , supporting that this death is p53- independent . Consistent with these data , mitotic catastrophe has been shown to take place in a p53-independent manner [27] . Chk2 is required for the enforcement of the G2/M cell cycle checkpoint following DNA damage [16] , [18] . It has been shown that loss of Chk2 expression or inhibition of its kinase activity sensitizes cells to death during mitosis [23] . Therefore , we have examined G2/M cell cycle checkpoint in untreated and MMC-treated double mutant and control activated B-cells ( Figure 5D ) . While the fraction of mitotic cells ( positive for phospho-Histone H3 ) was reduced 18 hr and 24 hr post MMC treatment in WT and Mus81Δex3-4/Δex3-4 B-cells , both Chk2−/− and Mus81Δex3-4/Δex3-4Chk2−/− B-cells failed to activate G2/M checkpoint in response to MMC treatment as indicated by their increased fraction of phospho-Histone H3 positive cells compared to untreated controls ( Figure 5D ) . These data indicate that despite MMC-induced DNA damage , double mutant cells are allowed to enter mitosis , due to their impaired G2/M checkpoint , therefore resulting in mitotic catastrophe and cell death . Mus81Δex3-4/Δex3-4 mice display elevated levels of genomic instability [11] , likely due to their defective repair of stalled RFs [28] . Genomic instability was also increased in Mus81Δex9-12/Δex9-12 MEFs expressing the oncoprotein E6 [10] and in Mus81Δex1-10/Δex1-10 ES cells treated with Hydoxyurea [28] . In addition , inactivation of MUS81 by gene targeting in the human colon cancer cell line HCT116 also increased the level of genomic instability of these cells [13] . While Chk2 inactivation resulted in increased genomic instability of Brca1−/− cells , it did not for Nbs1−/− or Mre11−/− cells [29] , [30] . To determine the effect of Chk2 inactivation on the increased spontaneous genomic instability associated with Mus81Δex3-4/Δex3-4 mutation [11] , we examined metaphase spreads of activated B-cells from the different genotypes . These analyses indicated elevated level of spontaneous genomic instability of Mus81Δex3-4/Δex3-4 B-cells while Chk2−/− B-cells exhibited only a slightly increased level of spontaneous genomic instability ( Figure 6A and 6B; Table S3 ) . Remarkably , while inactivation of p53 exacerbated spontaneous genomic instability of Mus81Δex3-4/Δex3-4 cells [12] , inactivation of Chk2 in these cells significantly suppressed their spontaneous genomic instability . Multicolor fluorescence in situ hybridization ( mFISH ) also failed to detect any interchromosomal aberrations like translocations in Mus81Δex3-4/Δex3-4Chk2−/− cells and their controls ( Figure 6C ) . These data suggest that either Chk2 inactivation decreases the survival of Mus81−/− cells that carry genomic instability , or alternatively that it prevents genomic instability from occurring in Mus81 mutants . However , the latter is inconsistent with the drastic increased genomic instability of MMC-treated double mutant B-cells compared to Mus81Δex3-4/Δex3-4 controls ( Figure 6A and 6B; Table S3 ) . To examine whether the reduced level of spontaneous genomic instability of Mus81Δex3-4/Δex3-4 B-cells in Chk2 null background was restricted to these cells or could be observed in other cell types , we have examined metaphase spreads from activated T-cells and primary MEFs from the four genotypes . Similar to B-cells , Mus81Δex3-4/Δex3-4Chk2−/− T-cells ( Table S4 ) and MEFs ( Table S5 ) displayed reduced levels of spontaneous genomic instability compared to Mus81Δex3-4/Δex3-4 controls . In addition , in response to MMC treatment , similar to double mutant-B-cells , Mus81Δex3-4/Δex3-4Chk2−/− T-cells exhibited elevated levels of genomic instability compared to their Mus81Δex3-4/Δex3-4 controls ( Table S4 ) . The increased genomic instability of MMC treated Mus81Δex3-4/Δex3-4Chk2−/− cells compared to Mus81Δex3-4/Δex3-4 cells is consistent with their failure to activate G2/M checkpoint . Despite their DNA damage , these double mutant cells enter mitosis where they suffer mitotic catastrophe . Under normal conditions , mitotic catastrophe could serve to trigger cell death and eliminate Mus81Δex3-4/Δex3-4Chk2−/− cells carrying damaged DNA . Therefore , increased mitotic catastrophe of Mus81Δex3-4/Δex3-4Chk2−/− cells could allow to suppress spontaneous genomic instability . Under chronic conditions of DNA damage ( presence of MMC ) , Mus81Δex3-4/Δex3-4Chk2−/− cells accumulate excessive DNA damage due to their impaired G2/M checkpoint ( Chk2 inactivation ) and defective DNA damage repair ( Mus81 inactivation ) , thus leading to accumulation of chromosomal aberrations . However , these cells are ultimately eliminated as indicated by the increased MMC sensitivity of Mus81Δex3-4/Δex3-4Chk2−/− mice ( Figure 3E ) , increased mitotic catastrophe ( Figure 4 ) and cell death ( Figure 3F ) of MMC double mutant treated cells . CHK2 is mutated in certain human familial cancers and in a number of other tumors [18] . Tumorigenesis was also observed in a mouse model for CHK2 del1100C , a mutation associated with increased cancer risk in humans [31] . In addition , inactivation of Chk2 increased cancer risk of Brca1 , Nbs1 and Mre11 mutant mice [30] . Stracker et al . demonstrated that inactivation of Chk2 in DNA-PKcs deficient background did not predispose these mice for cancer , and proposed that Chk2 suppresses the oncogenic potential of DNA damage arising in the S and G2 but not G1 phases of the cell cycle [30] . Mus81Δex3-4/Δex3-4 mice have increased risk for cancer [11] , and this cancer risk is further exacerbated in p53−/− background [12] . In order to address the effect of Chk2 inactivation on tumorigenesis of Mus81 mutant mice , cohorts of Mus81Δex3-4/Δex3-4Chk2−/− , Mus81Δex3-4/Δex3-4Chk2+/− , Mus81Δex3-4/Δex3-4 , Chk2−/− and WT mice were monitored for survival and tumorigenesis for a period of one year ( Figure 7A ) . As expected , tumors were not observed in WT and Chk2−/− mice in accordance with our previous studies [11] , , Mus81Δex3-4/Δex3-4 mice displayed increased risk for tumors and only 55% ( 11/20 ) of the monitored Mus81Δex3-4/Δex3-4 mice were viable and tumor-free at the end of one year . Remarkably , loss of Chk2 dramatically rescued tumor susceptibility of Mus81Δex3-4/Δex3-4 mice as 90% of double mutant mice ( 18/20 ) were viable and tumor-free at the end of one year ( P = 0 . 02; double mutants versus Mus81Δex3-4/Δex3-4 mice ) . The two double mutant mice that did not survive the one year observation period died of infection . Mus81−/− mice in Chk2 null background were however not fully protected from tumorigenesis as three double mutant mice ( 6% ) developed tumors past 15 months of age . Histological analyses demonstrated that tumors developed in Mus81Δex3-4/Δex3-4 mice ( 1 year old or younger ) were B-cell lymphoma ( 84% ) , large T-cell lymphoma ( 8% ) and osteocarcinoma ( 8% ) . Histological examination of the three tumors that developed in Mus81Δex3-4/Δex3-4Chk2−/− mice past 15 months of age , indicated that one of the tumors was a sarcoma while the two others were B-cell lymphomas . The remarkable mitigation of tumorigenesis of Mus81Δex3-4/Δex3-4 mice by Chk2 inactivation parallels their reduced level of spontaneous genomic instability , failure of G2/M checkpoint and increased mitotic catastrophe . Our finding is consistent with a beneficial effect of CHK2 inhibition on tumor responses to genotoxic chemotherapeutic drugs [18] . Dysregulation of oncogenes , similar to tumor suppressors , plays critical roles in human cancer . Chk2 is activated by oncogenes such as Ras , and is important for mediating oncogene-induced senescence , a safeguard against cancer development [32] . In addition , Chk2 inactivation has been shown to promote oncogenic transformation [19] . We therefore investigated the effect of Chk2 inactivation on transformation of Mus81Δex3-4/Δex3-4 cells and examined oncogenic transformation of double mutant primary MEFs , single mutants and WT controls . Cells were infected with a retrovirus expressing E1A and Ras and transformed colonies were counted three weeks later ( Figure 7B and 7C ) . Consistent with previous studies [19] , the number of E1A-Ras transformed colonies formed by Chk2−/− MEFs was elevated compared to WT controls ( P<0 . 05 ) . Strikingly , while Mus81Δex3-4/Δex3-4 MEFs were able to form E1A-Ras transformed colonies at a similar frequency compared to WT MEFs ( P = 0 . 36 ) , no E1A-Ras transformed colonies were obtained from Mus81Δex3-4/Δex3-4Chk2−/− MEFs in three independent experiments . To confirm that the lack of transformation of Mus81Δex3-4/Δex3-4Chk2−/− MEFs is due to the loss of Mus81 and Chk2 and not due to the inability of these MEFs to be infected with pBabe E1A-HRasV12 retrovirus , MEFs from the four genotypes were infected with this retrovirus and genomic DNA of these cells was prepared 7 days post puromycin selection and PCR analysis was performed to assess the presence of E1A-HRasV12 in the extracted genomic DNA . These analyses demonstrated that despite integration of E1A-HRasV12 in the genome of Mus81Δex3-4/Δex3-4Chk2−/− MEFs ( Figure 7D ) , these cells failed to be transformed . These data demonstrate that similar to the suppression of spontaneous tumorigenesis of Mus81Δex3-4/Δex3-4 mice , Chk2 inactivation also suppresses oncogenic transformation of Mus81Δex3-4/Δex3-4 cells . Collectively our data demonstrate an important role for Chk2 in maintaining homeostasis of BM cells , thymocytes and splenocytes deficient for Mus81 . Remarkably , inactivation of Chk2 also reduced spontaneous genomic instability associated with Mus81 mutation , and significantly protected Mus81 mutants from tumorigenesis and oncogenic transformation . We also report increased mitotic catastrophe in double mutant cells , likely due to the inactivation of G2/M checkpoint associated with loss of Chk2 and increased spontaneous DNA damage associated with loss of Mus81 . The phenotypes observed in Mus81Δex3-4/Δex3-4Chk2−/− mice are p53-independent . In contrast to Mus81Δex3-4/Δex3-4Chk2−/− cells , Mus81Δex3-4/Δex3-4p53−/− cells retain proficient G2/M checkpoint that prevents their entry to mitosis in the presence of damaged DNA . Therefore , we propose that G2/M checkpoint failure and increased mitotic catastrophe are the mechanisms that result in reduced spontaneous genomic instability , tumorigenesis and oncogenic transformation . Our data provide in vivo evidence that inhibition of Chk2 can have remarkable inhibitory effects on tumorigenesis . While pharmacological inhibitors of CHK2 are being considered for cancer therapy [18] , our data suggest that the therapeutic effect of such inhibitors is likely to depend on the genetic background of the tumors . Namely , while Chk2 inactivation promotes tumorigenesis of Brca1 , Nbs1 and Mre11 mutant strains , it remarkably suppresses cancer in Mus81Δex3-4/Δex3-4 mutant background . Our preclinical data highly support the potential therapeutic value for CHK2 or MUS81 inhibitors for cancer patients with MUS81 or CHK2 mutations , respectively .
Mus81Δex3-4/Δex3-4Chk2−/− mice were obtained by crossing Mus81 [11] and Chk2 [33] mutant mice . Mice were in a mixed 129/J × C57BL/6 genetic background and were genotyped by PCR ( conditions available upon request ) . BM , thymus and spleen cells from 6–8 week-old mice were stained with antibodies ( Pharmingen ) against B220 , CD43 , IgM , IgD , Thy1 . 2 , TCR , CD4 , CD8 , CD44 , CD25 and TCRVβ ( 4 , 5 . 1–5 . 2 , and 17a ) . Fluorescence-activated cell sorting ( FACS ) analyses were performed using a FACS Calibur ( Becton Dickinson ) . Peripheral T-cells stimulated with anti-CD3 and IL-2 ( 50 U/mL ) for 48 h , were treated with 0 . 5 µg/ml MMC ( Sigma ) for 18 hr . Cells were washed three times with PBS and cultured for an additional 18 hr . Cells were fixed in 70% ethanol , and DNA was stained with 5 µg/mL of PI ( Sigma ) . Cells at the G1 , S , and G2/M phases of the cell cycle were determined using FLOWJO analysis software . Peripheral B-cells activated with LPS ( 10 µg/ml ) for 48 hr , were treated with 0 . 1 µg/ml of MMC ( Calbiochem ) for 18 hr . Cells fixed and permeabilized with ice cold methanol were stained with anti-phospho Histone-H3 ser10 FITC ( Cell Signaling ) . Cells were analysed using FACS Calibur ( Becton Dickinson ) and results were analysed using FLOWJO analysis software . Peripheral T-cells were stained with 5 µM CFSE and then activated for two days with plate bound anti-CD3 in the presence of IL-2 . Untreated and 0 . 1 µg/ml MMC treated cells were grown for an additional 72 hr and analyzed by flow cytometry . Primary MEFs ( 3×105 ) were cultured in DMEM plus 10% FCS in the presence of pBabe E1A-HRasV12 retrovirus and 8 µg/ml of polybrene . At day three post-infection , Puromycin selection ( 2 µg/ml ) was carried out for three weeks . Colonies on the plates were fixed with ice cold methanol , stained with 0 . 5% crystal violet and counted . To confirm that MEFs have integrated E1A-HRasV12 , genomic DNA was prepared from infected cells post 7 days of puromycin selection . PCR was then performed on MEFs genomic DNA using specific primers for HRasV12 ( F: CGGAATATAAGCTGGTGGTG and R: CGGTATCCAGGATGTCCAAC ) . Eme2 primers ( F: ACGGCTTCCCTACCAGCACA and R: AGTGGCTGCTACTCGGCTTCA ) were used as controls for the PCR . LPS ( 10 µg/ml ) activated splenocytes were cultured for 48 hr in the presence or absence of MMC ( 40 ng/ml ) and metaphase spreads prepared as previously described [11] . Chromosomal aberrations were determined for a minimum of 60 metaphase spreads per cell type . In mFISH , all 21 chromosomes are each painted in a different color using combinatorial labeling and mFISH probe kit ( MetaSystems ) as previously described [34] . T-cells were activated with anti-CD3 ( 10 µg/mL ) + IL2 ( 50 U/mL ) for 48 hr in the presence or the absence of MMC ( 40 ng/ml ) . Metaphase spreads were prepared as described [11] . Chromosomal aberrations were determined for a minimum of 100 metaphase spreads . Metaphase spreads of primary MEFs were similarly prepared and chromosomal aberrations were determined for a minimum of 50 metaphase spreads . Percent aberrations are calculated as aberrations per metaphase then percentages are determined . Breaks/Fragments include chromosome breaks , chromatid breaks , and chromosome fragments . Structural aberrations include chromosome fusions ( such as end to end fusions , Robertsonian fusion like configurations ) . BM cells ( 1×105 ) seeded on 35 mm dishes in Methocult GF M3434 media ( Stemcell Technologies Inc . ) were either left untreated or treated with MMC ( 40 ng/ml ) and cultured for 12 days prior to counting . Cell death was assessed using 7AAD , PI or Annexin V-PI staining and FACS analysis as described [12] . Activated B-cells treated with 0 . 1 µg MMC ( Sigma ) were harvested and lysed 18 hr post-treatment . Proteins were detected using the following anti-rabbit antibodies: anti-p53 ( FL393 , Santa Cruz ) , anti-Phospho-p53 Ser-15 ( cell signaling ) , anti-p21 ( Santa cruz ) , anti-Bax ( Santa cruz ) , anti-Survivin ( Novus ) and anti-β-actin ( Sigma ) . Passage 1 MEFs derived from WT and Mus81Δex3-4/Δex3-4 , Chk2−/− , and Mus81Δex3-4/Δex3-4Chk2−/− embryos were seeded onto coverslips and either left untreated or treated with MMC ( 1 µg/ml ) . 24 hr post treatment , MEFs were fixed with 2% paraformaldehyde , blocked with antibody dilution buffer and stained with DAPI . Images were taken on a microscope ( DM400B; Leica ) under 100X magnification . Bone marrow cells were harvested from the femur of 8 week old mice . 1×106 cells were transplanted to 6 week old Rag1−/− mice by tail vein injection . The transplanted mice were scarified and analysed 8 weeks post-transplantation . Mice ( n = 10 ) for each genotype were injected ( i . p ) with 12 . 5 mg MMC/Kg of body mass and observed for two weeks post treatment . Mice were sacrificed when they became moribund and the day of sacrifice was counted as day of death . The two tailed unpaired student's t test was used for statistical analysis except for survival curves where Log Rank test was employed ( Prism 5 , GraphPad Software ) . All experiments were performed in compliance with Ontario Cancer Institute animal care committee guidelines .
|
Failure to repair DNA damage has been associated with a number of human syndromes , neurodegenerative diseases , immunodeficiency , and cancer . In addition , radiotherapy and many cancer chemotherapeutic drugs induce DNA damage , thus allowing the killing of tumors . Recent data indicated Mus81's role in maintaining genomic integrity and suppressing cancer . Furthermore , inactivation of p53 , the most frequently inactivated tumor suppressor in cancer , leads to synergistic tumorigenesis in Mus81 mutant mice . As Chk2 is important for p53 activation , we have examined the effect of its inactivation on the phenotypes associated with Mus81 loss of function . We report that Chk2 is essential for the development of lymphoid cells deficient for Mus81 . Chk2 inactivation increased spontaneous cell death of Mus81 deficient cells and impaired the development of T and B-cell lineages . Chk2 inactivation also reduced the frequency of Mus81-deficient cells that carry elevated levels of spontaneous genomic instability . Importantly , inactivation of Chk2 protected Mus81 mutant mice from developing spontaneous tumorigenesis . These data indicate potential therapeutic benefits for the inactivation of Chk2 and Mus81 .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
"and",
"genomics/cancer",
"genetics",
"molecular",
"biology/dna",
"repair",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses"
] |
2011
|
Inactivation of Chk2 and Mus81 Leads to Impaired Lymphocytes Development, Reduced Genomic Instability, and Suppression of Cancer
|
Bacteria often face complex environments . We asked how gene expression in complex conditions relates to expression in simpler conditions . To address this , we obtained accurate promoter activity dynamical measurements on 94 genes in E . coli in environments made up of all possible combinations of four nutrients and stresses . We find that the dynamics across conditions is well described by two principal component curves specific to each promoter . As a result , the promoter activity dynamics in a combination of conditions is a weighted average of the dynamics in each condition alone . The weights tend to sum up to approximately one . This weighted-average property , called linear superposition , allows predicting the promoter activity dynamics in a combination of conditions based on measurements of pairs of conditions . If these findings apply more generally , they can vastly reduce the number of experiments needed to understand how E . coli responds to the combinatorially huge space of possible environments .
Bacteria respond to their environment by regulating gene expression [1]–[5] . Gene expression is determined by global factors such as the cell's growth rate and overall transcription and translation capacity [6]–[10] , together with specific factors such as transcription regulators that respond to specific signals . The environments that bacteria encounter are often complex , made up of combinations of many biochemical components and physical parameters . For example , natural habitats of bacteria include the soil [11] , [12] and the human gut [13]–[15] . Complex conditions are also of interest in applications such as food science and bioenergy [16]–[20] . It is therefore of interest to understand how cells respond to complex conditions . However , experimental tests run up against a combinatorial explosion problem: in order to test all combinations of N factors , one needs 2N experiments . For example , a food scientist that seeks to test bacterial gene expression in all possible cocktails of 20 ingredients at two possible doses needs more than a million experiments , 220 = 1 , 048 , 576 experiments . If four doses are considered , 420∼1012 experiments are needed . Important recent advances on bacterial gene expression made by Gerosa et al [7] and Keren et al [10] do not overcome this concern , because one still needs to measure expression in each combination of conditions . Thus , the search for simplifying principles is important . One such simplifying principle was suggested in a study of the protein dynamics in human cancer cells in response to drug cocktails [21] . Protein dynamics in a drug combination were well described by weighted averages of the dynamics in the individual drugs . This feature was termed linear superposition ( also known as convex combination or weighted average ) . Furthermore , it was found that measuring dynamics in drug pairs could be used to predict the dynamics in drug triplets and quadruplets . This opens a possibility for avoiding the combinatorial explosion problem: To predict gene expression in all possible combinations of N drugs it is sufficient to measure all N ( N-1 ) /2 pairwise combinations instead of 2N . For example , the response to all combinations of 20 drugs can be well approximated by measurement of the 190 pairwise combinations , rather than over a million combinations . The number of necessary experiments is reduced by more than 5000 fold . Here , we asked whether the linear superposition principle might apply also to understanding the response of E . coli to combinations of growth conditions . Since we consider the transcriptional response of bacteria to natural stress conditions , rather than the proteomic response of cancer cells to anti-cancer drugs , this study explores this principle in a very different biological context . We used a promoter library to obtain accurate dynamics of 94 promoters as bacteria grew from exponential to stationary phase in all possible combinations of a set of nutrients and stresses . We find that dynamics in a mixture of conditions is , for most genes and conditions , well described as a linear combination – a weighted average – of the dynamics in the individual condition . The weights sum up to approximately one . We also found that part of the reason for this feature is that promoter activity dynamics for each gene seem to be quite limited , and are explained effectively by one or two principal components . Using linear superposition , we employ mathematical formulae that allow predicting the dynamics in cocktails of conditions based on measuring pairs of conditions . This suggests that the combinatorial explosion problem may be circumvented , to understand and predict bacterial responses to complex conditions .
We studied 94 genes and 2 control strains ( see Materials and methods ) , in a 96 well plate format . We chose 94 genes which represent a wide range of biological functions ( Table S1 ) , and which have a strong detectable fluorescence signal in a range of growth conditions ( more than 2 standard deviation above background ) . We measured promoter activity of these genes as a function of time using the E . coli reporter library developed in our lab [22] ( Figure 1a ) . Each reporter strain had a rapidly maturing GFP variant ( gfpmut2 ) under control of a full length intragenic region containing the promoter for the gene of interest , on a low copy plasmid ( Figure 1a ) . Promoter activity was measured as the time derivative of GFP fluorescence accumulation divided by cell density , as described [23]–[25] ( Methods ) . Using this approach , the temporal dynamics of promoter activity can be measured at high accuracy [26]–[28] . We aimed at understanding the promoter activity dynamics in growth media composed of combinations of chemical conditions . For this purpose we chose 4 elementary conditions . Each condition is based on a chemically defined medium , M9+0 . 2% glucose as the carbon source . In each elementary condition one supplement is added ( A ) 0 . 05% casamino acids , ( B ) 3% ethanol , ( C ) 10 µM hydrogen peroxide H2O2 ( D ) 300 mM NaCl salt . In all four conditions , cells reached a similar final optical density ( OD ) , with different growth rates ( Table S2 ) . We studied combinations of these conditions by mixing the appropriate supplements into the standard medium . Thus , condition A+B is standard medium supplemented with 0 . 05% casamino acids and 3% ethanol ( Figure 1b ) . In total , we studied all four single conditions , all six pairs , all four triplets and the quadruplet A+B+C+D ( The different growth rates of all combinations is given in table S2 ) . In each condition , we measured promoter activity of the 94 genes at an 8 minute resolution , throughout batch culture growth , including exponential growth phase and stationary phase . Depending on the growth rate in a given condition the stationary phase was reached after 8 to 22 hours of growth . Each experiment was repeated on four different days . We observed that promoter activity dynamics of a given promoter can vary both in shape and in amplitude across different growth conditions . Using principal component analysis we can identify the typical shapes of every promoter across conditions . In figure 2 we show the activity dynamics of fliY in all measured conditions ( Figure 2a ) and its two principal dynamic curves PC1 and PC2 ( Figure 2b ) . We found that each promoter can be well described by two principal component dynamic curves , which explain 80–99% of its variance ( Figure 2c ) . In more than 93% of the promoters , the two first PCs explain 90% or more of the variance ( Figure 2c ) . Because of the 2PC property , each promoter activity curve is a linear combination of its two PCs to a good approximation . The first two PCs explain much more variance than expected in randomized data ( See Figure 1 in Text S1 ) . About one third ( 30/94 ) of the promoters were well explained by one principal component in all measured conditions ( Figure 2f ) . The dynamics of these promoters thus had a rather constant shape in different conditions , and differed only in amplitude . For example one PC explains 98% of the variance in the σ70 activated ribosomal promoter rrnB ( Figure 2d , e ) . The other 2/3 of the promoters , explained well by 2PCs , showed condition-dependent shape changes in their dynamics . The low number of principal component curves needed in order to explain the promoter activity dynamics could be a result of general nonspecific transcription for promoters with only one PC ( with only change in amplitude with different growth rates ) , and could be condition dependent yet limited in number for promoters with two principal component curves . We find that for 76% of promoter activities , the first PC is highly correlated ( R2 above 0 . 8 ) with instantaneous growth rate ( See Figure 6a , b , c in Text S1 ) . This may relate to a principal component analysis by Bollenbach et al [29] that instead of considering dynamics , considered a single point at exponential growth in response to antibiotic combinations . The first PC correlated with growth rate and the second with drug specific effects . The second PC in our dataset varies more widely in shape between different promoters ( See Figure 6d in Text S1 ) . We now use the 2PC property to understand how promoter activity dynamics in a mixed condition PA+B relate to the dynamics in each supplement alone , PA and PB . Since a promoter can be described as a linear combination of the same 2PCs in any condition , we expect the combined PA+B to be a combination of the one-supplement conditions PA and PB: Where the best fit weights are wA and wB . To find the best fit weights we aligned the dynamics in conditions A , B and A+B according to a shared axis of generations ( – see Materials and methods and Text S1 Extended methods ) . Using a generation axis helped compare conditions despite variations in growth rate . We performed linear regression of PAB based on PA and PB . These weights are constant over time . Similarly , dynamics in three and four supplements can be represented as linear combinations of the one supplement dynamic: We determined the best fit weights wi ( 1…N ) using an error-in-variables linear regression [30] ( where wi ( 1…N ) is the weight contributed by condition i which best fits the combined condition 1…N – see Text S1 ) . To measure how similar a linear combination is to the measured combination dynamics we compute the relative fit error between the two ( Text S1 ) . Linear combination describes the dynamics well ( relative error 10% see Text S1 ) , as expected . So far , these findings are consistent with the 2PC finding . However , we find information beyond the 2PC property , when we examine the sum of the weights in these equations . We find that the sum of weights wA+wB in each fit is distributed around one ( See Figure 2 in Text S1 ) , with a standard deviation of 0 . 6 . The weights are usually positive ( 76%>−0 . 05 ) . This means that the linear combination is approximately a weighted average ( see also [29] ) . The same applies to three and four supplement mixtures . We therefore tested a simpler model , named linear superposition , in which the weights are constrained to sum to one , and be positive: Here the dynamics in the mixture conditions is a linear combination of the individual supplement conditions but with only one free parameter wAB , with the constraint that wAB ranges between 0 to 1 . In most conditions , the linear superposition model gives a better score in describing the data in tests that take model simplicity into account ( Akaike information criterion [31] , which sums the log likelihood of the model fit and the number of model parameters , see Text S1 ) . The linear superposition model also gave better predictions than a multiplicative superposition model ( in which , See Text S1 ) . A representative sample of promoter dynamics and the corresponding linear superposition model is shown in figure 3 . The mean fit error is 12% , with 86% showing less than 20% fit error . This compares well with the day-to-day experimental error estimated from 4 day-to-day repeats , with average error of 14% ( See Figure 3 in Text S1 ) . A table with the weights and errors for all promoters and conditions is provided in the SI ( Table S3 ) . We also sought conditions where linear superposition does not apply . We found one such condition using the classic diauxic shift experiment [32]–[34] . In this case , bacteria grow on a combination of two sugars , glucose and lactose . They begin to utilize the preferred sugar , glucose , and only when glucose is depleted switch to using the second sugar , lactose . The cells thus delay the production of the lactose utilization system – the lacZ promoter – until glucose concentration becomes low[23] . Then , cells switch to growth on lactose and express lacZ vigorously . Considering glucose and lactose as conditions X and Y , one does not find that lacZ is a linear combination in the combined condition X+Y . This is because under glucose alone , lacZ is weakly expressed ( Figure 4 ) , and under lactose alone it is strongly and constantly expressed ( Figure 4 ) . Linear combination would mean a constant expression at some intermediate value . In contrast , in X+Y , lacZ expression is strongly time dependent ( Figure 4 ) . Such an effect is expected whenever two conditions interact to regulate genes sequentially [17] , [35] , rather than simultaneously . Another example we found is the metabolic operon nudC , which showed behavior similar to lacZ , and a poor fit to linear combination ( See Figure 4 in Text S1 ) . A table with the weights and errors for all promoters in the diauxic shift is provided in the SI ( Table S4 ) . We note that all of the other 92 genes in our study showed good linear superposition in the diauxie condition . This suggests that linear combination might break down for specific genes where the conditions have a nonlinear , sequential effect or more generally distinct temporal dependence on their dynamics . We now use linear superposition to predict the dynamics in a combination of conditions given only data on individual-supplement dynamics , and data on pairs ( that is , given the weights wi ( ij ) in pair conditions ) . Previous work by Wood et al [36] , based on a different approach , successfully predicted the growth-inhibitory effect of antibiotic cocktails based on measurement of pairs of drugs . Such predictions are potentially useful because , as discussed in the introduction , it is much easier to measure all pairs than to measure all possible cocktails of N conditions . The predictions rely on the assumption of linear superposition , specifically that weights sum to one . We apply the formula developed by Geva-Zatrosky et al [21] for predicting protein dynamics in cancer drug cocktails . The formula uses the fact that a combination , say A+B+C , can be treated in three different ways: a mixture of A+B and C , and equivalently as a mixture of A+C and B , and as a mixture of B+C and A . Each of these three possibilities can be described using superposition , and should yield the same result . This provides enough equations to predict the weights needed to calculate the triplet dynamics ( See Text S1 ) . The formula predicts the linear superposition weights in an N-supplement cocktail The prediction for the weights wi ( 1…N ) based on measurements of the weights in all cocktails of N-1 supplements is [21]:where the superscript ( ≠j ) relates to which supplement is missing in the N-1 cocktail . When only pair data is available , this formula is used iteratively: the triplets are predicted from pair weights , the quadruplet uses these predictions for the triplets weights and so on . Using this equation , with pair data only , we find good predictions for the promoter dynamics . Representative dynamics and predictions are shown in figure 5 . The median relative error between prediction and measurement is 27% for triplets and 34% for the quadruplet ( See Figure 5 in Text S1 ) . These prediction errors are about 2 times larger than the day-to-day experimental error . To evaluate the predictive power of this formula we compared it to what one could expect given no additional information . For this purpose , we ‘predicted’ the dynamics for a given promoter in condition X by randomly picking an exemplar from the available set of measured curves for that promoter in all conditions except X . We then averaged the error between these ‘predictions’ and the measurement in condition X . For example , for a given promoter in condition A+B+C , we used the measured curves in all 14 conditions except A+B+C , namely the 4 single conditions ( A , B , C , D ) , 6 pairs , 3 triplets after excluding A+B+C and one quadruplet . We generated 14 errors and compared the average error to the present formula prediction error . Our formula predictions show about 2 . 3 times less error than the average error for triplet conditions and about 1 . 5 times less error in the quadruplet condition ( Figure 6 ) .
We studied promoter activity dynamics in combinations of conditions by means of fluorescent reporters . We find that almost all promoters and conditions tested show a linear combination property: the dynamics in a combined condition is a linear combination of the dynamics in individual conditions . The weights in the combination tend to sum to one , and thus combinations act as weighted averages of individual conditions , a property called linear superposition . Linear superposition allowed us to predict the dynamics in triplets and quadruplet based on the dynamics in pairs of conditions . This prediction formula offers a way to reduce the combinatorial complexity of understanding complex conditions . Genes regulated by specific signals that are strongly time dependent in the complex environment , such as lacZ in a diauxic shift experiment ( Figure 4 ) , may not display the linear superposition principle . Note that in the diauxie condition , 92 of the 93 other promoters did show linear superposition with good accuracy . Almost all promoters in this study needed only two principal components to explain their dynamic curves across conditions . This finding is in line with studies on gene expression in a range of organisms [37]–[41] . About one third of the promoters did not show an environmental specific change in the shape of their dynamics and were well explained by only one principal component ( Figure 2d–f ) . It would be interesting to extend this study to investigate the biological meaning of these principal components . It seems that the first PC captures general effects related to the growth [29] ( See Figure 6 in Text S1 ) , and the second captures the way that the specific regulation of the promoter changes its first PC dynamics . The fact that two PCs explain the data well means that promoter activity in a mixed condition can be described as a linear combination of the promoter dynamics in the basic conditions . A further finding is that the sum of weights in this combination is distributed around one . A model of linear superposition , in which weights are constrained to be positive and sum to one , explain the data very well in most conditions . This feature- sum of weights equals one- is crucial to allow predictions of higher order combinations . If the sum of weights was not constrained , one would not have enough equations to predict the weights in a cocktail . The linear superposition property calls for a biological explanation . One possible framework is the recently suggested finding that when cells compromise between a few tasks , their optimal solution is a gene expression profile that is a weighted average of the optimal profiles for each individual task [42]–[44] . Testing this theory , which is based on a multi-objective compromise between several tasks [45] , also known as Pareto optimality , would require understanding the tasks of the cells under the present conditions . Pareto theory points to one possible reason why linear combination might be optimal , which applies in the limit of strong selection under environments which include many combinations of conditions . How linear summation is achieved is a mechanistic question which needs further research . One way that a linear summation can be achieved is when regulatory factors compete over a limiting component - for example: σ70 and σS compete over the RNA polymerase , such that the fraction of σ70-RNApol is equal to 1 minus the fraction of σS-RNApol ( here we neglected other σ factors ) . Therefore , the fraction of transcription allocated to growth ( σ70 ) and survival ( σS ) genes follows a line in gene expression space [43] . The position on the line is determined by the ratio of the two σ factor concentrations . It would be interesting to extend this study to other genes , conditions and organisms . It would be important to find conditions where superposition breaks down , as for lacZ in the diauxie conditions described here , to find the limitations of this approach . This approach can be tested also in other levels of cell response , for example one may ask whether linear superposition applies to dynamics of metabolite fluxes [45] , [46] . It would be interesting to extend this analysis to situations in which cells show all-or-none patterns of gene expression [35] , [47]–[49] , and to enhance our understanding of how bacteria compute [50] . If the present approach for predicting dynamics in complex conditions applies more generally , one may attempt to computationally navigate the combinatorial huge space of possible environments , to search for growth conditions with desired gene expression profiles .
All media were based on M9 defined medium ( 42 mM Na2HPO4 , 22 mM KH2PO4 , 8 . 5 mM NaCl , 18 . 7 mM NH4Cl , 2 mM MgSO4 , 0 . 1 mM CaCl ) . The media used in this study are: Casamino acids ( M9 minimal medium , 0 . 2% glucose , 0 . 05% Casamino acids ) ; NaCl ( M9 minimal medium , 0 . 2% glucose , 300 mn NaCl ) ; H2O2 ( M9 minimal medium , 0 . 2% glucose , 10 µM H2O2 ) ; Ethanol ( M9 minimal medium , 0 . 2% glucose , 3% ethanol ) ; and all 15 combinations: 4 single conditions , 6 pairs , 4 triplets and one quadruplet . For example Casamino acids+NaCl ( M9 minimal medium , 0 . 2% glucose , 0 . 05% Casamino acids , 300 mn NaCl ) . In addition we measured glucose alone ( M9 minimal medium , 0 . 2% glucose ) ; Casamino acids with no glucose ( M9 minimal medium , 0 . 05% Casamino acids ) ; Low concentration glucose ( M9 minimal medium , 0 . 04% glucose ) ; Lactose ( M9 minimal medium , 0 . 4% lactose ) ; Lactose with low concentration glucose ( M9 minimal medium , 0 . 4% lactose , 0 . 04% glucose ) . In each experiment the bacteria were cultivated in the presence 50 µg kanamycin/ml . GFP levels were measured over time for 96 reporter strains ( Table S1 ) , each bearing a green fluorescent protein gene ( GFP ) optimized for bacteria ( gfpmut2 ) on a low copy plasmid ( pSC101 origin ) . All strains in this study were derivatives of wild type E . coli K12 strain MG1655 . Reporter strains were inoculated from frozen stocks and grown over-night on M9 with 0 . 2% glucose and 0 . 05% casamino acids for 16 hours in 600 µl high-brim 96-well plate and reached a final OD of ∼0 . 9 . The 96-well plate was covered with breathable sealing films ( Excel Scientific Inc . ) . The 96-well plates were prepared using a robotic liquid handler ( FreedomEvo , Tecan Inc ) . Overnight cultures were diluted 1∶500 into the micro 96-well experiments plates . The final volume of the cultures in each well was 150 µl . A 100 µl layer of mineral oil ( Sigma ) was added on top to avoid evaporation and contamination , a step which we previously found not to significantly affect growth [25] , [28] . Cells were grown in an automated incubator with shaking ( 6 hz ) at 37°C . A robotic arm moved the micro 96-well plates from the incubator-shaker to the plate reader ( Infinite F200 , Tecan Inc . ) and back . Optical density ( 600 nm ) and fluorescence ( 535 nm ) were thus measured periodically at intervals of ∼8 minutes until reaching stationary phase with a final OD of ∼0 . 15 . Since the overnight cultures on high-brim 96-well plate reached a higher final OD equivalent to about 3 extra generations beyond the micro 96-well plates we obtain data for ∼6 generations of growth . Data was obtained from the plate reader software ( Evoware , Tecan ) and processed using custom Matlab software . Background fluorescence was subtracted from GFP measurements using a reporter strain bearing promoterless vector U139 for each well . Then , promoter activity was calculated using temporal derivative of GFP computed by finding the slope of a sliding window of 17 data points of GFP fluorescence using regression , divided by the mean OD over this window . Varying window size between 5 and 30 affects curve smoothness but does not change the conclusions of this study .
|
Bacteria face complex conditions in important settings such as our body and in biotechnological applications such as biofuel production . Understanding how bacteria respond to complex conditions is a hard problem: the number of conditions that need to be tested grows exponentially with the number of nutrients , stresses and other factors that make up the environment . To overcome this exponential explosion , we present an approach that allows computing the dynamics of gene expression in a complex condition based on measurements in simple conditions . This is based on the main discovery in this paper: using accurate promoter activity measurements , we find that promoter activity dynamics in a cocktail of media is a weighted average of the dynamics in each medium alone . The weights in the average are constant across time , and can be used to predict the dynamics in arbitrary cocktails based only on measurements on pairs of conditions . Thus , dynamics in complex conditions is , for the vast majority of genes , much simpler than it might have been; this simplicity allows new mathematical formula for accurate prediction in new conditions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"cell",
"biology",
"gene",
"expression",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"molecular",
"cell",
"biology",
"dna",
"transcription"
] |
2014
|
Linear Superposition and Prediction of Bacterial Promoter Activity Dynamics in Complex Conditions
|
Hemorrhagic fever with renal syndrome ( HFRS ) is highly endemic in mainland China , and has extended from rural areas to cities recently . Beijing metropolis is a novel affected region , where the HFRS incidence seems to be diverse from place to place . The spatial scan analysis based on geographical information system ( GIS ) identified three geo-spatial “hotspots” of HFRS in Beijing when the passive surveillance data from 2004 to 2006 were used . The Relative Risk ( RR ) of the three “hotspots” was 5 . 45 , 3 . 57 and 3 . 30 , respectively . The Phylogenetic analysis based on entire coding region sequence of S segment and partial L segment sequence of Seoul virus ( SEOV ) revealed that the SEOV strains circulating in Beijing could be classified into at least three lineages regardless of their host origins . Two potential recombination events that happened in lineage #1 were detected and supported by comparative phylogenetic analysis . The SEOV strains in different lineages and strains with distinct special amino acid substitutions for N protein were partially associated with different spatial clustered areas of HFRS . Hotspots of HFRS were found in Beijing , a novel endemic region , where intervention should be enhanced . Our data suggested that the genetic variation and recombination of SEOV strains was related to the high risk areas of HFRS , which merited further investigation .
Hantaviruses are rodent-borne pathogens with a worldwide distribution . More than 50 hantaviruses have been found in the world [1]–[3] , each of which appears to have coevolved with a specific rodent or insectivore host [4] . As with other members of the family Bunyaviridae , hantaviruses are enveloped , negative-sense RNA viruses . The genome consists of three segments , designated as large ( L ) , medium ( M ) , and small ( S ) . They respectively encoded the RNA polymerase , the glycoprotein precursor ( GPC ) protein that is processed into 2 separate envelope glycoproteins ( Gn and Gc ) , and the nucleocapsid ( N ) protein [1]–[4] . As nucleoproteins of many negative-strand RNA viruses , hantaviral N protein is a multifunctional molecule involved in various interactions during the life cycle of the virus . It has important functions in the viral RNA replication , encapsidation and also in the virus assembly [5] . Hantavirus can cause two kinds of human illnesses , hantavirus cardiopulmonary syndrome ( HCPS ) and hemorrhagic fever with renal syndrome ( HFRS ) . HCPS is caused by New World hantaviruses circulating in north America and south America . HFRS , a disease characterized by renal failure , hemorrhages , and shock with a case fatality of 0 . 1% to 10% , occurs primarily in Asia and Europe [1]–[3] . HFRS is highly endemic in mainland China accounting for 90% of the total cases reported in the world [6] . Although integrated intervention measures involving rodent control , environment management and vaccination have been implemented , HFRS remains a significant public health problem with more than 10 , 000 human cases diagnosed annually [7] . Hantaan virus ( HTNV ) and Seoul virus ( SEOV ) mainly carried by Apodemus agrarius ( striped field mouse ) and Rattus norvegicus ( Norway rat ) , respectively , were known to be the crucial causative agents of HFRS in China [7] , [8] . In addition , Amur virus ( AMRV ) and Puumala virus ( PUUV ) were detected recently from Apodemus peninsulae and Clethrionomys glareolus respectively in northeastern China [9] , [10] . HFRS mainly occurred in rural area in the past . But recently , the endemic areas of the disease have extended from rural to urban areas and even to city centers [11] . Beijing metropolis is a newly affected region of HFRS , where the incidence of the disease has rapid increased since 1997 and the cases have been reported in all the 18 districts . The HFRS incidence seemed to be diverse considerably in difference places of Beijing according to the report from Beijing Center for Disease Control and Prevention ( CDC ) . Previous epidemiological surveys revealed that hantaviruses detected in Beijing were all SEOV strains [12] , [13] , [14] . Although the environmental factors were related to the SEOV infectivity in rodent hosts and humans [15] , [16] , the hotspots of HFRS remained unclear and environmental factors weren't able to explain fully the distributional variation in incidence of disease in human . The objectives of this study were to detect “hotspots” of HFRS in Beijing metropolis for effective control , to characterize variance of SEOV from the novel endemic region , and to investigate the possible association between SEOV genetic clusters and HFRS “hotspots” .
The research involving human materials was approved by the Ethical Review Board , Science and Technology Supervisory Committee at the Beijing Institute of Microbiology and Epidemiology . The informed consents were written by patients or their guardians and the related information was used anonymously . The research involving animal samples was approved by Animal Subjects Research Review Boards of the authors' institution and the study was conducted adhering to the institution's guidelines for animal husbandry . Records on HFRS cases reported in Beijing between 2004 and 2006 were obtained from the National Notifiable Disease Surveillance System ( NNDSS ) . The vectorization of the village , street , and boundaries of each township was performed on a 1∶100 , 000 scale topographic map and digital map layers were created in ArcGIS 9 . 0 software ( ESRI Inc . , Redlands , CA , USA ) . Demographic information was integrated in terms of the administrative code . Each HFRS case was geo-coded according to their possible infected sites and a layer including information on HFRS cases was created and overlapped on the above digital map . To identify the geo-spatial clusters i . e . , “hotspots” with high HFRS risk of infection , the spatial scan statistic [17] , [18] was performed by using SaTScan software [19] . The incidence rates of HFRS in 223 townships were calculated by using the fifth national census data in 2000 . The maximum spatial cluster size was set to be 5% of the total population at risk and 9999 Monte Carlo replications were used to test the null hypothesis that the relative risk ( RR ) of HFRS was the same between any townships or their collection and remaining townships . A P-value<0 . 05 was considered statistically significant . RNA extraction and reverse transcription reaction were performed as described previously [14] . The complete S sequences were generated from overlapping fragments by polymerase chain reaction ( PCR ) ( primer pairs were presented in Table S1 ) . Partial L sequences were generated using primer pairs previously described [20] . The sequences of SEOV strains were aligned using Clustalx1 . 8 software [21] . Phylogenetic trees were generated using the Bayesian Metropolis-Hastings Markov Chain Monte Carlo ( MCMC ) tree-sampling methods implemented by Mr . Bayes 3 . 1 software [22] , using the GTR evolutionary model , with gamma- distributed rate variation across sites and a proportion of invariable sites . The run were stopped until the standard deviation of split frequencies was below 0 . 01 . For comparison , phylogenetic analysis was also performed with the maximum- likelihood algorithm using Phylip software [23] . ML topologies were evaluated by bootstrap analysis of 100 ML iterations . The automated suite of algorithms in the Recombination Detection Program 3 ( RDP3 ) ( available from http://darwin . uvigo . es/rdp/rdp . html ) [24] was used to detect the recombination events . The programs included RDP , GENECONV , BootScan , MaxChi , Chimaera , SiScan , PhylPro , LARD , and 3Seq implemented in RDP3 . According to the identified breakpoints , phylogenetic trees from spliced alignments were constructed by Bayesian methods using Mr . Bayes 3 . 1 software .
A total of 322 confirmed HFRS cases were reported in Beijing metropolis from 2004 to 2006 . The incident rates of HFRS in 223 townships ranged from 0/100 , 000 to 27/100 , 000 . Three clustered areas ( hotspots ) were identified by spatial cluster analysis ( Figure 1 ) . The most likely clustered area , which was designated as clustered area A , contained 16 townships and located at the east of Beijing downtown , including a population of 570 , 000 . The Relative Risk ( RR ) of the most likely clustered area was 5 . 45 ( P<0 . 001 ) . Clustered area B contained 14 townships , including a population of 650 , 000 with a RR of 3 . 57 ( P = 0 . 002 ) . This “hotspot” located at the northwest of Beijing and adjoined Hebei Province . Clustered area C contained 11 townships , including a population of 230 , 000 with a RR of 3 . 30 ( P<0 . 001 ) . The entire S segment nucleotide sequences or partial L segment sequences of SEOV were obtained from 7 R . Norvegicus locating in different areas in Beijing ( Rn-M11 , Rn-DC8 , Rn-YUE12 , Rn-CP7 , Rn-HD11 , Rn-HD27 , Rn-SHY17 ) , 1 isolate ( BjHD01 ) from R . Norvegicus and 8 HFRS patients ( HuBJ3 , HuBJ7 , HuBJ9 , HuBJ15 , HuBJ16 , HuBJ19 , HuBJ20 and HuBJ22 ) . The nucleotide sequences of the entire S segment of SEOV strains obtained from the HFRS patients and rodents had 97 . 0%–99 . 5% nucleotide sequences identity with each other and 95 . 0%–98 . 0% identity with those of other SEOV strains deposited in GenBank . 430-nucleotide L genomic sequences showed 97 . 4%–100% nucleotide sequence identity with each other and 93 . 7%–99 . 1% identity with those of other SEOV . The phylogenetic tree based on entire coding region sequence of S segment showed that the strains circulating in Beijing clustered into three distinct lineages regardless of their host origins ( Figure 2 , Figure S1 ) ( HuBJ20 was not included because it was obtained from a patient who got the disease in another place far away from Beijing and only was diagnosed in a hospital of Beijing ) . The phylogenetic tree based on complete S sequence showed the similar topology structure ( data not shown ) . Five strains from HFRS patients clustered together with a rodent-originated sequences ( Rn-SHY17 ) , designated as #1 . Two sequences from HFRS patients clustered together with six rodent-originated sequences ( including BjHD01 ) , designated as #2 . One strain from a rodent ( Rn-HD27 ) was distinct from other Beijing strains but close to those from Zhejiang Province where is more than 1 , 000 km away from Beijing , which was designated as #3 . The topology of the trees based on partial L sequences was similar to that based on S segment . In recombination analysis , three significant potential recombination events ( PRE ) were detected and two of them involved in the strains circulating in Beijing ( Table 1 ) . One of them involved HuBJ16 strain , whose major parent was Rn-YUE12 and minor parent was HuBJ19 . Another PRE happened in HuBJ22 and HuBJ7 with HuBJ19 as the major parent and HuBJ3 as minor parent . In the Phylogenetic trees constructed using sequences of either the putative recombinant region or the region without recombination , changes in the topology of the trees could be observed ( Figure 3 ) . However , In the Phylogenetic trees according to the breakpoints of HuBJ22 strain , it was weakly supported , although the change in the topology of the trees could also be observed ( data not shown ) . By contrast , no evidence of recombination was evident for the partial L segment sequence . The 3 spatial clustered areas of HFRS seemed associated with different SEOV strains . All SEOV strains in lineage #1 were from clustered area A and clustered area B , although the two clustered areas were not contiguous to each other . Apart from HuBJ15 , four patient-originated strains and one rodent-originated strain had a special homologous substitution of asparagine to threonine at position 259 of the deduced amino acid sequences of the N protein , which was distinct from all other SEOV strains . Interestingly , all the strains involving PRE belonged to lineage #1 . Most strains from rodent hosts were in lineage #2 and scattered in different areas of Beijing . Among them , four rodent-originated strains ( Rn-M11 , Rn-DC8 , Rn-YUE12 and Rn-CP7 ) had a same special homologous substitution of asparagine to serine at amino acid position 214 , which had never been detected from any other SEOV strains . None of the 4 strains were found in any spatial clustered areas with high RR value . Strains without the homologous substitution at position 214 in lineage #2 could be divided into two parts . One strain from a patient ( HuBJ 9 ) and two strains ( Rn-HD11 , BjHD01 ) from the rodent hosts located in clustered area C . Another strain ( HuBJ3 ) from a patient located in southwest of Beijing , an area with low RR value . Rn-HD27 strain in lineage #3 without any special substitution also located in clustered area C .
Cluster analyses are important in epidemiology in order to detect aggregation of disease cases , to test the occurrence of any statistically significant clusters , and ultimately to find evidences of etiologic factors . Cluster analysis identifies whether geographically grouped cases of disease can be explained by chance or are statistically significant . Spatial scan statistic [17] , [18] implemented in SaTScan software [19] is being widely used to detect the clusters of many infectious diseases [25] , [26] . Recently , we analyzed the distribution of HFRS cases nationwide using GIS-based spatial analysis and highlight geographic areas where the population had a high risk of acquiring the disease [27] . Beijing is one of the emerging endemic areas of HFRS in recent years . In this study , the district-based digital map layers and three-year surveillance data were analyzed altogether and a moving-window scan statistics approach were used to determine the geo-spatial “hotspots” . It reduced the effects resulted from probable reporting bias and selecting bias . The results of the study suggested that there were three spatial “hotspots” of HFRS in Beijing , where the population had a high risk of acquiring the disease and intervention should be enhanced . Mice of species Apodemus agrarius are quite close to people in rural areas . Consequently , HTNV is still the major cause of HFRS in China . However , more and more HFRS patients caused by SEOV were reported in mainland China . It is possibly related to its animal host , R . Norvegicus , which is spatially more close to human beings than any hosts of other hantaviruses . Sometimes , they can even migrate to the places far away by traveling on vehicles such as ship , train or truck [4] . They may cause international transmission by carrying HV to another place , just as presumed in Taiwan and Indonesia [28] , [29] . Beijing is the capital of China , transportation of goods and human migration with other regions that followed the rapid economic development was quite frequent in recent years . It is not surprising that several lineage of SEOV were circulating simultaneously in Beijing and appearance of recombination . It was reported that prevalence of SEOV in rodents was different among districts in Beijing [30] . However , it was not consistent with clustered areas based on GIS-based spatial analysis , suggesting other factors might be related to the incidence of the disease in human . Incorporating the additional genetically related taxa into a typical analysis usually leads to better phylogenetic resolution and comprehensive understanding . It had reported the phylogenetic tree based on partial M segment of most strains from rodents in Beijng ( 30 ) . Since the amount of available biological material from patients was limited , we proceeded to amplify and sequence the entire S genome segment and partial L segment . The S-segment phylogenetic tree indicated that at least 3 lineages of SEOV were circulating in Beijing ( figure 2 ) . Strains in the first lineage coincided with the spatial clustered areas of HFRS with high RR values and most strains had special substitution of asparagine to threonine at amino acid position 259 of the N protein . All the strains involving recombination signals in Beijing were all in lineage #1 and located in HFRS clustered areas with the highest RR value . It seemed that the PRE was not a seldom event in these areas since more than one PRE were detected . Our data , together with a number of other studies , suggested that homologous recombination events might be not an uncommon process in hantavirus natural evolution [31]–[33] . In lineage #2 , some strains had substitution of asparagine to serine at position 214 of the N protein . All of them could only be detected from rodent hosts and located in areas with lower risk of HFRS occurrence . The relationship between the hantavirus lineages and the cluster areas could be observed in partial L-segment phylogenentic tree . When compared with previous study based on partial M segment , there was another lineage circulating in Beijing ( 30 ) . Moreover , the relationship between the hantavirus lineages and the cluster areas could also be observed except that two strains distributed in areas with lower risk of HFRS ( Dongcheng district ) seemed fall into the lineage #1 ( 30 ) . It should be noted that the captured sites of the two rodents were in a railway station and were closed with the clustered area A , which might be the reason for the two strains different from strains in areas with low risk of HFRS but clustered together with strains in lineage #1 . Longer sequence such as complete S segment analysis of the two strains might be helpful to elucidate their phylogeny and genetic characteristic . Unfortunately , the limited amount of available biological materials from the two rodents hindered us to complete the task . Our findings , together with previous study , indicated that several lineage of SEOV were circulating simultaneously in Beijing . Furthermore , it suggested that genetic characteristics of SEOV might be associated with some risk areas of HFRS in Beijing . This correlation could be that different strains circulating in different areas were evolving at a different rate or different pattern due to environmental diversity , hence accumulated different mutations . On the other hand , mutations and recombination might play an important role for SEOV to adapt to hosts in an emerging endemic area such as Beijing . It seemed that the mutations at amino acid position 214 and 259 of the N protein didn't happened by chance , since the same mutations happened in more than one strains from several areas and these mutations seemed to consistent with the spatial cluster areas . Compared with other hantaviruses , the nucleotide sequences of SEOV seemed to be more conservative . The S genome segment nucleotide sequences identity of SEOV ranged from 93 . 7% to 99 . 5% and amino acid sequences identity ranged from 96 . 6% to 100% . Although there were some evidences that the two observed mutations located in regions with the important function to N protein for some hantaviruses [34]–[37] , laboratorial data about the effect of the mutation to the biological function of S segment gene products were absent at present . This issue needs to be further evaluated .
|
Hemorrhagic fever with renal syndrome ( HFRS ) is caused by Hantaviruses , the enzootic viruses with a worldwide distribution . In China , HFRS is a significant public health problem with more than 10 , 000 human cases reported annually and the endemic areas of the disease have extended from rural to urban areas and even to central cities in recent years . The HFRS incidence has increased recently and the morbidity seemed to be considerably diverse in different areas in Beijing , the capital of China . With the aim of gaining more information to control this disease , we carried out a spatial analysis of HFRS based on the data from human cases during 2004–2006 and investigated the genetic features of complete S and partial L segment sequences of Seoul virus from natural infected rodent hosts and patients . We found three geo-spatial clusters , i . e . , “hotspots” of HFRS in Beijing , where intervention should be enhanced . Our data indicated that the genetic variation and recombination of SEOV might be related to the high risk areas of HFRS in Beijing , which was worthy of further investigation .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"virology/virus",
"evolution",
"and",
"symbiosis",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"infectious",
"diseases/viral",
"infections",
"microbiology/medical",
"microbiology",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] |
2011
|
Geo-spatial Hotspots of Hemorrhagic Fever with Renal Syndrome and Genetic Characterization of Seoul Variants in Beijing, China
|
In fungi , mating between partners depends on the molecular recognition of two peptidyl mating pheromones by their respective receptors . The fission yeast Schizosaccharomyces pombe ( Sp ) has two mating types , Plus ( P ) and Minus ( M ) . The mating pheromones P-factor and M-factor , secreted by P and M cells , are recognized by the receptors mating type auxiliary minus 2 ( Mam2 ) and mating type auxiliary plus 3 ( Map3 ) , respectively . Our recent study demonstrated that a few mutations in both M-factor and Map3 can trigger reproductive isolation in S . pombe . Here , we explored the mechanism underlying reproductive isolation through genetic changes of pheromones/receptors in nature . We investigated the diversity of genes encoding the pheromones and their receptor in 150 wild S . pombe strains . Whereas the amino acid sequences of M-factor and Map3 were completely conserved , those of P-factor and Mam2 were very diverse . In addition , the P-factor gene contained varying numbers of tandem repeats of P-factor ( 4–8 repeats ) . By exploring the recognition specificity of pheromones between S . pombe and its close relative Schizosaccharomyces octosporus ( So ) , we found that So-M-factor did not have an effect on S . pombe P cells , but So-P-factor had a partial effect on S . pombe M cells . Thus , recognition of M-factor seems to be stringent , whereas that of P-factor is relatively relaxed . We speculate that asymmetric diversification of the two pheromones might be facilitated by the distinctly different specificities of the two receptors . Our findings suggest that M-factor communication plays an important role in defining the species , whereas P-factor communication is able to undergo a certain degree of flexible adaptation–perhaps as a first step toward prezygotic isolation in S . pombe .
Reproductive isolation , which restricts gene flow between sympatric populations , is one of the key mechanisms of speciation [1] . Mating between individuals of closely related species is prevented by a prezygotic barrier , which is mainly caused by changes in signals that enable individuals to appropriately recognize the opposite sex: for example , pheromones in insects [2 , 3] and amphibians [4 , 5] , body color in fish [6] , and song in birds [7] . Although such reproductive isolation has been frequently studied in higher organisms , far less is known in fungi [8] . In ascomycetes including yeasts , mating between partners critically depends on the molecular recognition of peptidyl mating pheromones by receptors [9 , 10] . Our recent study in the fission yeast S . pombe demonstrated that several mutations in a pheromone and its corresponding receptor created a new prezygotic barrier that can give rise to a new species [11] . This experimental observation supports the idea that pheromone/receptor systems drive reproductive isolation through very subtle variations in nature . Thus , genetic alterations of pheromones and their receptors are likely to be important to promote speciation in yeasts . More generally , however , loss of pheromone activity may result in extinction of an organism’s lineage; therefore , changes in the mating pheromone systems might occur gradually and/or coincidently before speciation happens . This hypothesis is an attractive explanation for the speciation process in yeasts , but the mechanisms of genetic alterations of pheromone/receptor systems in nature remain to be elucidated . S . pombe has two mating types , Plus ( P ) and Minus ( M ) [12 , 13] . Under nitrogen-limited conditions , two haploid cells of opposite mating types mate via reciprocal stimulation of their mating pheromone receptors [14] . P cells secrete a mating pheromone called P-factor , a simple 23-amino acid peptide , which is recognized by its corresponding G-protein coupled receptor ( GPCR ) mating type auxiliary minus 2 ( Mam2 ) on M cells [15 , 16] . The mating type auxiliary plus 2+ ( map2+ ) gene encodes a precursor polypeptide containing four tandem repeats of mature P-factor , which is a mixture of three different peptides in the laboratory strain . P-factor is secreted by the standard secretory pathway [16] . On the other hand , M cells secrete a mating pheromone called M-factor , which is recognized by its GPCR Map3 on P cells [17] . Mature M-factor , a farnesylated and methylated peptide of nine amino acids , is encoded by three redundant genes: mfm1+ , mfm2+ , and mfm3+ [18 , 19] . Each of these genes generates a precursor containing a single copy of the same M-factor sequence . M-factor is secreted specifically by the ATP-binding cassette ( ABC ) transporter Mam1 [20] . S . pombe M cells also produce a pheromone-degrading enzyme encoded by the sexually activated 2+ ( sxa2+ ) gene [21–23] . Sxa2 is a serine carboxypeptidase that specifically degrades extracellular P-factor . The C-terminal Leu residue of P-factor is removed by Sxa2 , which is secreted by M cells [22] , and the resulting P-factor lacking Leu is inactive and not recognized by Mam2 [24] . Yeast cells sense a gradient of pheromones secreted by the opposite cell and then extend a mating projection toward the pheromone source [25] . Degradation of the pheromones by the peptidase is thought to make the gradient more stable [26] . In contrast , an enzyme that degrades M-factor has not yet been found . Instead , expression of the mfm genes encoding M-factor might be differentially controlled to facilitate fine tuning . These differences in the two mating pheromones , including chemical structure , secretion pathway , and degradation , are widely common in ascomycetes [9] , but the biological significance remains unclear . The standard laboratory strain of S . pombe , L968 , has four copies of the P-factor–encoding sequence and three genes encoding M-factor [27] . Hence , evolution has a mechanism for creating new versions of pheromones , while at the same time , cells retain the ability to mate via the original versions; therefore , we hypothesized that the redundancy in the pheromones might allow unrestricted diversification . In this study , we examined how pheromones and their receptors coevolve in nature . First , we examined pheromone diversity by determining the nucleotide sequences of the pheromone and receptor genes in 150 wild S . pombe strains [28 , 29] , finding that the amino acid sequence of M-factor and Map3 was completely conserved , whereas that of P-factor and Mam2 was very diverse . In some strains , for example , the copy number of P-factor increased to 5–8 repeats . Second , we analyzed the specificity of pheromones–receptor recognition between S . pombe and the related species S . octosporus . Whereas So-M-factor was not functional in S . pombe , all So-P-factors tested were partially functional in S . pombe , enabling these cells to mate successfully using So-P-factors . Thus , recognition of M-factor is highly stringent , whereas that of P-factor is relatively relaxed . Our findings suggest that M-factor communication plays an important role in partner discrimination , whereas P-factor communication allows flexible adaptation to create variations . We speculate that such an asymmetric pheromone/receptor system may have evolved to create a prezygotic barrier in S . pombe .
To investigate the diversity of S . pombe mating pheromones and their corresponding receptors in nature , we analyzed 150 wild strains ( S1 and S2 Tables ) whose origins differ from the standard laboratory strain , L968 , first described by U . Leupold [27] . These strains were derived from various countries and regions ( Fig 1A ) and were isolated from several different sources ( S2 Table ) [28] . We sequenced the M-factor genes ( mfm1 , mfm2 , and mfm3 ) , the receptor gene for M-factor ( map3 ) , the P-factor gene ( map2 ) , and the receptor gene for P-factor ( mam2 ) of all 150 strains and compared the nucleotide sequences with that of the L968 strain registered in the database ( PomBase , https://www . pombase . org ) . We constructed a phylogenetic tree based on the sequences of these six genes in the 151 S . pombe strains ( Fig 1B ) ( trees for the individual genes are shown in S1 Fig ) . Many nucleotide differences in these genes were found among the strains ( S3 and S4 Tables ) . As shown in Fig 1B , the sequence patterns of these strains were relatively diversified , with characteristic patterns depending , in part , on region ( i . e . , Europe or South America ) . Notably , three mfm genes of all 151 strains ( i . e . , 453 genes in total ) produced an identical mature M-factor peptide , YTPKVPYMCFar-OCH3 ( 102 genes have been previously reported [33] ) . Many mutations were found in the prosequences and introns , but only one mutation was found in the mature M-factor–encoding region; this was a synonymous change that did not cause an amino acid substitution ( S4 Table ) . Moreover , the amino acid sequences of Map3 in the 150 strains were also identical to that in the L968 strain ( Table 1 ) . In other words , the amino acid sequences of the M-factor/Map3 pair seem to be completely conserved in nature . In contrast , the sequences of the map2 genes were very diverse ( S3 and S4 Tables ) . Whereas the map2 gene in L968 carries four P-factor–encoding tandem repeats ( P1-P2-P3-P2 ) , extensive variations in the number of repeats were observed in the 150 strains , ranging from four to eight ( Fig 2A ) . In addition , the map2 genes of the wild strains were predicted to produce six different mature P-factor peptides ( P1–P6; see Fig 2B ) . Interestingly , there were five different amino acid sequences of Mam2 across the 150 strains ( Table 1 ) . Thus , the amino acid sequences of the P-factor/Mam2 pair seem to be very diverse in nature . Collectively , these findings show that the two mating pheromones in S . pombe have diversified asymmetrically . Having observed increased numbers of P-factor–encoding repeats in the map2 gene in about half of the 150 strains ( Fig 2A ) , we tested whether repeat number directly affects mating frequency . The native 4-repeat Map2 open reading frame ( ORF ) from the L968 strain was replaced with an ORF carrying different numbers of the P2 repeat , which was first characterized as P-factor of S . pombe [16] ( see Materials and methods ) . We found that a decrease in P2 repeat number ( <4 repeats ) resulted in an extremely low frequency of zygotes ( Fig 2C ) . For example , the strain with the 3-repeat ORF produced less than one-tenth of the zygotes ( % ) of the strain with the 4-repeat ORF ( Fig 2C ) . However , few significant differences in mating efficiency were observed among the strains carrying an ORF with more than 4 repeats ( S2A Fig ) . To compare the activity of the different P-factor peptides ( P1–P6 ) , we introduced a modified map2 gene carrying four tandem repeats of each P-factor into the P-factorless strain ( FY23418; S1 Table ) , in which the native map2+ gene had been deleted ( see Materials and methods ) . The resulting strains each produced one of the six P-factors . The mating efficiency of these strains was assessed by the frequency of zygotes . The strain producing four P2 peptides from the map2 gene showed a high frequency of zygotes ( 66 . 9% ± 4 . 4% ) ; remarkably , however , the zygote frequency of the remaining strains producing the other P-factor peptides ( P1 , P3–P6 ) was extremely low or zero ( Fig 2D ) . Furthermore , in a strain in which the native map2+ gene carrying a sequence of repeats ( P1-P2-P3-P2 ) was introduced , the frequency of zygotes was fairly high ( 76 . 6% ± 5 . 1% ) , despite the production of only two copies of the P2 peptide ( Fig 2D ) . To determine whether the order of nucleotide sequences encoding mature P-factor affects zygote frequency , we also introduced a map2 gene carrying a permuted sequence of repeats ( P2-P3-P2-P1 ) into the FY23418 strain . Similar to the strain with the native sequence , this strain also mated at high frequency ( 74 . 2% ± 1 . 7% ) ( Fig 2D ) ; therefore , the effect of peptide-coding position appears small . Next , we considered that if mature P-factor production is modulated by a length-dependent biosynthetic pathway , the low mating efficiency observed in strains carrying an ORF with fewer than 4 repeats might be attributed to translation level . To examine this possibility , we constructed a map2 gene carrying the four tandem P-factor–encoding repeats P5-P2-P5-P2 and introduced it into the FY23418 strain . Surprisingly , however , this strain was almost as sterile as the strain with the 2-repeat ORF ( Fig 2C and 2D ) . Thus , the apparently inactive P1 and P3 peptides might have important roles during the mating process . The amino acid sequences of the P1 and P3 peptides slightly differ from that of the P2 peptide ( Fig 2B ) . In fact , some peptides with slight mutations of P1–P3 had markedly decreased mating efficiency ( S2B Fig ) . These results indicate that the substitution of only a few amino acids of P-factor have a large influence on copulation . To investigate further the causes of the low frequencies of the P-factor peptides ( P1 , P3–P6 ) , we constructed strains carrying two map2 genes encoding four tandem repeats of each P-factor ( i . e . , eight copies in total ) and examined the zygote frequency of these strains in the same way . Remarkably , the strains producing eight copies of peptide showed a much higher frequency of zygotes ( Fig 2E ) relative to those carrying one gene ( Fig 2D ) . We considered that this is probably due to a dose-dependent effect . Overall , these results indicated that the P2 peptide is likely to be most compatible with the native Mam2 receptor; curiously , however , no wild strains that we investigated generated a precursor containing only copies of the P2 peptide sequence . As described above , the map2 gene was found to contain at least four tandem repeats encoding multiple P-factor peptides in all 150 wild strains . To assess the effect of producing multiple P-factors , we performed a quantitative competitive mating assay . In this experiment , P cells producing a multiple P-factor ( P1-P2-P3-P2 ) and P cells producing a single P-factor ( P2-P2-P2-P2 ) were inoculated with wild-type M cells onto malt extract agar ( MEA ) plates at a cell number ratio of 1:1:2 . The three strains were differentially marked by different drug-resistant markers ( see Materials and methods ) . After incubation for 24 hours , the cell suspension was spread onto yeast extract agar ( YEA ) plates containing combinations of the appropriate drugs . Doubly resistant hybrid descendants of mating between P cells and M cells were counted to determine the recombinant frequency . The assay indicated that M cells showed a slight preference to mate with P cells producing a multiple-type P-factor as compared with a single-type P-factor ( Fig 3 ) . This tendency was not influenced by the type of drug-resistant marker used ( Fig 3 ) . These results suggest that the production of multiple P-factors might be advantageous for mate choice by M cells . In S . pombe , P-factor is largely degraded outside the cell by the carboxyl peptidase Sxa2 [22] . Next , therefore , we examined whether each P-factor peptide is truly recognized by Mam2 in the absence of Sxa2 . An M-type strain lacking Sxa2 ( TS402; S1 Table ) was treated with synthetic P-factors at different concentrations ( 0–1 , 000 nM ) in nitrogen-free liquid medium ( EMM2−N ) . Yeast cells elongate a mating projection ( “shmoo” ) when they sense sufficient pheromones; therefore , the ability of Mam2 to recognize the different P-factors was determined by measuring the ratio between the length ( L ) and width ( W ) of an individual cell ( see Materials and methods ) . Here , we defined cells with an L/W ratio of 2 . 0 or more as shmooing cells . The addition of P2 peptide clearly induced cell elongation at 10 nM after 1 day of incubation . According to our definition , one-third of cells treated with P2 peptide ( 10 nM ) became shmooing cells in response to P-factor after 1 day ( S3 Fig ) . Notably , cells treated with 10 nM P1 , P3 , P4 , and P6 also elongated , indicating that these peptides were recognized by Mam2 . As compared with P2 peptide , however , the proportion of shmooing cells after treatment with these peptides was significantly low ( 15%–23% at 10 nM; see S3 Fig ) . In contrast , the P5 peptide , produced only by the 24CBS5682 strain ( S2 , S3 and S4 Tables ) , was not recognized by Mam2 even when the cells were treated with a concentration of 1 , 000 nM ( S3 Fig ) . To measure the activity of the P5 peptide accurately , we used a P-factor–sensitive strain ( TS578; see Materials and methods ) and treated the cells with 1 , 000 nM synthetic P5 peptide . As shown in S4 Fig , no cells underwent shmoo elongation . This result indicates that the P5 peptide is not recognized by native Mam2 , as far as we have examined . In conclusion , the shmoo formation assay indicated that most P-factor peptides are sufficiently recognized by Mam2 in vitro . Sxa2 removes the C-terminal Leu residue of P-factor [22] . Based on the above results , we considered that differences in the processing of each P-factor peptide by Sxa2 might affect pheromone-based mate choice . To examine this possibility , the sxa2+ ORF was cloned into the pTS111 plasmid downstream of the no message in thiamine 1+ ( nmt1+ ) promoter , which is strongly expressed in absence of thiamine [34] ( see Materials and methods ) . The resulting plasmid ( pTS284 ) was then introduced into the TS402 strain lacking Sxa2 . The resulting cells were grown in EMM2 medium without thiamine to induce the nmt1+ promoter , and the culture supernatant was assayed to confirm carboxypeptidase activity ( see Materials and methods ) . Next , each P-factor peptide ( 200 μM ) was mixed with an aliquot of the cell-free culture supernatant including abundant active Sxa2 ( total protein 100 ng ) for up to 60 minutes , and the amount of leucine released from each P-factor was determined as a measure of Sxa2 activity . All six P-factor peptides ( P1–P6 ) were sufficiently degraded after 60 minutes in the presence of Sxa2 ( Table 2 ) . Unexpectedly , degradation of P2 peptide was found to be more efficient than that of other P-factor peptides . In 10 minutes , approximately 70% of P2 peptide ( 133 . 0 ± 21 . 7 μM ) was degraded , as compared with , for example , approximately 50% of P1 ( 97 . 7 ± 5 . 8 μM; see Table 2 ) . These results suggest that native Sxa2 might not efficiently degrade P-factor peptides containing a few residues that differ from P2 peptide . Thus , the P2 peptide is likely to be the best substrate for Sxa2 protease . In fact , sxa2 deletion mutants are virtually sterile [23] . Hence , degradation of P-factor seems to be important for the correct orchestration of mating . We speculate that the diversification of P-factor probably affects the selection of mates by M cells . As described above , the amino acid sequences of M-factor were completely conserved , whereas those of P-factor were diversified in 150 wild S . pombe strains analyzed . To determine whether this asymmetry in pheromone diversity is common to other species , we analyzed the nucleotide sequences of both M-factor and P-factor genes in S . octosporus , the species most closely related to S . pombe . Whole-genome sequences of S . octosporus have been determined by the Broad Institute [35] and indicate that this species has six M-factor–encoding genes ( hereafter called “So-mfm1–So-mfm6” ) and one P-factor–encoding gene ( hereafter called “So-map2” ) . The six redundant genes ( So-mfm1–So-mfm6 ) encode M-factor peptides with the same amino acid sequence ( Fig 4A ) ; therefore , the primary structures of the putative So-M-factors are the same , YQPKPPAMCFar-OCH3 ( S5A Fig ) . In contrast , the So-map2 gene carries seven tandem So-P-factor repeats , which encode four different So-P-factors ( Fig 4B and S5C Fig ) , similar to S . pombe . Interestingly , therefore , the two pheromones have also diversified asymmetrically in this closely related species . Next , we assessed whether the pheromone peptides of S . octosporus are effective on S . pombe cells . First , the wild-type S . pombe mfm1+ gene was integrated into the genome of an M-factorless strain ( FY23412; S1 Table ) , which led to high mating efficiency ( 72 . 1% ± 3 . 7% ) ( Fig 4C ) . Next , we replaced the Sp-M-factor–encoding sequence in the mfm1+ gene with the So-M-factor–encoding sequence ( mfm1So ) . The resulting strain , which produced only So-M-factor instead of Sp-M-factor , was found to be completely sterile ( Fig 4C ) . In short , S . pombe P cells were unable to mate with M cells producing So-M-factor . In contrast , replacement of the Sp-P-factor–encoding sequence ( 4 repeats ) in the map2+ gene with the So-P-factor–encoding sequence ( 7 repeats ) ( map2So ) , resulted in a strain with approximately half the mating frequency of the strain with the map2+ gene ( 35 . 8% ± 5 . 1% ) ( Fig 4D ) . Thus , S . pombe M cells can mate with P cells that produce So-P-factors , indicating that at least one of the So-P-factors is effective on S . pombe cells . We also replaced the So-P-factor–encoding sequence ( 7 repeats ) in the So-map2+ gene with the Sp-P-factor–encoding sequence ( 4 repeats ) ( So-map2Sp ) in S . octosporus cells . The resulting strain , which produced only Sp-P-factor instead of So-P-factor , retained mating ability ( 17 . 3% ± 4 . 9%; see Fig 5 ) . Thus , at least one of the Sp-P-factors is also effective on S . octosporus cells . Three of the nine amino acids of So-M-factor were found to differ from those of Sp-M-factor ( S5A Fig ) . To assess whether So-M-factor is recognized at all by the M-factor receptor of S . pombe ( Sp-Map3 ) , we carried out a shmooing assay ( see S5B Fig ) in which we measured the L/W ratio of cells treated with synthetic So-M-factors at various concentrations in EMM2−N . The M-factor-sensitive strain ( TS405; S1 Table ) significantly induced cell elongation after one day of incubation with 100 nM Sp-M-factor but showed no elongation when treated with So-M-factor ( S5B Fig ) . Under these conditions , approximately 40% of cells treated with Sp-M-factor peptide underwent shmoo formation , whereas no cells treated with So-M-factor peptide underwent shmooing ( S5B Fig ) . When the concentration of So-M-factor was raised to 1 , 000 nM , the cells underwent shmoo elongation a little ( S5B Fig ) . These results imply that S . pombe is reproductively isolated from S . octosporus , owing to the lack of compatibility of M-factor . On average , eight of the 23 amino acids of P-factor differed between the two species . To test whether the S . octosporus P-factor peptides ( hereafter named P1’–P4’; S5C Fig ) are recognized by the P-factor receptor of S . pombe ( Sp-Mam2 ) , we treated TS402 with each of the four synthetic So-P-factor peptides . Remarkably , cells formed visible shmoos in all cases ( S5D Fig ) . The most effective peptide was P2’; that is , one-fifth of S . pombe cells treated with P2’ peptide ( 10 nM ) elongated a shmoo in response to P-factor after one day , and the proportion of shmooing cells increased to about 80% at a peptide concentration of 100 nM ( S5D Fig ) . In contrast , recognition of the P4’ peptide by Sp-Mam2 was relatively low . Taken altogether , these findings indicate that all of the So-P-factors have a partial effect on S . pombe M cells . The above findings showed that S . pombe cells can respond to So-P-factors produced by S . octosporus . We therefore examined whether the P-factor–degrading enzyme of S . pombe ( Sp-Sxa2 ) can degrade So-P-factors . Each of the synthetic So-P-factor peptides was mixed at 200 μM with culture supernatant containing active Sp-Sxa2 , and the amount of leucine released from each P-factor was measured at fixed time points , as described above for Sp-P-factors . All four So-P-factor peptides ( P1’–P4’ ) were considerably degraded in the presence of Sp-Sxa2 ( Table 2 ) . The P2’ peptide was degraded most efficiently , with 83 . 7 ± 4 . 0 μM degraded after 60 minutes of incubation; as compared with Sp-P-factor peptides , however , Sp-Sxa2 inefficiently removed the Leu residue from So-P-factor peptides ( Table 2 ) . In conclusion , Sp-Sxa2 acts most effectively on Sp-P-factors but has the capacity to degrade P-factor peptides from related species such as S . octosporus , probably limiting the interspecies mating response . Lastly , we examined the diversity of Sxa2 in nature . In the 150 wild S . pombe strains , there were four different amino acid sequences of Sxa2 ( Table 1 and S4 Table ) . In exploring the asymmetric diversification of the two mating pheromones , it is notable that M-factor is not degraded by a specific enzyme . We speculate that overlapping regions of P-factor ( probably the C terminus ) are recognized by both its receptor Mam2 and its degradation enzyme Sxa2 , thereby facilitating coevolution of their substrate specificities together with divergence of P-factor . Taken together , diversification of the two pheromones might be accelerated asymmetrically by the distinctly different specificities of the two receptors , in addition to the existence of Sxa2 .
Mutational alterations of the pheromone/receptor system can affect the recognition between mating partners , resulting in prezygotic isolation . In this study , we explored the diversification of the pheromones and cognate receptors of S . pombe in nature . We found that the amino acid sequences of M-factor and its receptor Map3 are completely conserved , whereas those of P-factor and its receptor Mam2 are very diversified ( Table 1 ) . Such asymmetric diversification of the two mating pheromones was also seen in the related species S . octosporus ( Fig 4A ) . Moreover , we noticed that So-M-factor was not functional on S . pombe cells , whereas all of the So-P-factors tested were partially functional; in other words , S . pombe cells were capable of mating with cells producing only So-P-factors ( Fig 4D ) and vice versa ( Fig 5 ) . Therefore , it is more likely that the recognition specificities of the two pheromone receptors vary in strictness for their respective pheromones . Map3 and Mam2 are both class IV GPCRs but differ clearly in their amino acid sequences . Recently , Rogers and colleagues [10] reported that a-type pheromones ( lipid peptide ) either promote efficient mating completely or do not promote it at all , while α-type pheromones ( simple peptide ) show a more graded distribution of mating efficiency in Saccharomyces cerevisiae . Thus , the different specificities of the two GPCRs might lead to asymmetric competence and diversification of pheromones . S . pombe has three genes encoding Sp-M-factor , and S . octosporus has six putative genes encoding So-M-factor ( Fig 4A ) . Such redundancy might enable the cells to alter one copy of M-factor to adapt to genetic changes in Map3 , while keeping the others unchanged . In fact , the N-terminal half of M-factor has been shown to be dispensable for recognition by Map3 [33] . Nevertheless , the redundant genes encoding M-factor peptides generate the same sequence of amino acids in nature ( Fig 4A ) . Wild-type S . pombe has four copies of P-factor , so in principle both pheromones should be free to evolve . We speculate that the amount and/or activity of M-factor might affect mating frequency . Previously , Nielsen and colleagues [19] reported that any one of the three genes of M-factor is sufficient for mating . However , this might not reflect the actual situation in nature . For example , efficient copulation might depend largely on the number of M-factor genes under more severe nutrient-limited conditions , resulting in restricted variation . Because M-factor is encoded by different genes , rather than by repeats , intergenic conversion can occur with considerable frequency [36] . Concerted evolution of the mfm genes might allow conservation of the sequence of mature M-factor and the rapid spread of favorable mutations . We could not confirm whether there is an increase or decrease in mfm genes in the 150 wild S . pombe strains , but the reason why the M-factor sequence is fixed will be investigated in a future study . In the wild S . pombe strains , the number of P-factor–encoding repeats in the map2 gene varied from four to eight ( Fig 2A ) . Variations in repeat number in the Saccharomyces genus have previously been reported [37 , 38] . For example , decreasing numbers of repeats in Mfα1 , a structural gene for α-factor pheromone , results in a stepwise decrease in α-factor production in S . cerevisiae [39] . Our experimental data also revealed that removing a single repeat of the 4-repeat sequence in map2 has a marked effect on mating frequency ( Fig 2C ) . Perhaps , decreasing numbers of repeats in the map2 gene might lead to a lower production of P-factor in S . pombe . In addition , a previous study suggested that the secretion pathway has a minimum size requirement for transportation [40] . Hence , the product of the map2 gene might need to have sufficient length for processing by the biosynthetic pathway . Although a higher repeat number in the map2 gene might result in increased P-factor production , further increases in repeat number do not necessarily lead to greater pheromone production because Rogers and colleagues [38] also showed that an 8-repeat strain is less favorable than a 6-repeat strain for mating choice in S . cerevisiae , probably due to reduced pheromone production caused by a decrease in the rate of translation . When the various map2 genes ( 4–8 repeats ) obtained from the 150 wild strains were integrated into the FY23418 strain , almost all of the resulting strains showed high mating frequency ( S2A Fig ) . The only exception was the map2 gene ( 6 repeats ) obtained from the 22CBS10468 strain ( map2_D8; S3 Table ) , in which a spacer sequence located between the fourth and the fifth repeat was changed from KKR to KKC ( S4 Table ) . The kexin-related endopeptidase kexin-related protease 1 ( Krp1 ) cleaves the KKR motif ( three basic amino acids ) in the Golgi during the biosynthetic pathway to generate mature P-factor [41]; therefore , such a mutation might affect the production of P-factor . Yeast cells choose a favorable partner producing the highest levels of pheromone , whereas a cell that cannot produce pheromones is not chosen as a mating partner by the opposite mating cell [42 , 43] . Such fluctuations in the repeat number of P-factor are likely to have an influence on various factors related to mating events . All of the wild S . pombe strains produced at least three different P-factors , as far as we investigated ( Fig 2A ) . Curiously , however , our experimental data clearly showed that the P2 peptide was much more efficiently recognized by Mam2 as compared with the others in vivo and in vitro ( Fig 2D and S3 Fig ) . Why don’t S . pombe cells produce only P2 peptides ? Interestingly , we found that cells producing multiple different P-factors seemed to be slightly preferred as a mate by the opposite cell type ( Fig 3 ) . This might be explained by the differences in the extent of degradation by Sxa2 . In fact , sxa2 deletion mutants are virtually sterile [23] . Recent studies by Martin’s group have clearly shown that the local pheromone gradient rather than the absolute pheromone concentration is important for efficient mating [26 , 44] . In this study , we revealed that all constructed strains lacking the sxa2+ gene showed fairly low zygote frequency ( S2D and S6 Figs ) , and although the P2 peptide is recognized more efficiently by native Mam2 , it is also degraded more rapidly by Sxa2 ( Table 2 ) . Therefore , we consider that the production of different peptides might affect the whole orchestration of mating by slightly changing the substrate specificity of Sxa2 , resulting in the diversification of P-factor . One of the possible reasons for the extremely low mating frequency of cells expressing each of the P1 , P3 , P4 , and P6 ( 4 repeats ) peptides ( Fig 2D ) is that these peptides are not expressed as effectively as P2 . However , we could not examine this possibility here . All functions of sexual differentiation ( i . e . , expression of pheromone and receptor genes ) are stimulated only by nitrogen starvation in S . pombe; hence , it is extremely difficult to transfer the majority of conventional methods used with Saccharomyces ( e . g . , Halo assay ) to S . pombe . Nielsen’s group developed an alternative technique based on the observation that pheromone stimulation is necessary to undergo meiosis and sporulation [45] . In our case , however , there are significant differences in bioactivity among the peptides; thus , it is hard to estimate the amount of each P-factor secreted . Therefore , a method that quantifies pheromone secretions independent of the bioactivity of peptides will be needed in S . pombe . Nevertheless , we believe that the P1 , P3 , P4 , and P6 peptides are certainly expressed because the strains carrying two map2 genes showed a much higher frequency of zygotes ( Fig 2E ) relative to those carrying one gene ( Fig 2D ) . We further noticed that there is a positive relationship between the recognition of P-factor by Mam2 and its efficient degradation by Sxa2 ( S3 Fig and Table 2 ) . This is because both Mam2 and Sxa2 are likely to depend on the same regions of P-factor activity . A recent study also suggests that coevolution of sterile 2 ( Ste2 ) ( a receptor for α-pheromone ) and barrier 1 ( Bar1 ) ( a peptidase of α-pheromone ) can occur , together with evolution of α-pheromone in Candida albicans , because Ste2 and Bar1 recognize the overlapping regions of α-pheromone [46] . In this study , although we did not obtain conclusive evidence that novel compatible combinations of the P-factor/Mam2 and P-factor/Sxa2 pairs can occur ( S2C and S2D Fig ) , such coevolution might proceed little by little , even though reproductive isolation would not be prevented during this time . This study has revealed an asymmetric diversification of the pheromone/receptor system in S . pombe: namely , recognition by M-factor is extremely stringent , whereas that by P-factor is relatively relaxed ( Fig 6 ) . In ascomycetes , one pheromone is a farnesylated peptide with hydrophobicity ( i . e . , M-factor ) , while the other is an unmodified peptide with hydrophilicity ( i . e . , P-factor ) [9] . This chemical asymmetry might be more beneficial for yeasts living in a liquid environment because a hydrophilic peptide is probably more diffusible and therefore might reach far-away cells , enabling rapid identification of mating partners . In fact , our findings lead us to propose two hypotheses . One is that the sexual behavior of individual species in nature is controlled by sexual interactions across species . The interaction between a pheromone and its receptor is essential for successful mating and appropriate mate choice . In the Saccharomyces clade , outbreeding is thought to be relatively rare [47 , 48]; therefore , this hypothesis might be reasonable for yeasts in which the haploid is thought to be the stable point of their life cycle . The other hypothesis is that the farnesyl group of pheromones is the key determinant for mating partner discrimination . Whereas M cells have a high basal production of M-factor , P-factor production is fully dependent on M-factor stimulation [16 , 49] . Perhaps , this difference is related to the conservation of M-factor . Overall , it seems that the asymmetric system in S . pombe might allow flexible adaptation of the simple peptide to mutational changes in the pheromone/receptor pair while maintaining stringent recognition for mating partners by the lipid peptide , perhaps as a first step toward prezygotic isolation .
The strains constructed in this study are listed in S1 Table . The 150 wild S . pombe strains with different origins from the standard laboratory strain ( Leupold’s strain , L968 [27] ) were obtained from the National BioResource Project ( deposited by J . Kohli , M . Sipiczki , G . Smith , H . Levin , A . Klar , and N . Rhind ) , H . Innan [28] , and J . Bähler [29] ( S2 Table ) . Cells were grown in yeast extract ( YE ) medium supplemented with adenine ( 75 mg/l ) , uracil ( 50 mg/l ) , and leucine ( 50 mg/l ) . For solid medium , 1 . 5% agar was added to yeast extract agar ( YEA ) . Where appropriate , antibiotics ( G418 , hygromycin B , and nourseothricin ) were added to YEA plates at a final concentration of 100 μg/ml . EMM2 was also used for growth [50] . Synthetic Dextrose ( SD ) medium was used to select S . pombe auxotrophic mutants . Malt extract agar ( MEA ) medium , EMM2−N medium , and PMG medium were used for mating and sporulation [50 , 51] . Cells were grown and conjugated for a few days at 30 °C . Genomic DNA was extracted from overnight cultures grown in YE medium . Each of the DNA fragments containing mfm1 , mfm2 , mfm3 , map2 , map3 , mam2 , and sxa2 were amplified using the following primer sets: oTS83/84 , oTS507/508 , oTS509/510 , oTS81/82 , oTS91/92 , oTS89/90 , and oTS624/625 , respectively ( all primers are listed in S5 Table ) . The PCR products were sequenced using the internally specific primers: oTS504 ( mfm1 ) , oTS505 ( mfm2 ) , oTS506 ( mfm3 ) , oTS85/86 ( map2 ) , oTS157/158 ( map3 ) , oTS151/152 ( mam2 ) , and oTS647/648/658 ( sxa2 ) . The sequences obtained were compared with the corresponding sequence of L968 . Differences from the L968 sequence are listed in S3 and S4 Tables . The map2+ gene ( approximately 2 . 6 kb ) containing its promoter and terminator regions was amplified from L968 genomic DNA using the primer set oTS81/82 . The DNA fragment was fused by using a Gibson Assembly ( New England Biolabs ) to a linearized vector derived from the integration vector pBS-ade6 [33] , which was prepared by inverse PCR with the primer set oTS79/80 . The resultant plasmid pTS13 ( all plasmids used in this study are listed in S6 Table ) was used as a starting template . To create a P2 unit , the correct two oligos ( oTS73 and oTS93; reverse complements of each other ) were mixed in Annealing Buffer ( 10 mM Tris-HCl , pH 7 . 4 , 1 mM EDTA , and 50 mM NaCl ) , incubated at 70 °C for 10 minutes by a GeneAmp PCR System 9700 , and then slowly cooled to 25 °C ( approximately 1 hour ) . The DNA fragment that was amplified from the resulting double-strand DNA fragment ( the P2 unit ) using the primer set oTS74/75 was fused to a linearized vector derived from pTS13 , which was prepared by inverse PCR with the primer set oTS71/72 to replace the map2+ gene . The resultant plasmid was referred to as pTS10 . To create Map2 ORFs with two to four P2 repeats , a further three P2 units carrying slightly different sequences at both sides ( see S7 Fig for detailed sequence information about the map2 genes constructed ) were amplified from pTS10 using the appropriate primers ( see S7 Table for all combinations of primers used for P-factor–related plasmids ) . Three mixed DNA fragments were fused to a linearized vector derived from pTS10 , which was prepared by inverse PCR with the primer set oTS72/179 . The resultant plasmids had various random repeats in the Map2 ORF . The number of repeats in each plasmid was checked by PCR using oTS85/86 , and plasmids with the desired number of repeats ( 2–4 repeats ) were sequenced to confirm their sequences . Thus , pTS47 , pTS48 , and pTS66 were constructed . The plasmids were cut near the center of the ade6+ gene with BamHI and integrated at the ade6 locus on Chromosome III in the FY23418 strain . To generate pTS54 , pTS55 , pTS145 , pTS146 , and pTS233 , inverse PCR was carried out using pTS10 and the primer sets oTS202/203 , oTS204/205 , oTS401/402 , oTS403/404 , and oTS204/404 , respectively . The amplified PCR products were subjected to DpnI treatment and 5′-phosphorylated by T4 polynucleotide kinase ( TaKaRa ) , ligated by T4 ligase ( TaKaRa ) , and transformed into Escherichia coli ( DH5α ) . The introduced mutations were confirmed by sequencing the recovered plasmids . All plasmids carrying 4-repeat Map2 ORFs were then constructed , as described above . To construct plasmids carrying two tandem map2 genes with 4 repeats , the map2 gene ( approximately 2 . 6 kb ) containing the promoter and terminator regions was amplified from the appropriate plasmid ( pTS59 , pTS48 , pTS60 , pTS147 , pTS148 , and pTS239 ) using the primer set oTS887/888 . Each DNA fragment was fused to a linealized vector derived from the corresponding plasmid , which was prepared by inverse PCR with the primer set oTS885/886 . Thus , pTS336–pTS341 were constructed . In addition , the map2 gene ( approximately 2 . 6 kb ) containing the promoter and terminator regions was amplified from the genomic DNA of nine wild strains ( 01CBS10391 , 04CBS2775 , 06CBS10460 , 13CBS10504 , 22CBS10468 , 24CBS5682 , 25CBS5680 , 32CBS352 , and 55FY28965 ) using the primer set oTS81/82 . Each DNA fragment was fused to a linearized vector derived from the integration vector pBS-ade6 , which was prepared by inverse PCR with the primer set oTS79/80 . All of the obtained plasmids were integrated into the FY23418 strain , as described above . Cells grown on YEA plates overnight were resuspended in sterilized water to a cell density of 1 × 108 cells/ml . A 30-μl aliquot of the resulting suspension was spotted onto sporulation media ( MEA for S . pombe; PMG for S . octosporus ) and then incubated for 2 days ( S . pombe ) or 3 days ( S . octosporus ) at 30 °C unless stated otherwise . The percentage of zygotes was calculated , as described previously [33 , 52] . In all cases , triplicate samples ( at least 300 cells each ) were counted , and the mean and standard deviation ( SD ) were calculated . Heterothallic haploid strains each carrying a chromosomal drug-resistance marker ( kanMX6 , hphMX6 , or natMX6 ) were cultured on YEA plates overnight , and the same cell numbers of the M-strain and two competing P-strains were mixed in sterilized water . A 30-μl aliquot of the resulting suspension was spotted onto MEA plate and incubated for exactly 24 hours at 30 °C . The mixed cells were allowed to mate , and the resulting hybrid diploids were sporulated to form spores . The cell suspension was diluted and spread on YEA plates containing different combinations of drugs . The number of colonies was counted after 3 days of incubation at 30 °C . The ratio of recombinant frequency was calculated , as described previously [11] . Three separate competitive tests were carried out . P-factor and M-factor peptides were chemically synthesized ( Eurofins ) for the shmooing assay . The purity of the preparations was over 95% ( HPLC ) . P-factor was dissolved in dimethyl sulfoxide ( DMSO ) at a concentration of 500 μM , and M-factor was dissolved in methanol ( MeOH ) at a concentration of 1 mM . The stock solutions were diluted with culture medium to the appropriate dilution ratio . For the assay , heterothallic haploid cells were grown in YE medium overnight , washed with sterilized water three times , and then resuspended in EMM2−N medium at a cell density of 4 × 107 cells/ml . The cells were treated with the synthetic pheromone and incubated for exactly 24 hours with gentle shaking . To assess whether Mam2 could recognize different P-factors , a P-factor sensitive strain lacking the sxa2+ gene ( TS402 ) was used . The cells were incubated with synthetic P-factors at different concentrations ( 0 , 10 , 100 , and 1 , 000 nM ) for 24 hours and observed by a DIC microscope . Images were recorded , and the L and W of a cell were measured to determine the L/W ratio . Cells with an L/W ratio of 2 . 0 or more were defined as shmooing cells; other cells were defined as arrested . In all cases , at least 100 cells each were measured . In the reciprocal experiment , an M-factor-sensitive strain lacking the rgs1+ gene [53] ( TS405 ) was used to assess whether Map3 can recognize different M-factors . The cells were treated with synthetic M-factors at different concentrations ( 0 , 100 , and 1 , 000 nM ) for 24 hours , and analyzed , as described above . An sxa2Δrgs1Δ double deletion strain ( TS578 ) was used to accurately measure the activity of the P5 peptide . The pREP vector was used to express sxa2+ under the control of the thiamine-repressible nmt1+ promoter in S . pombe [34] . First , a KanMX6 cassette amplified from pFA6a-kanMX6 [54] using the primer set oTS299/300 was fused to the linearized pREP1 vector prepared by inverse PCR with the primer set oTS301/302 . In the resultant plasmid pTS111 , the LEU2 gene was replaced with a KanMX6 cassette . Next , the sxa2+ gene ( approximately 1 . 5 kb ) was amplified from L968 genomic DNA using the primer set oTS729/730 and fused to a linearized vector derived from the integration plasmid pTS111 , which was prepared by inverse PCR with the primer set oTS305/306 . The resultant plasmid pTS284 was transformed into TS402 . Thus , a heterothallic strain ( TS407 ) ectopically overexpressing sxa2+ under the control of the nmt1+ promoter was obtained . The TS407 strain was precultured overnight in YE medium containing G418 and then washed with sterilized water three times . Cultures were inoculated into EMM2 medium containing G418 without thiamine ( to induce the nmt1+ promoter ) at a cell density of 1 × 107 cells/ml and incubated with aeration for 2 days . The cell culture supernatant was passed through a 0 . 22-μm filter ( Merck Millipore ) to completely remove cell debris and then concentrated 20-fold by ultrafiltration through a Vivaspin 20-10K ( GE healthcare ) because ultrafiltration has been shown to concentrate the carboxypeptidase without loss of Sxa2 activity [21] . Thus , culture medium including active Sxa2 was obtained . As a control , pTS111 ( sxa2− ) was transformed into TS402 , and the same preparation of culture medium was carried out . All reactions were performed at 30 °C in 50 mM citrate buffer , pH 5 . 5 , with 200 μM synthetic P-factor as described previously [55] . Culture medium containing 100 ng of protein , as determined by a Bradford protein assay ( BioRad ) , from strain TS406 or TS407 was added to a solution of P-factor , and then incubated for appropriate times ( 0 , 10 , and 60 mins ) with gentle shaking via the constant-temperature incubator shaker MBR-022UP ( TAITEC ) . Reactions were stopped by adding trifluoroacetic acid to 0 . 5% . The amount of leucine released from P-factor in the samples was measured by using a Branched Chain Amino Acid ( BCAA ) Assay kit ( Cosmo Bio co . ) in accordance with the manufacturer’s protocol and a Model 680 Microplate Reader ( BioRad ) . Carboxypeptidase assays were performed in at least triplicates , and the mean ± SD was calculated . As a negative control , it was verified that almost no leucine was detected in an assay with a P-factor peptide lacking the C-terminal Leu residue ( Table 2 ) . Plasmid pTS189 was integrated at the ade6 locus of an M-factorless strain ( FY23412 ) . The strain produced only So-M-factor and no Sp-M-factor . In contrast , the So-map2+ gene lacking both its signal sequence and two N-linked glycosylation sites was amplified from yFS286 genomic DNA using the primer set oTS57/58 . The DNA fragment was fused to a linearized vector derived from pTS13 , which was prepared by inverse PCR with the primer set oTS55/56 to replace the map2+ gene . The resultant plasmid pTS15 was integrated into the FY23418 strain , as described above . This strain produced only So-P-factors and no Sp-P-factors . Next , we constructed an S . octosporus strain producing only Sp-P-factors . To delete the native So-map2+ gene , the 3′-downstream sequence ( 1 kb ) was first amplified from yFS286 genomic DNA using the primer set oTS47/48 . The DNA fragment was fused to a linearized vector derived from pFA6a-kanMX6 , which was prepared by inverse PCR with the primer set oTS37/38 to construct the plasmid pTS5 . Next , the 5′-downstream sequence ( 1 kb ) was amplified from yFS286 genomic DNA using the primer set oTS46/78 . The DNA fragment was fused to a linearized vector derived from pTS5 , which was prepared by inverse PCR with the primer set oTS35/36 , as described above . Lastly , pTS11 was constructed . The DNA fragment from this plasmid , namely , a 3 . 5-kb fragment amplified from pTS11 using the primer set oTS48/78 , was purified , and 1 μg of the DNA was transformed into yFS286 by our previously described method [52] . The So-map2+ gene was successfully disrupted by a homologous recombination , and the resultant strain TS12 was sterile . The So-map2+ gene ( approximately 3 . 3 kb ) containing the promoter and terminator regions was amplified from yFS286 genomic DNA using the primer set oTS78/626 . The DNA fragment was fused to a linearized vector derived from the integration vector pFA6a-natMX6 , which was prepared by inverse PCR with the primer set oTS35/36 to construct the plasmid pTS247 . The Sp-map2+ gene lacking its signal sequence and two N-linked glycosylation sites was amplified from L968 genomic DNA using the primer set oTS635/636 . The DNA fragment was fused to a linearized vector derived from pTS247 , which was prepared by inverse PCR with the primer set oTS633/634 to replace the So-map2+ gene . The resultant plasmid pTS250 was integrated into the TS12 strain after restriction with BamHI near the center of the terminator region . This strain produced only Sp-P-factors and no So-P-factors . The mam2+ gene ( approximateley 3 . 1 kb ) containing its promoter and terminator regions was amplified from L968 genomic DNA using the primer set oTS90/99 . The DNA fragment was fused to a linearized vector derived from the integration vector pFA6a-hphMX6 , which was prepared by inverse PCR with the primer set oTS35/36 to construct the plasmid pTS14 . The mam2 gene ( approximately 1 . 0 kb ) was amplified from the genomic DNAs of four wild strains ( 02CBS2628 , 04CBS2775 , 06CBS10460 , and 26CBS5557 ) using the primer set oTS586/587 . Each of the DNA fragments was fused to a linearized vector derived from pTS14 , which was prepared by inverse PCR with the primer set oTS149/150 to replace the mam2+ gene . All obtained plasmids were integrated into the FY12677 strain after restriction with AfeI near the center of the terminator region . The sxa2+ gene ( approximately 3 . 5 kb ) containing its promoter and terminator regions was amplified from L968 genomic DNA using the primer set oTS624/625 . The DNA fragment was fused to a linearized vector derived from the integration vector pFA6a-natMX6 , which was prepared by inverse PCR with the primer set oTS35/36 to construct the plasmid pTS246 . The sxa2 gene ( approximately 1 . 5 kb ) was amplified from the genomic DNAs of three wild strains ( 01CBS10391 , 04CBS2775 , and 101FY29038 ) using the primer set oTS664/665 . Each of the DNA fragments was fused to a linearized vector derived from pTS246 , which was prepared by inverse PCR with the primer set oTS627/628 to replace the sxa2+ gene . All obtained plasmids were integrated into the TS356 strain after restriction with BamHI near the center of the terminator region .
|
The emergence of a new species might occur when two groups can no longer mate . Although such reproductive isolation is considered a key evolutionary process , the mechanisms by which it actually occurs have been confined to conjecture . The two sexes ( Plus [P] and Minus [M] ) of S . pombe each secrete a pheromone ( P-factor and M-factor ) , which binds to a corresponding receptor ( mating type auxiliary minus 2 [Mam2] and mating type auxiliary plus 3 [Map3] ) on cells of the opposite sex . The interaction between a pheromone and its receptor is essential for successful mating . Here , we explored conservation of the mating pheromone communication system among 150 wild S . pombe strains of different geographical origins and the closely related species S . octosporus . We found that 1 ) the M-factor/Map3 interaction was completely conserved , whereas the P-factor/Mam2 interaction was very diverse in the strains investigated , and 2 ) most of the P-factor variants were functional across species . Thus , we have revealed an asymmetric pheromone/receptor system in fungal mating: namely , whereas M-factor communication operates extremely stringently , P-factor communication has the flexibility to create variations , perhaps facilitating prezygotic isolation in S . pombe .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"plasmid",
"construction",
"germ",
"cells",
"zygotes",
"fungi",
"model",
"organisms",
"sex",
"pheromones",
"experimental",
"organism",
"systems",
"dna",
"construction",
"molecular",
"biology",
"techniques",
"dna",
"schizosaccharomyces",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"pheromone",
"receptors",
"animal",
"studies",
"animal",
"cells",
"bioinformatics",
"schizosaccharomyces",
"pombe",
"molecular",
"biology",
"yeast",
"biochemistry",
"signal",
"transduction",
"eukaryota",
"dna",
"sequence",
"analysis",
"cell",
"biology",
"ova",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"pheromones",
"cellular",
"types",
"organisms"
] |
2019
|
Asymmetric diversification of mating pheromones in fission yeast
|
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 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"rna",
"interference",
"vesicles",
"cloning",
"neuroscience",
"molecular",
"motors",
"nerve",
"fibers",
"epigenetics",
"molecular",
"biology",
"techniques",
"cellular",
"structures",
"and",
"organelles",
"neuronal",
"dendrites",
"motor",
"proteins",
"endosomes",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"genetic",
"interference",
"proteins",
"axons",
"gene",
"expression",
"molecular",
"biology",
"biochemistry",
"rna",
"sensory",
"neurons",
"cellular",
"neuroscience",
"cell",
"biology",
"nucleic",
"acids",
"neurons",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types"
] |
2018
|
Prd1 associates with the clathrin adaptor α-Adaptin and the kinesin-3 Imac/Unc-104 to govern dendrite pruning in Drosophila
|
Since Kaposi's sarcoma-associated herpesvirus ( KSHV or human herpesvirus 8 ) was first identified in Kaposi's sarcoma ( KS ) lesions of HIV-infected individuals with AIDS , the basic biological understanding of KSHV has progressed remarkably . However , the absence of a proper animal model for KSHV continues to impede direct in vivo studies of viral replication , persistence , and pathogenesis . In response to this need for an animal model of KSHV infection , we have explored whether common marmosets can be experimentally infected with human KSHV . Here , we report the successful zoonotic transmission of KSHV into common marmosets ( Callithrix jacchus , Cj ) , a New World primate . Marmosets infected with recombinant KSHV rapidly seroconverted and maintained a vigorous anti-KSHV antibody response . KSHV DNA and latent nuclear antigen ( LANA ) were readily detected in the peripheral blood mononuclear cells ( PBMCs ) and various tissues of infected marmosets . Remarkably , one orally infected marmoset developed a KS-like skin lesion with the characteristic infiltration of leukocytes by spindle cells positive for KSHV DNA and proteins . These results demonstrate that human KSHV infects common marmosets , establishes an efficient persistent infection , and occasionally leads to a KS-like skin lesion . This is the first animal model to significantly elaborate the important aspects of KSHV infection in humans and will aid in the future design of vaccines against KSHV and anti-viral therapies targeting KSHV coinfected tumor cells .
The most recently described human tumor virus , KSHV is a γ-2 herpesvirus and was first identified in association with KS , the most common neoplasm amongst AIDS patients [1] . KS is clinically separated into four different forms: classical KS , endemic KS , iatrogenic KS , and epidemic HIV-associated KS [2] . In addition to KS , KSHV is linked with two other cancers , Primary effusion lymphoma ( PEL ) [1] , [3] , [4] and Multicentric Castleman's disease [5] , [6] , [7] . Both of these cancers are B cell proliferative disorders , generally have poor outcomes , and have short median survival times complicated by their association with AIDS . One experimental barrier to working with KSHV has been the lack of an in vitro system for examining lytic replication . While KSHV can infect a wide variety of primary cells and cell lines , none support the growth of KSHV to a high titer [8] . Typically , viruses can be stimulated toward replication only through the addition of agents like phorbol esters [9] , this limitation extending to the in vivo setting . These problems had previously been addressed in two ways: through manipulation of the virus for increased titer or cell infectivity and the use of highly related viruses . By inserting a gene conferring resistance to an antibiotic , one can select cell populations that are essentially 100% infected [10] . Meanwhile , two examples of related viruses used as stand-ins for KSHV are Herpesvirus saimiri ( HVS ) [11] and Rhesus rhadinovirus ( RRV ) [12] , [13] . These viruses are largely co-linear with KSHV , carry many of the same genes , and are known to infect non-human primates [13] . RRV infection develops abnormal cellular proliferations characterized as extranodal lymphoma and retroperitoneal fibromatosis , a proliferative mesenchymal proliferative lesion , in an experimentally co-infected rhesus macaque with simian immunodeficiency virus , suggesting an excellent primate model to investigate KSHV-like pathogenesis [14] , [15] . In the case of HVS , infection of New World primates results in an aggressive , fulminant lymphoma . However , HVS primarily infects T cells , not B cells , as KSHV does . RRV persists upon infection in rhesus macaques , infects B cells , and induces B cell hyperplasia , but no KS-like disease occurs [15] . On the other hand , murine Herpesvirus 68 ( MHV-68 ) provides a small , experimentally accessible mouse model , but its infection does not associate with KS or related diseases [16] . The introduction of KSHV genes into these systems has proven to be useful , albeit limited , for the study of KSHV [17] . Besides these related virus models , in vitro experiments and transgenic animal models have been the main forces in elucidating the potential roles of individual KSHV proteins in cell culture and mouse models , respectively [18] , [19] , [20] , [21] . In a recent study , SCID-hu Thy/Liv mice reconstituted with the liver and thymus of human fetuses were utilized to study viral transcription as well as the susceptibility of the mice to infection with BCBL-1 derived KSHV [19] , [22] . In addition , Parsons et . al have shown that NOD/SCID mice infected with purified KSHV provide a system for demonstrating latent and lytic viral gene expression in addition to cell tropism [19] , [22] . Furthermore , they have investigated immune responses to KSHV via implanted NOD/SCID mice reconstituted with human fetal bone , thymus , and skin [19] , [22] . In spite of these significant improvements , none of these models truly reflect the in vivo setting . To understand the relative contributions of KSHV proteins to the cellular activation of KSHV-associated diseases and host-viral interactions for viral persistent infection , an animal model that provides a complete viral infection in addition to latent and lytic viral gene expression within the context of an intact host immunity still needs to be developed . In this report , we describe the efficient zoonotic transmission of KSHV into common marmosets ( Callithrix jacchus , Cj ) , a New World primate . Common marmosets intravenously inoculated with recombinant KSHV rapidly seroconverted and maintained high antibody responses for over one and a half years . In addition , KSHV DNA and LANA proteins were readily detectable in PBMCs and various tissues of the infected marmosets at a variety of time points . Furthermore , two common marmosets inoculated with rKSHV . 219 by the oral route seroconverted and were positive for viral DNA in their PBMCs . Remarkably , a common marmoset infected with rKSHV . 219 via the oral route developed a KS-like lesion with the characteristic spindle cells along with small blood vessels and extravasated erythrocytes . These results demonstrate that human KSHV effectively infects common marmosets , establishes persistence , and occasionally associates with the development of KS-like skin lesions . This is the first animal model of KSHV persistent infection to allow for analyses of the molecular mechanisms of the KSHV lifecycle directly in a non-human primate .
Given its magnitude as a human health problem , it is crucial to understand the molecular details of KSHV biology . However , the lack of an animal model of KSHV infection greatly hampers studies of KSHV pathogenesis and persistence . Therefore , we explored whether common marmosets can be experimentally infected with human KSHV , and if so , to what extent experimental infection recapitulates the important aspects of KSHV infection . To facilitate the infection of common marmosets with KSHV , we chose a recombinant KSHV , rKSHV . 219 , from KSHV-infected JSC-1 cells [23] . rKSHV . 219 expresses red fluorescent protein ( RFP ) from the KSHV lytic PAN promoter , green fluorescent protein ( GFP ) from the EF-1α promoter , and contains the gene for puromycin resistance as a selectable marker [10] . Two common marmosets ( Cj15-05 and Cj16-05 ) were inoculated intravenously with 5×106 infectious units ( IU ) of rKSHV . 219 . Blood samples were obtained at various time points to measure the marmosets' antibody responses and to detect viral DNA and proteins . Both monkeys quickly seroconverted to anti-KSHV-positive status within 20 days after inoculation ( Fig 1A ) and the animals' anti-KSHV antibodies persisted at very high levels for over 1 . 5 years . In addition , the sera from both infected monkeys readily reacted with purified KSHV virion proteins on immunoblots , with higher antibody reactivities detected over time ( Fig 1B ) . A positive control , immunoreactive serum from a KSHV-infected patient was included to validate this immunoblot assay ( Supplemental Fig S1 ) . PBMCs from KSHV-infected animals were PCR-positive for KSHV LANA and ORF9 10 and 20 days after infection , respectively ( Fig 1C and 1D ) . Viral DNA persisted in the PBMCs of monkey Cj15-05 for the entire 1 . 5- year span the animal was studied , whereas the level of viral DNA in monkey Cj16-05 decreased after 200 days postinfection ( Fig 1C ) . Real-time PCR analysis indicated that the KSHV DNA copy number/µg of genomic PBMC DNA from the infected monkeys were substantially lower than that of rKSHV . 219-infected Vero cells ( Fig 1D ) . The low viral DNA copy number suggests that a minor population of the monkey PBMCs carried the rKSHV . 219 genome . This was confirmed by confocal microscopy which showed that the KSHV LANA protein was detected in monkey PBMCs at a frequency of 2–5 cells per 1×106 cells ( Fig 1E ) . It should be noted that this figure depicts a rare positive field and does not reflect the overall incidence of LANA-positive cells . However , the infection frequency is similar to that of other γ-2 Herpesviruses , such as Rhesus lymphocryptovirus , Rhesus rhadinovirus , and Herpesvirus saimiri , which persist asymptomatically in their natural hosts [11] , [12] , [15] , [24] . Due to the lack of an efficient in vitro culture system for KSHV infection and replication , virus recovery from the PBMCs of the experimentally infected marmosets was unsuccessful . Nevertheless , these results unambiguously demonstrate the persistent infection of naïve common marmosets by rKSHV . 219 . Analysis of CD20+ B cells in the blood of the two infected marmosets showed increased B cell populations when compared to naïve marmosets ( Fig 2 ) . Although the total number of B cells did not change significantly in the initial few months after infection with rKSHV . 219 , their numbers noticeably increased around seven months postinfection and remained relatively elevated for as long as we followed these animals . Naïve common marmosets had 10–15% CD20+ B cells in their PBMCs while rKSHV . 219-infected marmosets Cj15-05 and Cj16-05 had 15–20% CD20+ B cells ( Fig 2 and Supplemental Fig S2 ) . Interestingly , a 7 fold increase in HLA-DR− CD20+ B cells at 200 days postinfection was observed in marmosets infected with rKSHV . 219 ( Fig 1F ) and this population was maintained until these animals were euthanized ( data not shown ) , indicating that rKSHV . 219 infection leads to elevated levels of CD20+ B cells in marmosets . However , no B cell hyperplasia was observed in either monkey ( data not shown ) . To detect early KSHV infection , a marmoset ( Cj325-04 ) was sacrificed 21 days after intravenous inoculation with 5×106 IU of rKSHV . 219 . An enzyme-linked immunosorbent assay ( ELISA ) showed a strong anti-KSHV antibody response ( Fig 3A ) while PCR products from a variety of tissues to test the presence of KSHV LANA DNA were yielded positive results only for samples from the jejunum and liver ( Fig 3B ) . PBMCs were collected at the time of sacrifice and cultured in vitro for one week . Approximately 1/104–5 lymphocytes were GFP-positive and RFP-negative , suggesting latent KSHV infection ( Fig 3C ) . In addition , the KSHV LANA latency-associated protein was readily detected in PBMCs of the rKSHV . 219-infected marmoset ( Fig 3D ) . Taken together , these results indicate that KSHV rapidly establishes latent infection in common marmoset PBMCs . It should be noted that the GFP-positive signal from PBMCs of infected marmosets disappeared after an additional week of incubation in vitro ( data not shown ) . Due to a low frequency of GFP-positive cells , however , it is unclear whether the GFP-positive cells died or lost the viral genome after a week of incubation in vitro . This suggests that while GFP is a convenient marker for viral infection , its use may be limited to the early phase of KSHV infection in marmosets . In transplant recipients , immune suppression is thought to disturb the host's surveillance of KSHV , leading to viral reactivation and an increased systemic viral load [2] . In contrast , rapamycin , an immunosuppressive drug , reduces KSHV-infected PEL cell growth in culture [25] and inhibits the progression of dermal KS in kidney transplant recipients while providing effective immunosuppression [26] . To investigate the direct impact in vivo of an immunosuppressive agent on persistent KSHV infection , two common marmosets ( Cj333-04 and Cj139-04 ) were treated with FK506 ( Tacrolimus or Fujimycin , 100 µg/kg/day ) immunosuppressive drug [27] for 14 days prior to intravenous inoculation with 5×106 IU of rKSHV . 219 , with treatment continuing for an additional 100 days . Immune-suppressed monkeys inoculated with rKSHV . 219 seroconverted to anti-KSHV-positive status within 14 days after inoculation ( Fig 4A ) . Although the anti-KSHV response and KSHV DNA remained present 100 days after infection , they were of a considerably lower magnitude than those of immune-competent monkeys . Compared to untreated animals Cj15-05 and Cj16-05 , FK506-treated monkeys Cj333-04 and Cj139-04 showed anti-KSHV antibody responses approximately 2–3 fold lower throughout at 40–80 days postinfection , a shorter duration of time when a positive KSHV LANA DNA signal could be obtained by PCR , and substantially lower copy numbers ( Fig 4B ) . To assess the tissue distribution of rKSHV . 219 in infected marmosets , KSHV LANA-specific DNA was amplified from various tissues and organs of immune-competent and immune-suppressed common marmosets . rKSHV . 219 DNA was readily detected in numerous tissues of infected monkeys Cj15-05 and Cj16-05 , including the tonsils , tongue , lymph nodes , spleen , jejunum , lungs , colon , liver , thymus , submandibular salivary gland , inguinal skin , and bone marrow ( Fig 5 ) . In contrast , only the submandibular lymph nodes and bone marrow were positive for rKSHV . 219 DNA in FK506-treated animals Cj333-04 and Cj139-04 ( Fig 5 ) . These results indicate that immunosuppressive drug treatment leads to a significant reduction of persistent KSHV infection in vivo . Two common marmosets were orally inoculated with 5×107 IU of rKSHV . 219 . Both monkeys inoculated with rKSHV . 219 quickly seroconverted to anti-KSHV-positive status within 20 days after inoculation ( Fig 6A ) . However , the anti-KSHV response was much lower in orally infected marmosets than in intravenously infected marmosets and persisted for a shorter period of time ( Fig 6A ) . PBMCs from KSHV-infected marmosets Cj10-05 and Cj11-05 collected on day 41 postinfection were positive for the KSHV LANA sequence but we could not detect KSHV LANA DNA in the PBMCs at any other time under the same conditions ( Fig 6B ) . Like the intravenously infected marmosets , the KSHV LANA protein was also detectable in the PBMCs of orally infected marmosets ( Fig 6C ) . However , since the efficiency of oral infection was much lower than that of intravenous infections , the detection frequency and time period of LANA positivity via confocal microscopy was much lower and more limited with oral infection compared to that in intravenous infection . These results indicate that KSHV infects common marmosets through the oral route but as seen with other viruses [24] , oral infection of KSHV is not as efficient as intravenous infection in common marmosets . Remarkably , one ( Cj10-05 ) of two animals orally infected with KSHV developed a skin lesion on its ventral abdomen at 41 days postinfection ( Fig 7A ) . This purple skin lesion was approximately 1 . 5 cm in diameter and had similar histopathological features to those observed in AIDS-associated KS lesions . Histological examination of a biopsy revealed a nonencapsulated dermal mass with characteristics typical of KS lesions . Pleomorphic spindle cells arranged in short bundles were observed , along with small blood vessels and extravasated erythrocytes ( Fig 7B and C ) . The spindle cells had a high mitotic index and had infiltrated into the surrounding soft tissues ( Fig 7B and C ) . Additionally , this KS-like lesion of Cj10-05 was PCR-positive for the KSHV LANA sequence ( Fig 7D ) . Immunohistochemistry with antibodies against LANA , vIL-6 , and K8 . 1 detected KSHV LANA , vIL-6 , and K8 . 1 , respectively , in the skin lesion ( Fig 7E and F and Supplemental Fig S3 ) . The anti-KSHV LANA , anti-vIL-6 , and anti-K8 . 1 reactivities were specific , and no staining was observed with these antibodies in control skin tissues from naïve common marmosets or in the biopsy tissue of Cj10-05 when the antibodies were replaced with isotype-matched irrelevant antibodies ( data not shown and Supplemental Fig S3 ) . Moreover , immunohistochemistry showed virtually identical phenotypes for the skin lesion on Cj10-05 and human KS lesions ( table in Fig 7 and Supplemental Fig S4 ) . These results collectively demonstrate that a common marmoset orally infected with KSHV can develop a skin lesion with similar histopathological features to those seen in human KS lesions and is also positive for KSHV DNA and proteins . However , the intensity and prevalence of LANA positive staining within the KS-like lesion of Cj10-05 were not as strong or widespread as those of human KS lesions . In addition , the expression of vIL-6 and K8 . 1 was restricted to a few cells in the KS-like lesion of Cj10-05 , the question of whether these cells were latently infected or lytically replicated requiring further study . Previous studies have illustrated the persistent infection of rhesus macaque monkeys infected with RRV , a primate homolog of KSHV [15] , [28] . Macaques inoculated with RRV alone display transient viremia followed by a vigorous anti-RRV response with no specific clinical features . In contrast , experimental RRV infection of SIV-infected rhesus macaques induces some of the hyperplastic B cell lymphoproliferative diseases which manifest themselves in AIDS patients coinfected with KSHV [15] . Furthermore , cotton-top tamarins ( Saguinus oedipus ) inoculated with human Epstein-Barr virus ( EBV ) develop diffuse malignant lymphomas resembling human reticulum cell or immunoblastic sarcomas [29] , [30] . Additionally , Rhesus lymphocryptovirus , which is very similar to EBV and naturally endemic in rhesus monkeys , can efficiently infect naïve animals orally , with the resulting infection closely mimicking key aspects of human EBV infection [31] . Our study demonstrates that experimental KSHV infection of the common marmoset is highly analogous to its infection of humans , including the means of infection , atypical lymphocytosis , sustained serological responses , latent infection of PBMCs , and virus persistence . However , it should be noted while one of two KSHV-infected marmosets developed KS-like lesion , the number of marmosets used for this experiment is too low to reach the specific conclusion of the frequency of KS-like lesion development induced by KSHV infection . Additional extensive experiments with different conditions such as various KSHV strains and titers and co-infection with HIV-1 or EBV may be able to increase the incidence of KS development . Nevertheless , this model thus provides a unique opportunity to dissect the molecular mechanisms of KSHV infection , persistence , and pathogenesis directly in primates . We have found that FK506 immunosuppressive drug treatment leads to a significant reduction of persistent KSHV infection in vivo . Zoeteweij et al demonstrated that cyclosporine and FK506 , specific inhibitors of calcineurin-dependent signal transduction , effectively block the in vitro KSHV reactivation induced by ionomycin and thapsigargin , activators of intracellular calcium mobilization [27] . This indicates that FK506 immunosuppressive drug treatment may directly block KSHV reactivation in vivo or suppress lymphocyte activation at an early stage of infection , indirectly affecting KSHV persistent infection . Additional experiments , e . g . the timing and type of immune suppression , are necessary to determine the role of host immune competence in the establishment of KSHV persistent infection . The examination of multiple primate species have demonstrated that γ-herpesviruses are nearly ubiquitous , with homologs of each virus found in a number of different species [32] . Infection of a naïve , natural host by these viruses usually results in a persistent infection that rarely progresses to a pathogenic event outside of specific clinical events . In contrast , cross-species transmission of these viruses can result in profound diseases . For example , herpesvirus saimiri ( HVS ) is a natural virus of squirrel monkeys , found in over 90% of animals in captivity with no observable pathogenesis [11] . However , transmission of HVS to common marmosets results in a fatal , lymphoproliferative disorder with 100% efficiency . In addition , the transmission of RRV into common marmoset was clear in regard to persistent infection , although it was less conclusive with regards to pathogenicity ( unpublished results ) . Furthermore , viruses could be re-isolated from this animal at multiple time points throughout the experiment ( unpublished results ) . Thus , it is intriguing that the common marmoset is highly susceptible to infection by various pathogens . We speculate that the limited major histocompatibility complex class I ( MHC I ) polymorphism of common marmosets may contribute to their susceptibility to KSHV infection and pathogenesis . Extensive polymorphism of the MHC is thought to confer immune protection on populations . Restriction fragment length polymorphism analysis showed that there were a limited number of common marmoset MHC class I alleles , whereas the MHC class II gene loci were polymorphic . This may play a role in the susceptibility of this New World primate species to a variety of pathogens . In summary , this is the first animal model that significantly recapitulates the important aspects of KSHV infection in humans and will greatly aid the future designing of anti-viral therapies and be of use in the development of vaccines against KSHV .
All common marmosets ( Callithrix jacchus , Cj ) were housed at the New England Primate Research Center ( NEPRC ) in accordance with the standards of the American Association for Accreditation of Laboratory Animal Care and Harvard Medical School's Internal Animal Care and Use Committee . Common marmosets experimentally inoculated with rKSHV . 219 were individually housed in bio-level 3 containment facilities . Vero cells carrying rKSHV . 219 were stimulated with trichostatin A ( TSA ) , the supernatants were harvested to purify rKSHV . 219 , and the virus titer was determined in 293A and Vero cells by performing a GFP-positive infection assay as described [10] . In order to produce the virus , Vero cells carrying rKSHV . 219 were stimulated with 75 nM of TSA for 24 hrs , the media changed , and grown for an additional 48 hrs in DMEM media without FBS . To harvest the virus , the cells were pelleted at 2000 rpm for 10 min with Sorvall SW40 rotor and the ensuing supernatant passed through a 0 . 45 µm filter , after which it was centrifuged at 18000 rpm for 3 hrs with Sorvall SA-600 rotor to concentrate rKSHV . 219 . 5×106 IU of this rKSHV . 219 was then used to intravenously inoculate the marmosets , for which a flexible , small-bore orogastric tube was utilized to slowly deliver the viral inoculum to the caudal aspect of the oral cavity , tonsils , and nostril mucosa . The inoculum ( 5×107 IU of rKSHV . 219 ) was dripped slowly over the oral mucosa while the animal was under light sedation , whereby the swallow reflex was maintained . The animals were given ketamine ( 10–20 mg/kg body weight ) intramuscularly prior to blood sampling , inoculation , and euthanasia . The animals underwent periodic blood sampling for viral isolation attempts and the detection of viral DNA . The animals were maintained until the termination of the study or when any of the conditions for euthanasia were met . rKSHV . 219 was purified from Vero . rKSHV . 219 cells and lysed with a 1% Triton X-100 buffer , after which it was put through five cycles of freezing in liquid nitrogen followed by thawing . The virion proteins were coated onto plates and used to detect reactive antibodies by ELISA as described [12] , [28] . Following experimental rKSHV . 219 inoculation , all animals were examined daily . Body temperature and clinical data were recorded via an implanted microchip and transponder ( Bio Medic Data Systems , Maywood , NJ ) . PBMCs were obtained prior to inoculation and at various time points throughout the study . Tissues were fixed in 10% neutral-buffered formalin and snap-frozen at −70°C . Blood was obtained at various time points to quantify the viral antibody response , viral load , and complete blood count . Formalin-fixed , paraffin-embedded , and snap-frozen tissues were used in immunohistochemical procedures to define the immunophenotype of cells within the skin lesion tissue as described [28] . Briefly , tissue sections were fixed in 2% paraformaldehyde and immunostained with an avidin-biotin-horseradish peroxidase complex technique with diaminobenzidine chromogen . The primary antibodies used in this study were anti-CD20 ( B1 ) , anti-CD8 ( DK25 ) , anti-CD3 ( Nu-Th/1 ) , anti-HLA-DR ( CR3/43 ) , anti-Ki67 ( MIB-1 ) , anti-vWF ( A00082 ) , anti-vimentin ( 3B4 ) , and anti-HAM56 ( M0632 ) . Primary antibodies for viral markers LANA ( clone 4A4 ) and vIL-6 were obtained from Advanced Biotechnologies Inc . ( Columbia , MD ) . Confocal microscopy was used to define the immunophenotypes of virus-infected cells among the PBMCs . To test for the presence of rKSHV . 219 , DNA was extracted from PBMCs or fresh frozen tissues using a QIAmp tissue kit ( Qiagen , Valencia , CA ) according to the manufacturer's instructions . DNA was eluted in 50 to 100 µl of sterile water treated with diethylpyrocarbonate and PCR was performed as described below . PBMCs were separated from the whole blood of infected common marmosets using standard Ficoll isolation techniques as described by the manufacturer ( Organon Teknika , Malvern , PA ) . Real-time PCR was performed with genomic DNA isolated from the PBMCs or tissues of the infected animals and specific primers based on previous analyses of the KSHV sequence [33] . LANA-specific primers [forward primer ( 5′-CCT CCA TCC CAT CCT GTG TC-3′ ) and backward primer ( 5′-GGA CGC ATA GGT GTT GAA GAG-3′ ) ] were used to generate a 146-bp product for LANA detection . ORF9-specific primers [forward primer ( 5′-ATT CAA GGT CAT ATA CGG CG-3′ ) and backward primer ( 5′-CTG GAC AAA ACG ACA GGC TG-3′ ) ] were used to generate a 262-bp product for ORF9 detection . Amplification was performed at 95°C for 25 s and 67 . 5°C for 60 s for 45 cycles in an iCycler thermal cycler system ( Bio-Rad , CA ) . Data was obtained at CT values as per the manufacturer's guidelines ( the cycle number at which logarithmic PCR plots cross a calculated threshold line ) . The PCR products were resolved on a 3% ethidium bromide-stained agarose gel and sequenced to confirm the identities of the KSHV LANA and ORF9 gene fragments . As described in the section “Antibody responses , ” virion particles were prepared by stimulating Vero . rKSHV . 219 cells with TSA , then by freezing and thawing rKSHV . 219 a total of five times in a 1% Triton X-100 buffer using liquid nitrogen . Purified virion proteins ( 20 µg ) were resolved by SDS-polyacrylamide gel electrophoresis ( PAGE ) and transferred onto a PVDF membrane ( Bio-Rad ) . Immunodetection was achieved with 1∶500 diluted monkey sera . The proteins were visualized by a chemiluminescence reagent ( Pierce ) and detected by a Fuji chemiluminometer . 5×105 cells per sample were washed with PBS medium containing 1% fetal calf serum and incubated with either fluorescein isothiocyanate-conjugated ( FITC ) or phycoerythrin-conjugated ( PE ) monoclonal antibodies for 30 min at 4°C . After washing , each sample was fixed with a 2% paraformaldehyde solution and flow cytometry analysis was performed with a FACS Scan ( Becton Dickinson Co . ) .
|
Kaposi's sarcoma-associated herpesvirus ( KSHV or human herpesvirus 8 ) , the most recently identified human tumor-inducing virus , has been linked to Kaposi's sarcoma , pleural effusion lymphomas and multicentric Castleman's disease . In fact , KSHV accounts for a large proportion of the cancer deaths in Africa . Further , the incidence of KSHV in the US and Europe has greatly increased due to the AIDS pandemic . Despite these pressing human health problems , studies of KSHV infection are greatly hampered by the lack of cell culture and animal models . To address this serious need , we set out to develop an animal model for KSHV infection . In this manuscript , we report the successful zoonotic transmission of KSHV into common marmosets ( Callithrix jacchus , Cj ) , a New World primate . Our study demonstrates that experimental KSHV infection of the common marmoset is highly analogous to its infection of humans , including the means of infection , sustained serological responses , latent infection of PBMCs , virus persistence , and KS-like skin lesion development , although the latter was infrequent in experimental KSHV infections . This model thus provides a unique opportunity to dissect the molecular mechanisms of KSHV infection , persistence , and pathogenesis directly in primates .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology/animal",
"models",
"of",
"infection",
"virology/host",
"antiviral",
"responses"
] |
2009
|
Non-Human Primate Model of Kaposi's Sarcoma-Associated Herpesvirus Infection
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.